ABSTRACT
In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the delicious bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.
- Rabeeh Abbasi and Steffen Staab, 'Introducing triple play for improved resource retrieval in collaborative tagging systems', in In: Proc. of ECIR'08 Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR 2008), (3 2008).Google Scholar
- Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin, 'Incorporating contextual information in recommender systems using a multidimensional approach', ACM Trans. Inf. Syst., 23(1), 103--145, (2005). Google ScholarDigital Library
- Gediminas Adomavicius and Alexander Tuzhilin, 'Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions.', IEEE Trans. Knowl. Data Eng., 17(6), 734--749, (2005). Google ScholarDigital Library
- J. Arenas-García, A. Meng, K. B. Petersen, T. L. Schiøler, L. K. Hansen, and J. Larsen, 'Unveiling music structure via PLSA similarity fusion', in IEEE International Workshop on Machine Learning for Signal Processing, pp. 419--424. IEEE Press, (aug 2007).Google ScholarCross Ref
- David A. Cohn and Thomas Hofmann, 'The missing link - a probabilistic model of document content and hypertext connectivity', in NIPS, eds., Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, pp. 430--436. MIT Press, (2000).Google Scholar
- Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl, 'Evaluating collaborative filtering recommender systems', ACM Trans. Inf. Syst., 22(1), 5--53, (2004). Google ScholarDigital Library
- Paul Heymann, Georgia Koutrika, and Hector Garcia-Molina, 'Can social bookmarking improve web search?', in WSDM '08: Proc. of the int. conf. on Web search and web data mining, pp. 195--206, New York, NY, USA, (2008). ACM. Google ScholarDigital Library
- Paul Heymann, Daniel Ramage, and Hector Garcia-Molina, 'Social tag prediction', in SIGIR '08: Proc. of the 31st ann. int. ACM SIGIR conf. on Research and development in information retrieval, pp. 531--538, New York, NY, USA, (2008). ACM. Google ScholarDigital Library
- Thomas Hofmann, 'Probabilistic latent semantic analysis', in Proc. of Uncertainty in Artificial Intelligence, UAI'99, (1999). Google ScholarDigital Library
- Andreas Hotho, Robert Jäschke, Christoph Schmitz, and Gerd Stumme, 'Information retrieval in folksonomies: Search and ranking', in ESWC, eds., York Sure and John Domingue, volume 4011 of Lecture Notes in Computer Science, pp. 411--426. Springer, (2006). Google ScholarDigital Library
- Robert Jäschke, Leandro Marinho, Andreas Hotho, Lars Schmidt-Thieme, and Gerd Stumme, 'Tag recommendations in folksonomies', in Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), ed., Alexander Hinneburg, pp. 13--20, (sep 2007).Google Scholar
- Xin Jin, Yanzan Zhou, and Bamshad Mobasher, 'Web usage mining based on probabilistic latent semantic analysis.', in KDD, eds., Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel, pp. 197--205. ACM, (2004). Google ScholarDigital Library
- Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos, 'Tag recommendations based on tensor dimensionality reduction', in RecSys '08: Proc. of the 2008 ACM conf. on Recommender systems, pp. 43--50, New York, NY, USA, (2008). ACM. Google ScholarDigital Library
- Martin Szomszor, Ciro Cattuto, Harith Alani, Kieron O'Hara, Andrea Baldassarri, Vittorio Loreto, and Vito D. P. Servedio, 'Folksonomies, the semantic web, and movie recommendation', in Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007), pp. 71--84, (2007).Google Scholar
- Xuanhui Wang, Jian-Tao Sun, Zheng Chen, and ChengXiang Zhai, 'Latent semantic analysis for multiple-type interrelated data objects', in SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 236--243, New York, NY, USA, (2006). ACM. Google ScholarDigital Library
- Robert Wetzker, Carsten Zimmermann, and Christian Bauckhage, 'Analyzing social bookmarking systems: A del. icio. us cookbook', in Mining Social Data (MSoDa) Workshop Proceedings, pp. 26--30. ECAI 2008, (July 2008).Google Scholar
- Gui-Rong Xue, Wenyuan Dai, Qiang Yang, and Yong Yu, 'Topic-bridged plsa for cross-domain text classification', in SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 627--634, New York, NY, USA, (2008). ACM. Google ScholarDigital Library
Index Terms
- A hybrid approach to item recommendation in folksonomies
Recommendations
Improving recommendation accuracy based on item-specific tag preferences
Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in contextIn recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information about ...
Item recommendation using tag emotion in social cataloging services
We propose a tag-based recommendation method considering users emotions in tags.The tag weight is based on the rating and the emotion value of the tag.The emotion value of the tag is obtained using SenticNet.We apply a High-Order Singular Value ...
Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systemsTop-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Comments