Inferring User Preference Using Distance Dependent Chinese Restaurant Process and Weighted Distribution for a Content Based Recommender System

Authors: Bagher Rahimpour Cami, Hamid Hassanpour, Hoda Mashayekhi

Abstract:

Nowadays websites provide a vast number of resources for users. Recommender systems have been developed as an essential element of these websites to provide a personalized environment for users. They help users to retrieve interested resources from large sets of available resources. Due to the dynamic feature of user preference, constructing an appropriate model to estimate the user preference is the major task of recommender systems. Profile matching and latent factors are two main approaches to identify user preference. In this paper, we employed the latent factor and profile matching to cluster the user profile and identify user preference, respectively. The method uses the Distance Dependent Chines Restaurant Process as a Bayesian nonparametric framework to extract the latent factors from the user profile. These latent factors are mapped to user interests and a weighted distribution is used to identify user preferences. We evaluate the proposed method using a real-world data-set that contains news tweets of a news agency (BBC). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach related to existing methods, and its ability to effectively evolve over time.

Keywords: Content-based recommender systems, dynamic user modeling, extracting user interests, predicting user preference.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132136

References:

[1] Linden, G., Smith, B., and York, J. “Amazon.com recommendations: Item to-item collaborative filtering”. IEEE Internet computing, 2003, 7(1), 76-80.
[2] Liu, J., Dolan, P., and Pedersen, E. R. “Personalized news recommendation based on click behavior”. In Proceedings of the 15th international conference on intelligent user interfaces, 2010, pp. 31-40.
[3] Aggarwal, Charu C. "An Introduction to Recommender Systems." Recommender Systems. Springer International Publishing, 2016, pp. 1-28.
[4] Bobadilla, Jesús, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. "Recommender systems survey." Knowledge-based systems, 2013, pp. 109-132.
[5] Gauch, S., Speretta, M., Chandramouli, A., and Micarelli, A. “User profiles for personalized information access”. The adaptive web, 2007, pp. 54-89.
[6] Adomavicius, G., and Tuzhilin, A. “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”. Knowledge and Data Engineering, IEEE Transactions on, 2005, 17(6), pp. 734-749.
[7] Ricci, F., Rokach, L., and Shapira, B. Recommender systems: Introduction and challenges. In Recommender systems handbook, Springer, 2015, pp. 1-34.
[8] Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., and Stettinger, M. “Basic approaches in recommendation systems”. In Recommendation systems in software engineering, Springer, 2014, pp. 15-37.
[9] Wu, H., Pei, Y., Li, B., Kang, Z., Liu, X., and Li, H. “Item recommendation in collaborative tagging systems via heuristic data fusion”. Knowledge-Based Systems, 2015, 75, pp. 124-140.
[10] Aggarwal, C. C. “Content-based recommender systems”. In Recommender systems, Springer, 2016, pp. 139-166.
[11] Aggarwal, C. C. “Knowledge-based recommender systems”. In Recommender systems, Springer, 2016, pp. 167-197.
[12] Burke, R. “Hybrid recommender systems: Survey and experiments”. User modeling and user-adapted interaction, 2002, 12(4), pp. 331-370.
[13] Chen, Li, Guanliang Chen, and Feng Wang. "Recommender systems based on user reviews: the state of the art." User Modeling and User-Adapted Interaction 25.2, 2015, pp. 99-154.
[14] Garcin, F., Dimitrakakis, C., and Faltings, B. “Personalized news recommendation with context trees”. In Proceedings of the 7th acm conference on recommender systems, 2013, pp. 105-112.
[15] Kompan, M., & Bielikova, M. “Content-based news recommendation”. In International conference on electronic commerce and web technologies, 2010, pp. 6172.
[16] Li, L., Wang, D., Li, T., Knox, D., and Padmanabhan, B. “Scene: a scalable two-stage personalized news recommendation system”. In Proceedings of the 34th international acm sigir conference on research and development in information retrieval, 2011, pp. 125-134.
