Sarwar, B.; Karypis, G.; Konstan, J. & Riedl, J. (2001),
Item-based collaborative filtering recommendation algorithms, in
'WWW '01: Proceedings of the 10th International Conference on World Wide Web'
, ACM, New York, NY, USA
, pp. 285--295
.
[BibTeX]
[Endnote]
Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different item-based recommendation generation algorithms. We look into different techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and different techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available user-based algorithms.
Illig, J. (2006),
'Entwurf und Integration eines Item-Based Collaborative Filtering
g Recommender Systems in das BibSonomy-Projekt'
, Technical report, Fachgebiet Wissensverarbeitung, Universität Kassel
, Project report
.
[BibTeX]
[Endnote]
Bollen, D. & Halpin, H. (2009),
The Role of Tag Suggestions in Folksonomies, in
'HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia'
, ACM, New York, NY, USA
.
[BibTeX]
[Endnote]
Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users.
Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008),
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems, in
'Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)'
, pp. 39--43
.
[BibTeX]
[Endnote]
Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.
man Au Yeung, C.; Gibbins, N. & Shadbolt, N. (2007),
Understanding the Semantics of Ambiguous Tags in Folksonomies, in
Liming Chen; Philippe Cudré-Mauroux; Peter Haase; Andreas Hotho & Ernie Ong, ed.,
'ESOE'
, CEUR-WS.org,
, pp. 108-121
.
[BibTeX]
[Endnote]
Cao, H.; Xie, M.; Xue, L.; Liu, C.; Teng, F. & Huang, Y. (2009),
Social Tag Prediction Base on Supervised Ranking Model, in
Folke Eisterlehner; Andreas Hotho & Robert Jäschke, ed.,
, pp. 35--48
.
[BibTeX]
[Endnote]
Si, X.; Liu, Z.; Li, P.; Jiang, Q. & Sun, M. (2009),
Content-based and Graph-based Tag Suggestion, in
Folke Eisterlehner; Andreas Hotho & Robert Jäschke, ed.,
, pp. 243--260
.
[BibTeX]
[Endnote]
Lipczak, M.; Hu, Y.; Kollet, Y. & Milios, E. (2009),
Tag Sources for Recommendation in Collaborative Tagging Systems, in
Folke Eisterlehner; Andreas Hotho & Robert Jäschke, ed.,
'ECML PKDD Discovery Challenge 2009 (DC09)'
, pp. 157--172
.
[BibTeX]
[Endnote]
Sinclair, J. & Cardew-Hall, M. (2008),
'The folksonomy tag cloud: when is it useful?', Journal of Information Science
34
(1)
, 15-29
.
[BibTeX]
[Endnote]
The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset.
Wetzker, R.; Zimmermann, C.; Bauckhage, C. & Albayrak, S. (2010),
I tag, you tag: translating tags for advanced user models., in
Brian D. Davison; Torsten Suel; Nick Craswell & Bing Liu, ed.,
'WSDM'
, ACM,
, pp. 71-80
.
[BibTeX]
[Endnote]
Dittmann, C.; Dittmann, M.; Peters, I. & Weller, K. (2009),
Persönliches Tag Gardening mit tagCare., in M. Ockenfeld, ed.,
'Generation international - die Zukunft von Information, Wissenschaft und Profession. Proceedings der 31. Online-Tagung der Germany.'
, DGI Frankfurt a. M.,
, pp. 117-128
.
[BibTeX]
[Endnote]
Markines, B.; Cattuto, C.; Menczer, F.; Benz, D.; Hotho, A. & Stumme, G. (2009),
Evaluating Similarity Measures for Emergent Semantics of Social Tagging, in
'18th International World Wide Web Conference'
, pp. 641--650
.
[BibTeX]
[Endnote]
Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.
Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008),
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, in
Amit P. Sheth; Steffen Staab; Mike Dean; Massimo Paolucci; Diana Maynard; Timothy W. Finin & Krishnaprasad Thirunarayan, ed.,
'The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008'
, Springer, Heidelberg
, pp. 615--631
.
[BibTeX]
[Endnote]
Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks
like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
Symeonidis, P.; Nanopoulos, A. & Manolopoulos, Y. (2008),
Tag recommendations based on tensor dimensionality reduction, in
'RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems'
, ACM, New York, NY, USA
, pp. 43--50
.
