ABSTRACT
An accurate and comprehensive user modeling technique is crucial for the quality of recommender systems. Traditionally, we model user preferences using only actions from the target site and may suffer from cold-start problem. As nowadays people normally engage in multiple online sites for various needs, we consider leveraging the cross-site actions to improve the user modeling accuracy. Specifically, in this paper we aim at achieving a more comprehensive and accurate user modeling by modeling user's actions in multiple aligned heterogeneous sites simultaneously. To do so, we propose a modularized probabilistic graphical model framework JUMA. We further integrate topic model and matrix factorization into JUMA for joint user modeling over text-based and item-based sites. We assemble and publish large-scale dataset for comprehensive analyzing and evaluation. Experimental results show that our framework JUMA out performs traditional within-site user modeling techniques, especially for cold-start scenarios. For cold-start users, we achieve relative improvements of 9.3% and 12.8% comparing to existing within-site approaches for recommendation in item-based and text-based sites respectively. Thus we draw the conclusion that aligning heterogeneous sites and modeling users jointly do help to improve the quality of online recommender systems.
Supplemental Material
- S. Berkovsky, T. Kuflik, and F. Ricci. Cross-domain mediation in collaborative filtering. In User Modeling 2007, pages 355--359. Springer, 2007. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JML, 2003. Google ScholarDigital Library
- K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, and Y. Yu. Collaborative personalized tweet recommendation. In SIGIR, 2012. Google ScholarDigital Library
- S. Chib and E. Greenberg. Understanding the metropolis-hastings algorithm. The american statistician, 49(4):327--335, 1995.Google Scholar
- I. Fernández-Tobıas, I. Cantador, M. Kaminskas, and F. Ricci. Cross-domain recommender systems: A survey of the state of the art. In SCIR, 2012.Google Scholar
- H. Gao, J. Tang, and H. Liu. Addressing the cold-start problem in location recommendation using geo-social correlations. Data Mining and Knowledge Discovery, pages 1--25, 2014. Google ScholarDigital Library
- F. Godin, V. Slavkovikj, W. De Neve, B. Schrauwen, and R. Van de Walle. Using topic models for twitter hashtag recommendation. In WWW, 2013. Google ScholarDigital Library
- N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 595--604. ACM, 2011. Google ScholarDigital Library
- F. Hu and Y. Yu. Interview process learning for top-n recommendation. In Proceedings of the 7th ACM conference on Recommender systems, pages 331--334. ACM, 2013. Google ScholarDigital Library
- T. Iofciu, P. Fankhauser, F. Abel, and K. Bischoff. Identifying users across social tagging systems. In ICWSM, 2011.Google Scholar
- M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems, pages 135--142. ACM, 2010. Google ScholarDigital Library
- H. Kautz, B. Selman, and M. Shah. Referral web: combining social networks and collaborative filtering. Communications of the ACM, 40(3):63--65, 1997. Google ScholarDigital Library
- N. Koenigstein, G. Dror, and Y. Koren. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the fifth ACM conference on Recommender systems, pages 165--172. ACM, 2011. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426--434. ACM, 2008. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, pages 30--37, 2009. Google ScholarDigital Library
- X. N. Lam, T. Vu, T. D. Le, and A. D. Duong. Addressing cold-start problem in recommendation systems. In Proceedings of the 2nd international conference on Ubiquitous information management and communication, pages 208--211. ACM, 2008. Google ScholarDigital Library
- G. Linden, B. Smith, and J. York. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):76--80, 2003. Google ScholarDigital Library
- H. Liu and P. Maes. Interestmap: Harvesting social network profiles for recommendations. Beyond Personalization-IUI, page 56, 2005.Google Scholar
- J. Liu, F. Zhang, X. Song, Y.-I. Song, C.-Y. Lin, and H.-W. Hon. What's in a name?: an unsupervised approach to link users across communities. In Proceedings of the 6th ACM international conference on Web search and data mining, pages 495--504. ACM, 2013. Google ScholarDigital Library
- S. Liu, S. Wang, F. Zhu, J. Zhang, and R. Krishnan. Hydra: Large-scale social identity linkage via heterogeneous behavior modeling. In SIGMOD, 2014. Google ScholarDigital Library
- J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In RecSys, pages 165--172. ACM, 2013. Google ScholarDigital Library
- A. Mnih and R. Salakhutdinov. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2007. Google ScholarDigital Library
- A. Narayanan and V. Shmatikov. De-anonymizing social networks. In Security and Privacy, 2009 30th IEEE Symposium on, pages 173--187. IEEE, 2009. Google ScholarDigital Library
- A. Narayanan and V. Shmatikov. Myths and fallacies of personally identifiable information. Communications of the ACM, 53(6):24--26, 2010. Google ScholarDigital Library
- S. Sahebi and P. Brusilovsky. Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation. In UMAP. 2013.Google Scholar
- A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In SIGIR, pages 253--260, 2002. Google ScholarDigital Library
- S. Tan, J. Bu, X. Qin, C. Chen, and D. Cai. Cross domain recommendation based on multi-type media fusion. Neurocomputing, 127:124--134, 2014. Google ScholarDigital Library
- S. Tan, Z. Guan, D. Cai, X. Qin, J. Bu, and C. Chen. Mapping users across networks by manifold alignment on hypergraph. In AAAI, 2014. Google ScholarDigital Library
- J. Tang, S. Wu, J. Sun, and H. Su. Cross-domain collaboration recommendation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1285--1293. ACM, 2012. Google ScholarDigital Library
- M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1(1--2):1--305, 2008. Google ScholarDigital Library
- J. Zhang, X. Kong, and P. S. Yu. Predicting social links for new users across aligned heterogeneous social networks. In 2013 IEEE 13th International Conference on Data Mining, pages 1289--1294. IEEE, 2013.Google ScholarCross Ref
- Y. Zhang and J. Koren. Efficient bayesian hierarchical user modeling for recommendation system. In SIGIR, pages 47--54. ACM, 2007. Google ScholarDigital Library
- Z.-K. Zhang, C. Liu, Y.-C. Zhang, and T. Zhou. Solving the cold-start problem in recommender systems with social tags. EPL, 92(2):28002, 2010.Google ScholarCross Ref
- E. Zhong, W. Fan, J. Wang, L. Xiao, and Y. Li. Comsoc: adaptive transfer of user behaviors over composite social network. In Proceedings of the 18th ACM SIGKDD, pages 696--704. ACM, 2012. Google ScholarDigital Library
- K. Zhou, S.-H. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 315--324. ACM, 2011. Google ScholarDigital Library
Index Terms
Joint User Modeling across Aligned Heterogeneous Sites
Recommendations
User and Item Modeling Methods Using Customer Reviews towards Recommender System Based on Personal Values
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03This paper proposes user and item modeling methods towards recommender systems based on personal values. Marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. While existing ...
Cross-representation mediation of user models
Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks
WWW '19: The World Wide Web ConferenceMany e-commerce platforms today allow users to give their rating scores and reviews on items as well as to establish social relationships with other users. As a result, such platforms accumulate heterogeneous data including numeric scores, short textual ...
Comments