@inproceedings{jaeschke2009testing, abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system 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. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.}, address = {New York, NY, USA}, author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd}, booktitle = {RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems}, interhash = {440fafda1eccf4036066f457eb6674a0}, intrahash = {1320904b208d53bd5d49e751cbfcc268}, location = {New York, NY, USA}, note = {(to appear)}, publisher = {ACM}, title = {Testing and Evaluating Tag Recommenders in a Live System}, year = 2009 } @inproceedings{melville2002contentboosted, abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.}, acmid = {777124}, address = {Menlo Park, CA, USA}, author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass}, booktitle = {Eighteenth National Conference on Artificial Intelligence}, interhash = {985028099c1a29f116ad7434005895ac}, intrahash = {a4917f0299f48e403966a8003ebd50be}, isbn = {0-262-51129-0}, location = {Edmonton, Alberta, Canada}, numpages = {6}, pages = {187--192}, publisher = {American Association for Artificial Intelligence}, title = {Content-boosted Collaborative Filtering for Improved Recommendations}, url = {http://dl.acm.org/citation.cfm?id=777092.777124}, year = 2002 } @book{balbymarinho2012recommender, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, doi = {10.1007/978-1-4614-1894-8}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8}, year = 2012 } @inproceedings{lipczak2010impact, abstract = {Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure -- folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to influence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor -- resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be influenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience.}, acmid = {1810648}, address = {New York, NY, USA}, author = {Lipczak, Marek and Milios, Evangelos}, booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, doi = {10.1145/1810617.1810648}, interhash = {a999b5f2eace0cd75028e57261afe3d7}, intrahash = {71dd1a473eaf0af9840758653746c221}, isbn = {978-1-4503-0041-4}, location = {Toronto, Ontario, Canada}, numpages = {10}, pages = {179--188}, publisher = {ACM}, series = {HT '10}, title = {The Impact of Resource Title on Tags in Collaborative Tagging Systems}, url = {http://doi.acm.org/10.1145/1810617.1810648}, year = 2010 } @phdthesis{jaschke2011formal, address = {Heidelberg}, author = {Jäschke, Robert}, interhash = {dcb2cd1cd72ae45d77c4d8755d199405}, intrahash = {bad02a0bbbf066907ecdee0ecaf9fb80}, isbn = {1-60750-707-2}, publisher = {Akad. Verl.-Ges. AKA}, series = {Dissertations in artificial intelligence}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038}, volume = 332, year = 2011 } @proceedings{jannach2014proceedings, bibsource = {dblp computer science bibliography, http://dblp.org}, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, interhash = {a1a704ec9c98e6031a1444c6eccc7c0a}, intrahash = {09cb7c63e60bd3c5e6773c9c871a8aba}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, url = {http://ceur-ws.org/Vol-1271}, volume = 1271, year = 2014 } @inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014}, doi = {10.1145/2645710.2645786}, interhash = {b465a3695da123d6ee9de1675cb3d480}, intrahash = {5773f799bec72240eda5e6cfb6a03d7b}, pages = 395, title = {The sixth {ACM} RecSys workshop on recommender systems and the social web}, url = {http://doi.acm.org/10.1145/2645710.2645786}, year = 2014 } @article{adomavicius2012impact, abstract = {This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.}, acmid = {2151166}, address = {New York, NY, USA}, articleno = {3}, author = {Adomavicius, Gediminas and Zhang, Jingjing}, doi = {10.1145/2151163.2151166}, interhash = {53e424cc9502ebb33d38de1d04230196}, intrahash = {e41453a56391ca382f2298607b361208}, issn = {2158-656X}, issue_date = {April 2012}, journal = {ACM Trans. Manage. Inf. Syst.}, month = apr, number = 1, numpages = {17}, pages = {3:1--3:17}, publisher = {ACM}, title = {Impact of Data Characteristics on Recommender Systems Performance}, url = {http://doi.acm.org/10.1145/2151163.2151166}, volume = 3, year = 2012 } @inproceedings{cremonesi2010performance, abstract = {In many commercial systems, the 'best bet' recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on accuracy metrics (such as precision/recall). An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task. Results show that improvements in RMSE often do not translate into accuracy improvements. In particular, a naive non-personalized algorithm can outperform some common recommendation approaches and almost match the accuracy of sophisticated algorithms. Another finding is that the very few top popular items can skew the top-N performance. The analysis points out that when evaluating a recommender algorithm on the top-N recommendation task, the test set should be chosen carefully in order to not bias accuracy metrics towards non-personalized solutions. Finally, we offer practitioners new variants of two collaborative filtering algorithms that, regardless of their RMSE, significantly outperform other recommender algorithms in pursuing the top-N recommendation task, with offering additional practical advantages. This comes at surprise given the simplicity of these two methods.