[BibTeX]
[Endnote]
Rendle, S.; Balby Marinho, L.; Nanopoulos, A. & Schmidt-Thieme, L. (2009),
Learning optimal ranking with tensor factorization for tag recommendation, in
'KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining'
, ACM, New York, NY, USA
, pp. 727--736
.
[BibTeX]
[Endnote]
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.
Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2008),
'Tag Recommendations in Social Bookmarking Systems', AI Communications
21
(4)
, 231-247
.
[BibTeX]
[Endnote]
Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2008),
'Tag Recommendations in Social Bookmarking Systems', AI Communications
21
(4)
, 231-247
.
[BibTeX]
[Endnote]
Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2008),
'Tag Recommendations in Social Bookmarking Systems', AI Communications
21
(4)
, 231--247
.
[BibTeX]
[Endnote]
Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
Jäschke, R.
(2011),
Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems
, Vol. 332
, Akademische Verlagsgesellschaft AKA
, Heidelberg, Germany
.
[BibTeX]
[Endnote]
One of the most noticeable innovation that emerged with the advent of the Web 2.0 and the focal point of this thesis are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords, so called tags. The tags are used for navigation, finding resources, and serendipitous browsing and thus provide an immediate benefit for the user. By now, several systems for tagging photos, web links, publication references, videos, etc. have attracted millions of users which in turn annotated countless resources. Tagging gained so much popularity that it spread into other applications like web browsers, software packet managers, and even file systems. Therefore, the relevance of the methods presented in this thesis goes beyond the Web 2.0. The conceptual structure underlying collaborative tagging systems is called folksonomy. It can be represented as a tripartite hypergraph with user, tag, and resource nodes. Each edge of the graph expresses the fact that a user annotated a resource with a tag. This social network constitutes a lightweight conceptual structure that is not formalized, but rather implicit and thus needs to be extracted with knowledge discovery methods. In this thesis a new data mining task – the mining of all frequent tri-concepts – is presented, together with an efficient algorithm for discovering such implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. Extending the theory of triadic Formal Concept Analysis, we provide a formal definition of the problem, and present an efficient algorithm for its solution. We show the applicability of our approach on three large real-world examples and thereby perform a conceptual clustering of two collaborative tagging systems. Finally, we introduce neighborhoods of triadic concepts as basis for a lightweight visualization of tri-lattices. The social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind, has been developed by our research group. Besides being a useful tool for many scientists, it provides interested researchers a basis for the evaluation and integration of their knowledge discovery methods. This thesis introduces BibSonomy as an exemplary collaborative tagging system and gives an overview of its architecture and some of its features. Furthermore, BibSonomy is used as foundation for evaluating and integrating some of the discussed approaches. Collaborative tagging systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In this thesis we evaluate and compare several recommendation algorithms on large-scale real-world datasets: an adaptation of user-based Collaborative Filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag co-occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag co-occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We demonstrate how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. Furthermore, we show how to integrate recommendation methods into a real tagging system, record and evaluate their performance by describing the tag recommendation framework we developed for BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. We also present an evaluation of the framework which demonstrates its power. The folksonomy graph shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Clicklogs of web search engines can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large folksonomy snapshot and on query logs of two large search engines. We find that all of the three datasets exhibit similar structural properties and thus conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of collaborative tagging users is driven by similar dynamics. In this thesis we further transfer the folksonomy paradigm to the Social Semantic Desktop – a new model of computer desktop that uses Semantic Web technologies to better link information items. There we apply community support methods to the folksonomy found in the network of social semantic desktops. Thus, we connect knowledge discovery for folksonomies with semantic technologies. Alltogether, the research in this thesis is centered around collaborative tagging systems and their underlying datastructure – folksonomies – and thereby paves the way for the further dissemination of this successful knowledge management paradigm.
Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008),
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, in
Amit P. Sheth; Steffen Staab; Mike Dean; Massimo Paolucci; Diana Maynard; Timothy W. Finin & Krishnaprasad Thirunarayan, ed.,
'The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008'
, Springer, Heidelberg
, pp. 615--631
.
[BibTeX]
[Endnote]
Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (to appear),
A Comparison of content-based Tag Recommendations in Folksonomy Systems, in
'Postproceedings of the International Conference on Knowledge Processing in Practice (KPP2007)'
, Springer,
.
