TY - CONF AU - Blohm, I. AU - Ott, F. AU - Bretschneider, U. AU - Huber, M. AU - Rieger, M. AU - Glatz, F. AU - Koch, M. AU - Leimeister, J. M. AU - Krcmar, H. A2 - T1 - Extending Open Innovation Platforms into the real world -

Using Large Displays in Public Spaces T2 - 10. European Academy of Management Conference (EURAM) 2010 PB - C1 - Rome, Italy PY - 2010/ CY - VL - IS - 10 SP - EP - UR - http://www.uni-kassel.de/fb7/ibwl/leimeister/pub/JML_197.pdf DO - KW - IdeaMirror KW - collaborative KW - community KW - computing KW - evaluation KW - filtering KW - idea KW - innovation KW - itegpub KW - myown KW - open KW - pub_jml KW - pub_ubr KW - support KW - ubiquitous L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Blohm, I. AU - Ott, F. AU - Bretschneider, U. AU - Huber, M. AU - Rieger, M. AU - Glatz, F. AU - Koch, M. AU - Leimeister, J. M. AU - Krcmar, H. A2 - T1 - Extending Open Innovation Platforms into the real world - Using Large Displays in Public Spaces T2 - 10. European Academy of Management Conference (EURAM) 2010 PB - C1 - Rome, Italy PY - 2010/ CY - VL - IS - 10 SP - EP - UR - http://pubs.wi-kassel.de/wp-content/uploads/2013/03/JML_248.pdf DO - KW - IdeaMirror KW - collaborative KW - community KW - computing KW - dempub KW - evaluation KW - filtering KW - idea KW - innovation KW - itegpub KW - open KW - pub_jml KW - pub_ubr KW - support KW - ubiquitous L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Gemmell, Jonathan AU - Schimoler, Thomas R. AU - Christiansen, Laura AU - Mobasher, Bamshad A2 - Jannach, Dietmar A2 - Geyer, Werner A2 - Freyne, Jill A2 - Anand, Sarabjot Singh A2 - Dugan, Casey A2 - Mobasher, Bamshad A2 - Kobsa, Alfred T1 - Improving Folkrank With Item-Based Collaborative Filtering T2 - ACM RecSys'09 Workshop on Recommender Systems and the Social Web PB - C1 - PY - 2009/october CY - VL - 532 IS - SP - 17 EP - 24 UR - http://ceur-ws.org/Vol-532/paper3.pdf DO - KW - bookmarking KW - collaborative KW - filtering KW - folkrank KW - recommender KW - social KW - tagging L1 - SN - N1 - N1 - AB - Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications. ER - TY - CONF AU - Koren, Yehuda A2 - T1 - Collaborative filtering with temporal dynamics T2 - Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining PB - ACM C1 - New York, NY, USA PY - 2009/ CY - VL - IS - SP - 447 EP - 456 UR - http://doi.acm.org/10.1145/1557019.1557072 DO - 10.1145/1557019.1557072 KW - cf KW - collaborative KW - dynamics KW - filtering KW - netflix KW - stair KW - temporal L1 - SN - 978-1-60558-495-9 N1 - N1 - AB - Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset. ER - TY - CHAP AU - Liang, Huizhi AU - Xu, Yue AU - Li, Yuefeng AU - Nayak, Richi A2 - Wen, Peng A2 - Li, Yuefeng A2 - Polkowski, Lech A2 - Yao, Yiyu A2 - Tsumoto, Shusaku A2 - Wang, Guoyin T1 - Tag Based Collaborative Filtering for Recommender Systems T2 - Rough Sets and Knowledge Technology PB - Springer Berlin Heidelberg C1 - Berlin, Heidelberg PY - 2009/ VL - 5589 IS - SP - 666 EP - 673 UR - http://dx.doi.org/10.1007/978-3-642-02962-2\_84 DO - 10.1007/978-3-642-02962-2\_84 KW - collaborative KW - filtering KW - recommender KW - tagging KW - taggingsurvey KW - toread L1 - SN - 978-3-642-02961-5 N1 - CiteULike: Tag Based Collaborative Filtering for Recommender Systems N1 - AB - Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches. ER - TY - CONF AU - Parra, Denis AU - Brusilovsky, Peter A2 - T1 - Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike T2 - Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web PB - C1 - PY - 2009/06 CY - VL - 467 IS - SP - EP - UR - http://ceur-ws.org/Vol-467/paper5.pdf DO - KW - algorithms KW - citedBy:doerfel2012leveraging KW - collaborative KW - evaluation KW - filtering L1 - SN - N1 - N1 - AB - Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. ER - TY - CONF AU - Parra, Denis AU - Brusilovsky, Peter A2 - T1 - Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike T2 - Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web PB - C1 - PY - 2009/06 CY - VL - 467 IS - SP - EP - UR - http://ceur-ws.org/Vol-467/paper5.pdf DO - KW - collaborative KW - filtering KW - folksonomy KW - item KW - recommender KW - social KW - tagging L1 - SN - N1 - N1 - AB - Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. ER - TY - JOUR AU - Takács, Gábor AU - Pilászy, István AU - Németh, Bottyán AU - Tikk, Domonkos T1 - Scalable Collaborative Filtering Approaches for Large Recommender Systems JO - Journal of Machine Learning Research PY - 2009/06 VL - 10 IS - SP - 623 EP - 656 UR - http://dl.acm.org/citation.cfm?id=1577069.1577091 DO - KW - collaborative KW - filtering KW - recommender L1 - SN - N1 - N1 - AB - The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets. ER - TY - CONF AU - Jaeschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Tag Recommendations in Folksonomies T2 - Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 13 EP - 20 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf DO - KW - 2007 KW - bookmarking KW - collaborative KW - filtering KW - folksonomy KW - itegpub KW - l3s KW - myown KW - recommender KW - social L1 - SN - 978-3-86010-907-6 N1 - N1 - AB - ER - TY - CONF AU - Jaeschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Tag Recommendations in Folksonomies T2 - Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 13 EP - 20 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf DO - KW - 2007 KW - bookmarking KW - collaborative KW - filtering KW - folksonomy KW - itegpub KW - l3s KW - myown KW - recommender KW - social L1 - SN - 978-3-86010-907-6 N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CHAP AU - Schafer, J. Ben AU - Frankowski, Dan AU - Herlocker, Jon AU - Sen, Shilad A2 - Brusilovsky, Peter A2 - Kobsa, Alfred A2 - Nejdl, Wolfgang T1 - Collaborative Filtering Recommender Systems T2 - The Adaptive Web: Methods and Strategies of Web Personalization PB - Springer C1 - Berlin, Heidelberg PY - 2007/ VL - 4321 IS - SP - 291 EP - 324 UR - http://dx.doi.org/10.1007/978-3-540-72079-9_9 DO - KW - cf KW - collaborative KW - filtering KW - recommender KW - webzu L1 - SN - 978-3-540-72078-2 N1 - N1 - AB - One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field. ER - TY - JOUR AU - Adomavicius, G. AU - Tuzhilin, A. T1 - Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions JO - Knowledge and Data Engineering, IEEE Transactions on PY - 2005/06 VL - 17 IS - 6 SP - 734 EP - 749 UR - DO - 10.1109/TKDE.2005.99 KW - collaborative KW - content-based KW - contextual KW - filtering KW - information KW - survey L1 - SN - N1 - N1 - AB - This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. ER - TY - JOUR AU - Herlocker, J.L. AU - Konstan, J.A. AU - Terveen, L.G. AU - Riedl, J.T. T1 - Evaluating collaborative filtering recommender systems JO - ACM Transactions on Information Systems PY - 2004/ VL - 22 IS - 1 SP - 5 EP - 53 UR - DO - KW - collaborative KW - evaluation KW - filtering KW - recommender