Abstract
In this article, a novel method for personalized item recommendation based on social tagging is presented. The proposed approach comprises a content-based tag propagation method to address the sparsity and “cold start” problems, which often occur in social tagging systems and decrease the quality of recommendations. The proposed method exploits (a) the content of items and (b) users' tag assignments through a relevance feedback mechanism in order to automatically identify the optimal number of content-based and conceptually similar items. The relevance degrees between users, tags, and conceptually similar items are calculated in order to ensure accurate tag propagation and consequently to address the issue of “learning tag relevance.” Moreover, the ternary relation among users, tags, and items is preserved by performing tag propagation in the form of triplets based on users' personal preferences and “cold start” degree. The latent associations among users, tags, and items are revealed based on a tensor factorization model in order to build personalized item recommendations. In our experiments with real-world social data, we show the superiority of the proposed approach over other state-of-the-art methods, since several problems in social tagging systems are successfully tackled. Finally, we present the recommendation methodology in the multimodal engine of I-SEARCH, where users' interaction capabilities are demonstrated.
- G. Adomavicius, G. Sankaranarayanan, R. Sen, and A. Tuzhilin. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23, 1 (2005), 103--145. Google ScholarDigital Library
- M. Belkin and P. Niyogi. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computing 15, 6 (2003), 1373--1396. Google ScholarDigital Library
- C. Cattuto, C. Schmitz, A. Baldassarri, V. D. P. Servedio, V. Loreto, A. Hotho, M. Grahl, and G. Stumme. 2007. Network properties of folksonomies. AI Communications 20, 4 (December 2007), 245--262. Google ScholarDigital Library
- K. Chakrabarti, M. Ortega-Binderberger, Sh. Mehrotra, and Kr. Porkaew. 2004. Evaluating refined queries in Top-k retrieval systems. IEEE Transactions on Knowledge and Data Engineering 16, 2 (2004), 256--270. Google ScholarDigital Library
- P. Daras, S. Manolopoulou, and A. Axenopoulos. 2012. Search and retrieval of rich media objects supporting multiple multimodal queries. IEEE Transactions on Multimedia 4, 3 (2012), 734--746. Google ScholarDigital Library
- J. French and X.-Y. Jin. 2004. An empirical investigation of the scalability of a multiple viewpoint CBIR system. In Proceedings of International Conference on Image and Video Retrieval. 252--260.Google ScholarCross Ref
- G. Furnas, T. Landauer, L. Gomez, and S. Dumais. 1987. The vocabulary problem in human-system communication. Commun. ACM 30, 11 (1987), 964--971. Google ScholarDigital Library
- S. Golder and B. Huberman. 2005. The Structure of Collaborative Tagging Systems. Computing Research Repository. Retrieved from http://arxiv.org/pdf/cs.dl/0508082.Google Scholar
- H. Halpin, V. Robu, and H. Shepherd. 2007. The complex dynamics of collaborative tagging. In Proceedings of the 16th International Conference on World Wide Web. 211--220. Google ScholarDigital Library
- J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transaction on Information Systems 22, 1 (2004), 5--53. Google ScholarDigital Library
- S. C. H. Hoi, M. R. Lyu, and R. Jin. 2006. A unified log-based relevance feedback scheme for image retrieval. IEEE Transaction on Knowledge and Data Engineering 18, 4 (2006), 509--524. Google ScholarDigital Library
- F. Jing, B. Zhang, F. Z. Lin, W. Y. Ma, and H. J. Zhang. 2001. A novel region-based image retrieval method using relevance feedback. In Proceedings of the 2001 ACM Workshops on Multimedia: Multimedia Information Retrieval (MULTIMEDIA'01). ACM, New York, NY, 28--31. Google ScholarDigital Library
- T. G. Kolda and J. Sun. 2008. Scalable tensor decompositions for multi-aspect data mining. In Proceedings of International Conference on Data Mining. 363--372. Google ScholarDigital Library
- R. Lambiotte and M. Ausloos. 2006. Collaborative tagging as a tripartite network. Lecture Notes in Computer Science 3993 (2006), 1114--1117. Google ScholarDigital Library
- L. De Lathauwer, B. De Moor, and J. Vandewalle. 2000. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 4 (2000), 1253--1278. Google ScholarDigital Library
- X. Li, C. G. M. Snoek, and M. Worring. 2008. Learning tag relevance by neighbor voting for social image retrieval. In Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval. ACM, New York, NY, 180--187. Google ScholarDigital Library
- D. Liu and T. Chen. 2005. Probabilistic relevance feedback with binary semantic feature vectors. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 513--516.Google Scholar
- L. B. Marinho, A. Nanopoulos, L. Schmidt-Thieme, R. Jaschke, A. Hotho, G. Stumme, and P. Symeonidis. 2011. Social Tagging Recommender Systems. In Recommender Systems Handbook (1st ed.), F. Ricci, L. Rokach, and B. Shapira (Eds.). Springer, 615--644.Google Scholar