[BibTeX]
[Endnote]
Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008),
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, in
'The Semantic Web - ISWC 2008'
, Springer Berlin / Heidelberg,
, pp. 615--631
.
[BibTeX]
[Endnote]
Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
Krestel, R. & Chen, L. (2008),
The Art of Tagging: Measuring the Quality of Tags, in
'Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web'
, Springer-Verlag, Berlin, Heidelberg
, pp. 257--271
.
[BibTeX]
[Endnote]
<p>Collaborative tagging, supported by many social networking websites, is currently enjoying an increasing popularity. The usefulness of this largely available tag data has been explored in many applications including web resources categorization,deriving emergent semantics, web search etc. However, since tags are supplied by users <em>freely</em> , not all of them are useful and reliable, especially when they are generated by spammers with malicious intent. Therefore, identifying tags of high quality is crucial in improving the performance of applications based on tags. In this paper, we propose TRP-Rank (Tag-Resource Pair Rank), an algorithm to measure the quality of tags by manually assessing a seed set and <em>propagating the quality</em> through a graph. The three dimensional relationship among users, tags and web resources is firstly represented by a graph structure. A set of seed nodes, where each node represents a tag annotating a resource, is then selected and their quality is assessed. The quality of the remaining nodes is calculated by propagating the known quality of the seeds through the graph structure. We evaluate our approach on a public data set where tags generated by suspicious spammers were manually labelled. The experimental results demonstrate the effectiveness of this approach in measuring the quality of tags.</p>
Guan, Z.; Bu, J.; Mei, Q.; Chen, C. & Wang, C. (2009),
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects., in
James Allan; Javed A. Aslam; Mark Sanderson; ChengXiang Zhai & Justin Zobel, ed.,
'SIGIR'
, ACM,
, pp. 540-547
.
[BibTeX]
[Endnote]
Rae, A.; Sigurbjörnsson, B. & van Zwol, R. (2010),
Improving tag recommendation using social networks, in
'Adaptivity, Personalization and Fusion of Heterogeneous Information'
, Le Centre De Hautes Etudes Internationales d'Informatique Documentaire, Paris, France
, pp. 92--99
.
[BibTeX]
[Endnote]
In this paper we address the task of recommending additional tags to partially annotated media objects, in our case images. We propose an extendable framework that can recommend tags using a combination of different personalised and collective contexts. We combine information from four contexts: (1) all the photos in the system, (2) a user's own photos, (3) the photos of a user's social contacts, and (4) the photos posted in the groups of which a user is a member. Variants of methods (1) and (2) have been proposed in previous work, but the use of (3) and (4) is novel.</p> <p>For each of the contexts we use the same probabilistic model and Borda Count based aggregation approach to generate recommendations from different contexts into a unified ranking of recommended tags. We evaluate our system using a large set of real-world data from Flickr. We show that by using personalised contexts we can significantly improve tag recommendation compared to using collective knowledge alone. We also analyse our experimental results to explore the capabilities of our system with respect to a user's social behaviour.
Widdows, D. & Dorow, B. (2002),
A Graph Model for Unsupervised Lexical Acquisition, in
'COLING'
.
[BibTeX]
[Endnote]
Sen, S.; Vig, J. & Riedl, J. (2009),
Tagommenders: connecting users to items through tags., in
Juan Quemada; Gonzalo León; Yoëlle S. Maarek & Wolfgang Nejdl, ed.,
'WWW'
, ACM,
, pp. 671-680
.
[BibTeX]
[Endnote]
Radelaar, J.; Boor, A.-J.; Vandic, D.; van Dam, J.-W.; Hogenboom, F. & Frasincar, F. (2011),
Improving the Exploration of Tag Spaces Using Automated Tag Clustering, in Sören Auer; Oscar Díaz & George Papadopoulos, ed.,
'Web Engineering'
, Springer Berlin / Heidelberg,
, pp. 274-288
.
[BibTeX]
[Endnote]
Suchanek, F. M.; Vojnovic, M. & Gunawardena, D. (2008),
Social tags: meaning and suggestions, in
'Proceeding of the 17th ACM conference on Information and knowledge management'
, ACM, New York, NY, USA
, pp. 223--232
.