L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Herlocker, Jonathan L. AU - Konstan, Joseph A. AU - Terveen, Loren G. AU - Riedl, John T. T1 - Evaluating collaborative filtering recommender systems JO - ACM Trans. Inf. Syst. PY - 2004/01 VL - 22 IS - SP - 5 EP - 53 UR - http://doi.acm.org/10.1145/963770.963772 DO - 10.1145/963770.963772 KW - collaEvaluating KW - collaborative KW - eval KW - evaluation KW - filtering KW - recommend KW - recommendation KW - recommender KW - systems L1 - SN - N1 - Evaluating collaborative filtering recommender systems N1 - AB - Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated. ER - TY - CONF AU - Whitby, Andrew AU - Jøsang, Audun AU - Indulska, Jadwiga A2 - T1 - Filtering Out Unfair Ratings in Bayesian Reputation Systems T2 - PB - C1 - PY - 2004/ CY - VL - IS - SP - EP - UR - DO - KW - Filtering KW - bayes KW - info20 KW - rating KW - reputation KW - review KW - unfair L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Melville, Prem AU - Mooney, Raymod J. AU - Nagarajan, Ramadass A2 - T1 - Content-boosted Collaborative Filtering for Improved Recommendations T2 - Eighteenth National Conference on Artificial Intelligence PB - American Association for Artificial Intelligence C1 - Menlo Park, CA, USA PY - 2002/ CY - VL - IS - SP - 187 EP - 192 UR - http://dl.acm.org/citation.cfm?id=777092.777124 DO - KW - collaborative KW - content KW - filtering KW - hybrid KW - recommender L1 - SN - 0-262-51129-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Sarwar, Badrul AU - Karypis, George AU - Konstan, Joseph AU - Riedl, John A2 - T1 - Item-based collaborative filtering recommendation algorithms T2 - Proceedings of the 10th international conference on World Wide Web PB - ACM C1 - New York, NY, USA PY - 2001/ CY - VL - IS - SP - 285 EP - 295 UR - http://doi.acm.org/10.1145/371920.372071 DO - 10.1145/371920.372071 KW - cf KW - collaborative KW - filtering KW - item KW - recommender L1 - SN - 1-58113-348-0 N1 - N1 - AB - ER - TY - CONF AU - Sarwar, Badrul AU - Karypis, George AU - Konstan, Joseph AU - Riedl, John A2 - T1 - Item-based collaborative filtering recommendation algorithms T2 - WWW '01: Proceedings of the 10th International Conference on World Wide Web PB - ACM C1 - New York, NY, USA PY - 2001/ CY - VL - IS - SP - 285 EP - 295 UR - http://portal.acm.org/citation.cfm?id=372071 DO - 10.1145/371920.372071 KW - collaborative KW - filtering KW - recommender KW - stair KW - tag L1 - SN - 1-58113-348-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Herlocker, Jonathan L. AU - Konstan, Joseph A. AU - Riedl, John A2 - T1 - Explaining collaborative filtering recommendations T2 - CSCW '00: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work PB - ACM C1 - New York, NY, USA PY - 2000/ CY - VL - IS - SP - 241 EP - 250 UR - http://portal.acm.org/citation.cfm?id=358995 DO - 10.1145/358916.358995 KW - cf KW - collaborative KW - explaining KW - filtering KW - recommender L1 - SN - 1-58113-222-0 N1 - Explaining collaborative filtering recommendations N1 - AB - Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users. ER - TY - JOUR AU - Kautz, Henry AU - Selman, Bart AU - Shah, Mehul T1 - Referral Web: combining social networks and collaborative filtering JO - Communications of the ACM PY - 1997/03 VL - 40 IS - 3 SP - 63 EP - 65 UR - http://doi.acm.org/10.1145/245108.245123 DO - 10.1145/245108.245123 KW - collaborative KW - filtering KW - hybrid KW - network KW - recommender KW - social KW - stair L1 - SN - N1 - N1 - AB - ER -