ABSTRACT
Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. Recommendations should also usually strive to satisfy a specific purpose. Within the Moviepilot mood track of the context-aware movie recommendation challenge, we propose a novel movie similarity measure that is specific to the movie property demanded by the challenge, i.e., movie mood. Our measure is further exploited by a joint matrix factorization model for recommendation. We experimentally validate the effectiveness of the proposed algorithm in exploiting mood-specific movie similarity for the recommendation with respect to several evaluation metrics, demonstrating that it could outperform several state-of-the-art approaches. In particular, mood-specific movie similarity is demonstrated to be more beneficial than general mood-based movie similarity, for the purpose of mood-specific recommendation.
- }}Adomavicius, G., Sankaranarayanan, R., Sen, S., and Tuzhilin, A., 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM TOIS, 23, 1, 103--145. Google ScholarDigital Library
- }}Adomavicius G., and Tuzhilin, A., 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17, 6, 734--749. Google ScholarDigital Library
- }}Burke, R., 2002. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 12, 4, 331--370. Google ScholarDigital Library
- }}Cantador, I., and Castells, P., 2009. Semantic contextualisation in a news recommender system. In CARS '09.Google Scholar
- }}De Carolis, B., Mazzotta, I., Novielli, N., and Silvestri, V., 2009. Using common sense in providing personalized recommendations in the tourism domain. In CARS '09.Google Scholar
- }}Deng, H., Lyu, M. R., and King, I., 2009. Effective latent space graph-based re-ranking model with global consistency. In WSDM '09, 212--221. Google ScholarDigital Library
- }}Deshpande, M., and Karypis, G., 2004. Item-based top-N recommendation algorithms. ACM TOIS, 22, 1, 143--177. Google ScholarDigital Library
- }}Gunawardana A., and Shani, G., 2009. A survey of accuracy evaluation metrics of recommendation tasks. JMLR, 10, 2935--2962. Google ScholarDigital Library
- }}Herlocker, J., Konstan, J., Borchers, A., and Riedl, J., 1999. An algorithmic framework for performing collaborative filtering. In SIGIR '99, 230--237. Google ScholarDigital Library
- }}Herlocker, J., Konstan, J., Terveen, L. G., and Riedl, J. 2004. Evaluating collaborative filtering recommender systems. ACM TOIS, 22, 1, 5--53. Google ScholarDigital Library
- }}Konstas, I., Stathopoulos, V., and Jose, J. M., 2009. On social networks and collaborative recommendation. In SIGIR '09, 195--202. Google ScholarDigital Library
- }}Koren, Y., Bell, R., and Volinsky, C., 2009. Matrix factorization techniques for recommender systems. IEEE Computer, 42, 8, 30--37. Google ScholarDigital Library
- }}Liu, N. N., Zhao, M., and Yang, Q., 2009. Probabilistic latent preference analysis for collaborative filtering. In CIKM '09, 759--766. Google ScholarDigital Library
- }}Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme L., 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI '09, 452--461. Google ScholarDigital Library
- }}Said, A., Berkovsky, S., and De Luca, E. W., 2010. Putting things in context: Challenge on context-aware movie recommendation. In CAMRa2010: Proceedings of the RecSys '10 Challenge on Context-aware Movie Recommendation, to appear. Google ScholarDigital Library
- }}Sarwar, B., Karypis, G., Konstan, J., and Reidl, J., 2001. Item-based collaborative filtering recommendation algorithms. In WWW '01, 285--295. Google ScholarDigital Library
- }}Shi, Y., Larson, M., and Hanjalic, A., 2009. Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering. In RecSys '09, 125--132. Google ScholarDigital Library
- }}Shi, Y., Larson, M., and Hanjalic, A., 2010. List-wise learning to rank with matrix factorization for collaborative filtering. In RecSys '10, to appear. Google ScholarDigital Library
- }}Su, J.-H., Yeh, H.-H., Yu, P. S., and Tseng, V., 2010. Music recommendation using content and context information mining. IEEE Intelligent Systems, 25, 1, 16--26. Google ScholarDigital Library
- }}Yildirim, H., and Krishnamoorthy, M. S., 2008. A random walk method for alleviating the sparsity problem in collaborative filtering. In RecSys '08, 131--138. Google ScholarDigital Library
- }}Zhang, J. and Pu, P., 2007. A recursive prediction algorithm for collaborative filtering recommender systems. In RecSys '07, 57--64. Google ScholarDigital Library
- }}Zheng, V. W., Zheng, Y., Xie, X., and Yang, Q., 2010. Collaborative location and activity recommendations with GPS history data. In WWW '10, 1029--1038. Google ScholarDigital Library
- }}Zhu, S., Yu, K., Chi, Y., and Gong, Y., 2007. Combining content and link for classification using matrix factorization. In SIGIR '07, 487--494. Google ScholarDigital Library
Index Terms
- Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
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