[17] Das, A. S., Datar, M., Garg, A., and Rajaram, S. “Google news personalization: scalable online collaborative filtering”. In Proceedings of the 16th international conference on world wide web, 2007, pp. 271-280.
[18] Pazzani, M. J., and Billsus, D. “Content-based recommendation systems”. In The adaptive web, Springer, 2007, pp. 325-341.
[19] Billsus, D., and Pazzani, M. J. “User modeling for adaptive news access”. User modeling and user-adapted interaction, 2000, 10(2-3), pp. 147-180.
[20] Li, L., Zheng, L., and Li, T. “Logo: a long-short user interest integration in personalized news recommendation”. In Proceedings of the fifth acm conference on recommender systems, 2011, pp. 317-320.
[21] Li, L., Zheng, L., Yang, F., and Li, T. “Modeling and broadening temporal user interest in personalized news recommendation”. Expert Systems with Applications, 2014, 41(7), pp. 3168-3177.
[22] Zheng, L., Li, L., Hong, W., and Li, T. “Penetrate: Personalized news recommendation using ensemble hierarchical clustering”. Expert Systems with Applications, 2013, 40(6), pp. 2127-2136.
[23] Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., and Sun, J. “Temporal recommendation on graphs via long-and short-term preference fusion”. In Proceedings of the 16th acm sigkdd international conference on knowledge discovery and data mining, 2010, pp. 723-732.
[24] Koren, Y., Bell, R., Volinsky, C., et al. “Matrix factorization techniques for recommender systems”. Computer, 2009, 42(8), pp. 30-37.
[25] Koren, Y. “Collaborative filtering with temporal dynamics”. Communications of the ACM, 2010, 53(4), pp. 89-97.
[26] Forbes, P., & Zhu, M. “Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation”. In Proceedings of the fifth acm conference on recommender systems, 2011 pp. 261-264.
[27] Lu, Z., Dou, Z., Lian, J., Xie, X., and Yang, Q. “Content-based collaborative filtering for news topic recommendation”. Aaai, 2015, pp. 217-223.
[28] Baltrunas, L., Ludwig, B., and Ricci, F. “Matrix factorization techniques for context aware recommendation”. In Proceedings of the fifth acm conference on recommender systems, 2011, pp. 301-304.
[29] Sahoo, N., Singh, P. V., and Mukhopadhyay, T. “A hidden markov model for collaborative filtering”. Management Information Systems Quarterly, Forth-coming, 2010, 36(4), pp. 1329-1356.
[30] Blei, D. M., and Frazier, P. I. “Distance dependent chinese restaurant processes”. Journal of Machine Learning Research, 2011, 12, pp. 2461-2488.
[31] Gershman, S. J., and Blei, D. M. “A tutorial on Bayesian nonparametric models”. Journal of Mathematical Psychology, 2012, 56(1), pp. 1-12.
[32] Teh, Y. W., Jordan, M. I., Beal, M. J., and Blei, D. M. “Hierarchical dirichlet processes”. Journal of the american statistical association, 2012, 101(476), pp. 1566-1581.
[33] Patil, G. P. “Encountered data, statistical ecology, environmental statistics, and weighted distribution methods”. Environmetrics, 1991, 2(4), pp. 377-423.
[34] Gupta, R. C., and Kirmani, S. N. U. A. “The role of weighted distributions in stochastic modeling”. Communications in Statistics-Theory and methods, 1990, 19(9), pp. 3147-3162.
[35] Patil, G. P., and Rao, C. R. “The weighted distributions: A survey of their applications”. Applications of statistics, 1977, 383, pp. 383-405.
[36] Lin, Y.-S., Jiang, J.-Y., and Lee, S.-J. “A similarity measure for text classification and clustering”. Knowledge and Data Engineering, IEEE Transactions on, 2014, 26(7), pp. 1575-1590.
[37] Hofmann, T. “Probabilistic latent semantic indexing”. In Proceedings of the 22nd annual international acm sigir conference on research and development in information retrieval, 1999, pp. 50-57.
[38] Blei, D. M., Ng, A. Y., and Jordan, M. I. “Latent dirichlet allocation”. Journal of machine learning research, 2003, 3, pp. 993-1022.
[39] Bird, S. “Nltk: the natural language toolkit”. In Proceedings of the COL-ING/ACL on interactive presentation sessions, 2006, pp. 69-72.
[40] Avazpour, I., Pitakrat, T., Grunske, L., and Grundy, J. “Dimensions and Metrics for Evaluating Recommendation Systems”. In P. M. Robillard, W. Maalej, J. R. Walker, & T. Zimmermann (Eds.), Recommendation systems in software engineering, 2014, pp. 245-273. Berlin, Heidelberg: Springer Berlin Heidelberg.