}, acmid = {1864721}, address = {New York, NY, USA}, author = {Cremonesi, Paolo and Koren, Yehuda and Turrin, Roberto}, booktitle = {Proceedings of the Fourth ACM Conference on Recommender Systems}, doi = {10.1145/1864708.1864721}, interhash = {04cb3373b65b03e03225f447250e7873}, intrahash = {aeab7f02942cfeb97ccc7ae0a1d60801}, isbn = {978-1-60558-906-0}, location = {Barcelona, Spain}, numpages = {8}, pages = {39--46}, publisher = {ACM}, series = {RecSys '10}, title = {Performance of Recommender Algorithms on Top-n Recommendation Tasks}, url = {http://doi.acm.org/10.1145/1864708.1864721}, year = 2010 } @incollection{kubatz2011localrank, abstract = {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 }, author = {Kubatz, Marius and Gedikli, Fatih and Jannach, Dietmar}, booktitle = {E-Commerce and Web Technologies}, doi = {10.1007/978-3-642-23014-1_22}, editor = {Huemer, Christian and Setzer, Thomas}, interhash = {19a8194d47a5f6722a563a3689606440}, intrahash = {f62135043913269240b8e7105c418214}, isbn = {978-3-642-23013-4}, pages = {258-269}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Business Information Processing}, title = {LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations}, url = {http://dx.doi.org/10.1007/978-3-642-23014-1_22}, volume = 85, year = 2011 } @article{montas2011tagranker, abstract = {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.}, author = {Montañés, Elena and Ramón Quevedo, José and Díaz, Irene and Cortina, Raquel and Alonso, Pedro and Ranilla, José}, doi = {10.1093/jigpal/jzq036}, eprint = {http://jigpal.oxfordjournals.org/content/19/2/395.full.pdf+html}, interhash = {132fda1e475b28c81e5f78373fec36a9}, intrahash = {1fa5025795ddc2e7cb58433ae40c8c05}, journal = {Logic Journal of IGPL}, number = 2, pages = {395-404}, title = {TagRanker: learning to recommend ranked tags}, url = {http://jigpal.oxfordjournals.org/content/19/2/395.abstract}, volume = 19, year = 2011 } @inproceedings{schein2002methods, abstract = {We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.}, acmid = {564421}, address = {New York, NY, USA}, author = {Schein, Andrew I. and Popescul, Alexandrin and Ungar, Lyle H. and Pennock, David M.}, booktitle = {Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, doi = {10.1145/564376.564421}, interhash = {49cbb254d33a01f79dac175c41ec70d8}, intrahash = {eab2ae9f99bd5aed7ee66cd57b1cbc47}, isbn = {1-58113-561-0}, location = {Tampere, Finland}, numpages = {8}, pages = {253--260}, publisher = {ACM}, series = {SIGIR '02}, title = {Methods and Metrics for Cold-start Recommendations}, url = {http://doi.acm.org/10.1145/564376.564421}, year = 2002 } @article{atzmueller2014ubicon, abstract = {The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.}, author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd}, doi = {10.1080/13614568.2013.873488}, eprint = {http://www.tandfonline.com/doi/pdf/10.1080/13614568.2013.873488}, interhash = {6364e034fa868644b30618dc887c0270}, intrahash = {5d1ed63c337f8473d2b5b3b6c02a5f20}, journal = {New Review of Hypermedia and Multimedia}, number = 1, pages = {53-77}, title = {Ubicon and its applications for ubiquitous social computing}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488}, volume = 20, year = 2014 } @misc{kang2013lalda, abstract = {Social media users have finite attention which limits the number of incoming messages from friends they can process. Moreover, they pay more attention to opinions and recommendations of some friends more than others. In this paper, we propose LA-LDA, a latent topic model which incorporates limited, non-uniformly divided attention in the diffusion process by which opinions and information spread on the social network. We show that our proposed model is able to learn more accurate user models from users' social network and item adoption behavior than models which do not take limited attention into account. We analyze voting on news items on the social news aggregator Digg and show that our proposed model is better able to predict held out votes than alternative models. Our study demonstrates that psycho-socially motivated models have better ability to describe and predict observed behavior than models which only consider topics.