- B. Markines, C. Cattuto, F. Menczer, D. Benz, A. Hotho, and G. Stumme. 2009. Evaluating similarity measures for emergent semantics of social tagging. In Proceedings of the 18th International Conference on World Wide Web. ACM, New York, NY, 641--650. Google ScholarDigital Library
- A. Nanopoulos, D. Rafailidis, P. Symeonidis, and Y. Manolopoulos. 2010. MusicBox: Personalized music recommendation based on cubic analysis of social tags. IEEE Transactions on Audio, Speech and Language Processing 18, 2 (2010), 407--412. Google ScholarDigital Library
- S. Papadopoulos, Y. Kompatsiaris, and A. Vakali. 2010. A graph-based clustering scheme for identifying related tags in folksonomies. In Proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery. Springer-Verlag, 65--76. Google ScholarDigital Library
- K. Porkaew, K. Chakrabarti, and S. Mehrotra. 1999. Query refinement for multimedia similarity retrieval in MARS. In Proceedings of the 7th ACM International Conference on Multimedia (Part 1). ACM, 235--238. Google ScholarDigital Library
- G.-J. Qi, C. Aggarwal, Q. Tian, H. Ji, and T. S. Huang. 2012. Exploring context and content links in social media: A latent space method. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 5 (2012), 850--862. Google ScholarDigital Library
- S. Rendle. 2010. Factorization Machines. In Proceedings of International Conference on Data Mining. IEEE, Washington, DC, USA, 995--1000. Google ScholarDigital Library
- S. Rendle, L. B. Marinho, A. Nanopoulos, and L. Schmidt-Thieme. 2009. Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM Conference on Knowledge Discovery and Data Mining. ACM, 727--736. Google ScholarDigital Library
- J. B. Scafer, D. Frankowski, J. Herlocker, and S. Jen. 2007. Collaborative filtering recommender systems. In The Adaptive Web. Springer, Berlin, 291--324. Google ScholarDigital Library
- S. G. Sevil, O. Kucuktunc, P. Duygulu, and F. Can. 2010. Automatic tag expansion using visual similarity for photo sharing websites. Multimedia Tools and Applications 49, 1 (August 2010), 81--99. Google ScholarDigital Library
- A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. 2000. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 12 (December 2000), 1349--1380. Google ScholarDigital Library
- P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos. 2010. A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis. IEEE Transactions on Knowledge and Data Engineering 22 (February 2010), 179--192. Issue 2. Google ScholarDigital Library
- J. Tang, G.-J. Qi, L. Zhang, and C. Xu. 2012. Cross-Space Affinity Learning with Its Application to Movie Recommendation. IEEE Transactions on Knowledge and Data Engineering (2012), accepted. Google ScholarDigital Library
- J. Tang, S. Yan, R. Hong, G. J. Qi, and T. S. Chua. 2009. Inferring semantic concepts from community-contributed images and noisy tags. In Proceedings of the 17th ACM International Conference on Multimedia. ACM, New York, NY, USA, 223--232. Google ScholarDigital Library
- K. Tso-Sutter, B. Marinho, and L. Schmidt-Thieme. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the 2008 ACM Symposium on Applied Computing. ACM, New York, NY, 1995--1999. Google ScholarDigital Library
- J. R. R. Uijlings, A. W. M. Smeulders, and R. J. H. Scha. 2010. Real-time visual concept classification. IEEE Transactions on Multimedia 12, 7 (2010), 665--681. Google ScholarDigital Library
- L. Wu, C. Faloutsos, K. Sycara, and T. Payne. 2000. FALCON: Feedback adaptive loop for content-based retrieval. In Proceedings of the 26th International Conference on Very Large Data Bases. Morgan Kaufmann, San Francisco, CA, 297--306. Google ScholarDigital Library
- X. Wu, L. Zhang, and Y. Yu. 2006. Exploring social annotations for the semantic web. In Proceedings of the 15th International Conference on World Wide Web. ACM, New York, NY, 417--426. Google ScholarDigital Library
- Y. Xu, L. Zhang, and W. Liu. 2006. Cubic Analysis of Social Bookmarking for Personalized Recommendation. In Proceedings of the 8th Asia-Pacific Web Conference. Springer, 733--738. Google ScholarDigital Library
- Y. Yang, D. Xu, F. Nie, J. Luo, and Y. Zhuang. 2009. Ranking with local regression and global alignment for cross media retrieval. In Proceedings of the ACM International Conference on Multimedia. 175--184. Google ScholarDigital Library
Index Terms
Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging
Recommendations
Pairwise interaction tensor factorization for personalized tag recommendation
WSDM '10: Proceedings of the third ACM international conference on Web search and data miningTagging 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 ...
A Random Walk Model for Item Recommendation in Social Tagging Systems
Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be ...
Personalized Image Tag Recommendation Algorithm for Web2.0 Platform Utilizing Tensor Factorization
ISDEA '14: Proceedings of the 2014 Fifth International Conference on Intelligent Systems Design and Engineering ApplicationsIn this paper, we a novel personalized image tag recommendation algorithm based on tensor factorization which is suitable to be used in the Web2.0 Platform. Firstly, the framework of the personalized image tag recommendation system is given, which is ...
Comments