[BibTeX]
[Endnote]
This paper aims to quantify two common assumptions about social tagging: (1) that tags are "meaningful" and (2) that the tagging process is influenced by tag suggestions. For (1), we analyze the semantic properties of tags and the relationship between the tags and the content of the tagged page. Our analysis is based on a corpus of search keywords, contents, titles, and tags applied to several thousand popular Web pages. Among other results, we find that the more popular tags of a page tend to be the more meaningful ones. For (2), we develop a model of how the influence of tag suggestions can be measured. From a user study with over 4,000 participants, we conclude that roughly one third of the tag applications may be induced by the suggestions. Our results would be of interest for designers of social tagging systems and are a step towards understanding how to best leverage social tags for applications such as search and information extraction.
Kim, H. L.; Scerri, S.; Breslin, J. G.; Decker, S. & Kim, H. G. (2008),
The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies, in
'Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications'
, Dublin Core Metadata Initiative, Berlin, Deutschland
, pp. 128--137
.
[BibTeX]
[Endnote]
Jäschke, R. (2011),
'Formal concept analysis and tag recommendations in collaborative tagging systems'.
[BibTeX]
[Endnote]
Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (2011),
A Comparison of content-based Tag Recommendations in Folksonomy Systems, in
'Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007)'
, Springer,
.
[BibTeX]
[Endnote]
Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2008),
'Tag Recommendations in Social Bookmarking Systems', AI Communications
21
(4)
, 231-247
.
[BibTeX]
[Endnote]
Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008),
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, in
Amit P. Sheth; Steffen Staab; Mike Dean; Massimo Paolucci; Diana Maynard; Timothy W. Finin & Krishnaprasad Thirunarayan, ed.,
'The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008'
, Springer, Heidelberg
, pp. 615--631
.
[BibTeX]
[Endnote]
Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (2011),
A Comparison of Content-Based Tag Recommendations in Folksonomy Systems, in
Karl Erich Wolff; Dmitry E. Palchunov; Nikolay G. Zagoruiko & Urs Andelfinger, ed.,
'Knowledge Processing and Data Analysis'
, Springer, Berlin/Heidelberg
, pp. 136--149
.
[BibTeX]
[Endnote]
Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (2011),
A Comparison of Content-Based Tag Recommendations in Folksonomy Systems, in
Karl Erich Wolff; Dmitry E. Palchunov; Nikolay G. Zagoruiko & Urs Andelfinger, ed.,
'Knowledge Processing and Data Analysis'
, Springer, Berlin/Heidelberg
, pp. 136--149
.
[BibTeX]
[Endnote]
Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
Zhang, Z.-K.; Zhou, T. & Zhang, Y.-C. (2011),
'Tag-Aware Recommender Systems: A State-of-the-Art Survey', Journal of Computer Science and Technology
26
(5)
, 767--777
.
[BibTeX]
[Endnote]
In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.
Musto, C.; Narducci, F.; Lops, P. & de Gemmis, M. (2010),
Combining Collaborative and Content-Based Techniques for Tag Recommendation., in
Francesco Buccafurri & Giovanni Semeraro, ed.,
'E-Commerce and Web Technologies'
, Springer, Berlin/Heidelberg
, pp. 13--23
.
[BibTeX]
[Endnote]
The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages. Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender.
Rendle, S. & Schmidt-Thieme, L. (2010),
Pairwise interaction tensor factorization for personalized tag recommendation, in
'Proceedings of the third ACM international conference on Web search and data mining'
, ACM, New York, NY, USA
, pp. 81--90
.
[BibTeX]
[Endnote]
Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.
Rendle, S. & Schmidt-Thieme, L. (2010),
Pairwise interaction tensor factorization for personalized tag recommendation, in
'Proceedings of the third ACM international conference on Web search and data mining'
, ACM, New York, NY, USA
, pp. 81--90
.
[BibTeX]
[Endnote]
Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.
Sinclair, J. & Cardew-Hall, M. (2008),
'The folksonomy tag cloud: when is it useful?', Journal of Information Science
34
(1)
, 15-29
.
[BibTeX]
[Endnote]
The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset.
Gemmell, J.; Schimoler, T.; Mobasher, B. & Burke, R. (2010),
Hybrid tag recommendation for social annotation systems, in
'Proceedings of the 19th ACM international conference on Information and knowledge management'
, ACM, New York, NY, USA
, pp. 829--838
.
