Massimiliano Albanese
ABSTRACT
In the last few years, recommender systems have gained significant attention in the research community, due to the increasing availability of huge data collections, such as news archives, shopping catalogs, or virtual museums. In this scenario, there is a pressing need for applications to provide users with targeted suggestions to help them navigate this ocean of information. However, no much effort has yet been devoted to recommenders in the field of multimedia databases. In this paper, we propose a novel approach to recommendation in multimedia browsing systems, based on an importance ranking method that strongly resembles the well known PageRank ranking system. We model recommendation as a social choice problem, and propose a method that computes customized recommendations by originally combing intrinsic features of multimedia objects, past behavior of individual users and overall behavior of the entire community of users. We implemented a prototype of the proposed system and preliminary experiments have shown that our approach is promising.
- The phone of the future. The Economist, 381(8506):18--20, December 2006.Google Scholar
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Eng., 17(6):734--749, June 2005. Google ScholarDigital Library
- M. Albanese, A. Chianese, A. d'Acierno, V. Moscato, and A. Picariello. A multimedia recommender integrating object features and user behavior. Multimedia Tools and Application, January 2010. Google ScholarDigital Library
- A. Altman and M. Tennenholtz. Axiomatic foundations for ranking systems. Journal of Artificial Intelligence Research, 31:473--495, March 2008. Google ScholarDigital Library
- S. S. Anand, P. Kearney, and M. Shapcott. Generating semantically enriched user profiles for web personalization. ACM Trans. Interet Techn., 7(4):22, 2007. Google ScholarDigital Library
- R. Andersen, C. Borgs, J. Chayes, U. Feige, A. Flaxman, A. Kalai, V. Mirrokni, and M. Tennenholtz. Trust-based recommendation systems: an axiomatic approach. In WWW '08: Proc. of the 17th Intl. Conf. on World Wide Web, pages 199--208, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- K. J. Arrow. Social Choice and Individual Values. John Wiley & Sons, second edition edition, 1963.Google Scholar
- N. A. Baloian, P. Galdames, C. A. Collazos, and L. A. Guerrero. A model for a collaborative recommender system for multimedia learning material. In G.-J. de Vreede, L. A. Guerrero, and G. M. Raventós, editors, CRIWG, volume 3198 of Lecture Notes in Computer Science, pages 281--288. Springer, 2004.Google Scholar
- J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In ICML '04: Proc. of the twenty-first Intl. Conf. on Machine learning, page 9. ACM, 2004. Google ScholarDigital Library
- P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In M. Jarke and et al., editors, 23rd VLDB, 1997, Athens, Greece, pages 426--435. Morgan Kaufmann, 1997. Google ScholarDigital Library
- Y. Hijikata, K. Iwahama, and S. Nishida. Content-based music filtering system with editable user profile. In SAC '06: Proc. of the 2006 ACM symposium on Applied computing, pages 1050--1057. ACM, 2006. Google ScholarDigital Library
- H. K. Kim, J. K. Kim, and Y. U. Ryu. Personalized recommendation over a customer network for ubiquitous shopping. IEEE Trans. Serv. Comput., 2(2):140--151, 2009. Google ScholarDigital Library
- J. K. Kim, H. K. Kim, and Y. H. Cho. A user-oriented contents recommendation system in peer-to-peer architecture. Expert Syst. Appl., 34(1):300--312, 2008. Google ScholarDigital Library
- X. N. Lam, T. Vu, T. D. Le, and A. D. Duong. Addressing cold-start problem in recommendation systems. In ICUIMC '08: Proc. of the 2nd Intl. Conf. on Ubiquitous information management and communication, pages 208--211. ACM, 2008. Google ScholarDigital Library
- G. Lekakos and P. Caravelas. A hybrid approach for movie recommendation. Multimedia Tools Appl., 36(1--2):55--70, 2008. Google ScholarDigital Library
- M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl., 2(1):1--19, 2006. Google ScholarDigital Library
- V. Maidel, P. Shoval, B. Shapira, and M. Taieb-Maimon. Evaluation of an ontology-content based filtering method for a personalized newspaper. In RecSys '08: Proc. of the 2008 ACM Conf. on Recommender systems, pages 91--98. ACM, 2008. Google ScholarDigital Library
- K. Musial, K. Juszczyszyn, and P. Kazienko. Ontology-based recommendation in multimedia sharing systems. System Science, 34(1):97--106, 2008.Google Scholar
- J. M. O'Brien. The race to create a 'smart' google. CNN Money, November 2006.Google Scholar
- L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the web. Technical Report 1999--66, Stanford InfoLab, November 1999.Google Scholar
- M. J. Pazzani and D. Billsus. The Adaptive Web: Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, chapter Content-Based Recommendation Systems, pages 325--342. Springer, 2007. Google ScholarDigital Library
- P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman. Reputation systems. Communications of the ACM, 43(12):45--48, December 2000. Google ScholarDigital Library
- P. Resnick and R. Zeckhauser. The Economics of the Internet and E-Commerce, volume 11 of Advances in Applied Microeconomics, chapter Trust Among Strangers in Internet Trans.: Empirical Analysis of eBay's Reputation System, pages 127--157. Elsevier Science, November 2002.Google Scholar
- S. Santini. Evaluation vademecum for visual information system. storage and retrieval for media databases. In In Proceedings of SPIE, Storage and Retrieval for Image and Video Databases, VIII, pages 132--143, 2000.Google Scholar
- L. Si and R. Jin. Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model. In Proc. of the 13th ACM Intl. Conf. on Inf. and Knowledge Management, pages 156--157, Washington, D.C., USA, November 2004. ACM. Google ScholarDigital Library
- V. S. Tseng, J.-H. Su, B.-W. Wang, C.-Y. Hsiao, J. Huang, and H.-H. Yeh. Intelligent multimedia recommender by integrating annotation and association mining. In Proc. of the 2008 IEEE Intl. Conf. on Sensor Networks, Ubiquitous, and Trustworthy Computing, pages 492--499, 2008. Google ScholarDigital Library
Index Terms
- A ranking method for multimedia recommenders
Recommendations
E-commerce recommenders' authority: applying the user's opinion relevance in recommender systems
WebMedia '06: Proceedings of the 12th Brazilian Symposium on Multimedia and the webConsiderable time is wasted in searching for information on the Internet because there is such a variety of items competing for attention. In an attempt to minimize this difficulty, and to provide support in the search for interesting and useful ...
Graph-based collaborative ranking
GRank is a novel framework, designed for recommendation based on rank data.GRank handles the sparsity problem of neighbor-based collaborative ranking.GRank uses the novel TPG graph structure to model users' choice context.GRank directly ranks itemsfor a ...
Collaborative Multi-objective Ranking
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementThis paper proposes to jointly resolve row-wise and column-wise ranking problems when an explicit rating matrix is given. The row-wise ranking problem, also known as personalized ranking, aims to build user-specific models such that the correct order of ...
Comments