}, author = {Kang, Jeon-Hyung and Lerman, Kristina and Getoor, Lise}, interhash = {18a900ae003a2aedb3879fcaaa4e89b6}, intrahash = {84ae222ddb615ca8ae9421a29c07a8f6}, note = {cite arxiv:1301.6277Comment: The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013)}, title = {LA-LDA: A Limited Attention Topic Model for Social Recommendation}, url = {http://arxiv.org/abs/1301.6277}, year = 2013 } @misc{mitzlaff2013recommending, abstract = {All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.}, author = {Mitzlaff, Folke and Stumme, Gerd}, interhash = {545658b6e337858f7865b51e46d1c7a6}, intrahash = {41f92650f0f7d78366febc1832cedba9}, note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013}, title = {Recommending Given Names}, url = {http://arxiv.org/abs/1302.4412}, year = 2013 } @proceedings{conf/recsys/2013rsweb, booktitle = {RSWeb@RecSys}, editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido}, ee = {http://ceur-ws.org/Vol-1066}, interhash = {31e724c09d1f4a4bbf013ecb8e1f6685}, intrahash = {aca768068f09003e97b51d48ec092ddc}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.}, url = {http://ceur-ws.org/Vol-1066}, volume = 1066, year = 2013 } @phdthesis{mcnee2006meeting, abstract = {In order to build relevant, useful, and effective recommender systems, researchers need to understand why users come to these systems and how users judge recommendation lists. Today, researchers use accuracy-based metrics for judging goodness. Yet these metrics cannot capture users' criteria for judging recommendation usefulness. We need to rethink recommenders from a user's perspective: they help users find new information. Thus, not only do we need to know about the user, we need to know what the user is looking for. In this dissertation, we explore how to tailor recommendation lists not just to a user, but to the user's current information seeking task. We argue that each recommender algorithm has specific strengths and weaknesses, different from other algorithms. Thus, different recommender algorithms are better suited for specific users and their information seeking tasks. A recommender system should, then, select and tune the appropriate recommender algorithm (or algorithms) for a given user/information seeking task combination. To support this, we present results in three areas. First, we apply recommender systems in the domain of peer-reviewed computer science research papers, a domain where users have external criteria for selecting items to consume. The effectiveness of our approach is validated through several sets of experiments. Second, we argue that current recommender systems research in not focused on user needs, but rather on algorithm design and performance. To bring users back into focus, we reflect on how users perceive recommenders and the recommendation process, and present Human-Recommender Interaction theory, a framework and language for describing recommenders and the recommendation lists they generate. Third, we look to different ways of evaluating recommender systems algorithms. To this end, we propose a new set of recommender metrics, run experiments on several recommender algorithms using these metrics, and categorize the differences we discovered. Through Human-Recommender Interaction and these new metrics, we can bridge users and their needs with recommender algorithms to generate more useful recommendation lists.}, address = {Minneapolis, MN, USA}, advisor = {Konstan, Joseph A.}, author = {Mcnee, Sean Michael}, interhash = {aa770067601fafb29655af4e21e47422}, intrahash = {4e3e619f4cdda96257b37eac6bb38899}, isbn = {978-0-542-83429-5}, school = {University of Minnesota}, title = {Meeting User Information Needs in Recommender Systems}, year = 2006 } @inproceedings{mueller2013recommendations, abstract = {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.}, address = {Aachen, Germany}, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, interhash = {23d1cf49208d9a0c8b883dc69d4e444d}, intrahash = {2bab3f013052bc741e795c5c61aea5c9}, issn = {1613-0073}, publisher = {CEUR-WS}, title = {Tag Recommendations for SensorFolkSonomies}, url = {http://ceur-ws.org/Vol-1066/}, volume = 1066, year = 2013 } @article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, interhash = {e8095b13630452ce3ecbae582f32f4bc}, intrahash = {e585a92994be476480545eb62d741642}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, url = {http://arxiv.org/abs/1310.1498}, volume = {1310.1498}, year = 2013 } @misc{mitzlaff2013recommending, abstract = {All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.}, author = {Mitzlaff, Folke and Stumme, Gerd}, interhash = {545658b6e337858f7865b51e46d1c7a6}, intrahash = {41f92650f0f7d78366febc1832cedba9}, note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013}, title = {Recommending Given Names}, url = {http://arxiv.org/abs/1302.4412}, year = 2013 }