Abstract
Recommender systems become increasingly significant in solving the information explosion problem. Two typical kinds of techniques treat the recommendation problem as either a rating prediction or a ranking prediction one. In contrast, we propose a two-step framework that considers recommendation as a simulation of users’ behaviors to generate ratings. The first step is to predict the probability that a user rates an item, and the second step is to predict rating values. After that, the predicted results from both steps are combined to compute the expectations of users’ ratings on items, which are used to generate recommendations. Based on this framework, we propose a hybrid approach which uses topic model in the first step and matrix factorization in the second to solve the recommendation problem. Experiments with MovieLens and EachMovie datasets demonstrate the effectiveness of the proposed framework and the recommendation approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
0 is a typical value out of the range of rating scale, which can be used to distinguish the rating value and the rating behavior.
The effectiveness is demonstrated in the experiment section, in which HTMMF using rating behaviors in the first step gets better results than the one (L&S) with same model but using rating values in the first step.
Types of implicit feedback include rating behaviors, purchase history, browsing history, and search patterns.
The original ratings from EM1 are in 0-to-1 scale. We convert it to 0-to-5 scale, and then exclude the ratings with value 0 in order to fit the proposed recommendation framework.
Since the restriction of the column width, we only report some typical performances for different K T .
References
Adomavicius, G., & Kwon, Y. (2012). Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 24(5), 896–911.
Aytekin, T., Karakaya, M.Ö. (2014). Clustering-based diversity improvement in top-n recommendation. Journal of Intelligent Information Systems, 42(1), 1–18.
Blei, D.M., Ng, A.Y., Jordan, M.I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.
Breese, J.S., Heckerman, D., Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the fourteenth conference on uncertainty in artificial intelligence (pp. 43–52). Morgan Kaufmann.
Chen, W., Niu, Z., Zhao, X., Li, Y. (2014). A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 17(2), 271–284.
Cremonesi, P., Koren, Y., Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on recommender systems (pp. 39–46). ACM.
Delgado, J., & Ishii, N. (1999). Memory-based weighted majority prediction. In ACM SIGIR’99 workshop on recommender systems. Citeseer.
Griffiths, T.L., & Steyvers, M. (2004). Finding scientific topics. In Proceedings of the National Academy of Sciences of the United States of America (Vol. 101, Suppl 1, pp. 5228–5235).
Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the fifteenth conference on uncertainty in artificial intelligence (pp. 289–296). Morgan Kaufmann
Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1), 89–115.
Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS), 20(4), 422–446.
Kantor, P.B., Rokach, L., Ricci, F., Shapira, B. (2011). Recommender systems handbook. Springer.
Koren, Y. (2008). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426–434). ACM.
Koren, Y., & Sill, J. (2011). Ordrec: An ordinal model for predicting personalized item rating distributions. In Proceedings of the fifth ACM conference on recommender systems (pp. 117–124). ACM.
Li, Y., Hu, J., Zhai, C., Chen, Y. (2010). Improving one-class collaborative filtering by incorporating rich user information. In Proceedings of the 19th ACM international conference on information and knowledge management (pp. 959–968). ACM.
Liu, N.N., Xiang, E.W., Zhao, M., Yang, Q. (2010). Unifying explicit and implicit feedback for collaborative filtering. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1445–1448). ACM.
Liu, N.N., Zhao, M., Yang, Q. (2009). Probabilistic latent preference analysis for collaborative filtering. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 759–766). ACM.
Liu, Q., Chen, E., Xiong, H., Ding, C.H., Chen, J. (2012). Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42 (1), 218–233.
Park, Y.J., & Tuzhilin, A. (2008). The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on recommender systems(pp. 11–18). ACM.
Perugini, S., Gonçalves, M.A., Fox, E.A (2004). Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems, 23(2), 107–143.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285–295). ACM.
Shang, M.S., Lü, L., Zeng, W., Zhang, Y.C., Zhou, T. (2009). Relevance is more significant than correlation: Information filtering on sparse data. EPL (Europhysics Letters), 88 (6), 68, 008.
Wei, X., & Croft, W.B. (2006). Lda-based document models for ad-hoc retrieval. In Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 178–185). ACM.
Weimer, M., Karatzoglou, A., Le, Q.V., Smola, A.J. (2007). Cofi rank-maximum margin matrix factorization for collaborative ranking. In Advances in neural information processing systems (pp. 1593–1600).
Zhao, X., Niu, Z., Chen, W. (2013). Interest before liking: Two-step recommendation approaches. Knowledge-Based Systems, 48, 46–56.
Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R. (2008). Large-scale parallel collaborative filtering for the netflix prize. In Algorithmic aspects in information and management (pp. 337–348). Springer.
Acknowledgements
This work is supported by the National Basic Research Program of China (No. 2012CB7207002), the National Natural Science Foundation of China (Project Nos. 61370137, 61250010, and 61272361), and the 111 Project of Beijing Institute of Technology. The EachMovie dataset is by courtesy of Digital Equipment Corporation and was generously provided by Paul McJones.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhao, X., Niu, Z., Chen, W. et al. A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. J Intell Inf Syst 44, 335–353 (2015). https://doi.org/10.1007/s10844-014-0334-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10844-014-0334-3