[BibTeX]
[Endnote]
Social annotation systems allow users to annotate resources with personalized tags and to navigate large and complex information spaces without the need to rely on predefined hierarchies. These systems help users organize and share their own resources, as well as discover new ones annotated by other users. Tag recommenders in such systems assist users in finding appropriate tags for resources and help consolidate annotations across all users and resources. But the size and complexity of the data, as well as the inherent noise and inconsistencies in the underlying tag vocabularies, have made the design of effective tag recommenders a challenge. Recent efforts have demonstrated the advantages of integrative models that leverage all three dimensions of a social annotation system: users, resources and tags. Among these approaches are recommendation models based on matrix factorization. But, these models tend to lack scalability and often hide the underlying characteristics, or "information channels" of the data that affect recommendation effectiveness. In this paper we propose a weighted hybrid tag recommender that blends multiple recommendation components drawing separately on complementary dimensions, and evaluate it on six large real-world datasets. In addition, we attempt to quantify the strength of the information channels in these datasets and use these results to explain the performance of the hybrid. We find our approach is not only competitive with the state-of-the-art techniques in terms of accuracy, but also has the added benefits of being scalable to large real world applications, extensible to incorporate a wide range of recommendation techniques, easily updateable, and more scrutable than other leading methods.
Doerfel, S. & Jäschke, R. (2013),
An Analysis of Tag-Recommender Evaluation Procedures, in
'Proceedings of the 7th ACM conference on Recommender systems'
, ACM, New York, NY, USA
, pp. 343--346
.
[BibTeX]
[Endnote]
Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.
Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G. (2013),
Tag Recommendations for SensorFolkSonomies, in
'Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings'
, ACM,
, pp. New York, NY, USA
.
[BibTeX]
[Endnote]
With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.
Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G. (2013),
Tag Recommendations for SensorFolkSonomies, in
'Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings'
, ACM,
, pp. New York, NY, USA
.
[BibTeX]
[Endnote]
With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.
Doerfel, S. & Jäschke, R. (2013),
An Analysis of Tag-Recommender Evaluation Procedures, in
'Proceedings of the 7th ACM conference on Recommender systems'
, ACM, New York, NY, USA
, pp. 343--346
.
[BibTeX]
[Endnote]
Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.
Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G. (2013),
Tag Recommendations for SensorFolkSonomies, in
'Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings'
, CEUR-WS, Aachen, Germany
.
[BibTeX]
[Endnote]
With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.
Montañés, E.; Ramón Quevedo, J.; Díaz, I.; Cortina, R.; Alonso, P. & Ranilla, J. (2011),
'TagRanker: learning to recommend ranked tags', Logic Journal of IGPL
19
(2)
, 395-404
.
[BibTeX]
[Endnote]
In a social network, recommenders are highly demanded since they provide user interests in order to construct user profiles. This user profiles might be valuable to be exploited in business management or marketing, for instance. Basically, a tag recommender provides to users a set keywords that describe certain resources. The existing approaches require exploiting content information or they just provide a set of tags without any kind of preference order. This article proposes TagRanker, a tag recommender based on logistic regression that is free of exploiting content information. In addition, it gives a ranking of certain tags and learns just from the relations among users, resources and tags previously posted avoiding the cost of exploiting the content of the resources. An adequate evaluation measure for this specific kind of ranking is also proposed, since the existing ones just consider the tags as coming from a classification. The experiments on several data sets show that TagRanker can effectively recommend relevant tags outperforming the performance of a benchmark of Tag Recommender Systems.
Kubatz, M.; Gedikli, F. & Jannach, D. (2011),
LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations, in Christian Huemer & Thomas Setzer, ed.,
'E-Commerce and Web Technologies'
, Springer Berlin Heidelberg,
, pp. 258-269
.
[BibTeX]
[Endnote]
On many modern Web platforms users can annotate the available online resources with freely-chosen tags. This Social Tagging data can then be used for information organization or retrieval purposes. Tag recommenders in that context are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags. Many of these algorithms however suffer from scalability and performance problems, including the popular
Lorince, J.; Zorowitz, S.; Murdock, J. & Todd, P. (2014),
“Supertagger” Behavior in Building Folksonomies, in
.
[BibTeX]
[Endnote]