Trong T. Nguyen
ABSTRACT
Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this paper, we propose two potential architectures: one that models social network among users as additional observations, and another that incorporates social network into the sharing of hidden units among related users. We study the efficacies of these proposed architectures on publicly available, real-life preference datasets with social networks, yielding useful insights.
Supplemental Material
- D. H. Ackley, G. E. Hinton, and T. J. Sejnowski. A learning algorithm for boltzmann machines. Cognitive Science, 9(1):147--169, 1985.Google Scholar
Cross Ref
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE, 17(6):734--749, 2005. Google ScholarDigital Library
- X. Amatriain and J. Basilico. Recommender systems in industry: A netflix case study. In Recommender Systems Handbook, pages 385--419. Springer, 2015.Google ScholarCross Ref
- R. M. Bell and Y. Koren. Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter, 9(2):75--79, 2007. Google ScholarDigital Library
- Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. TPAMI, 35(8):1798--1828, 2013. Google ScholarDigital Library
- I. Cantador, P. Brusilovsky, and T. Kuflik. 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In RecSys, 2011. Google ScholarDigital Library
- S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu. On deep learning for trust-aware recommendations in social networks. TNNLS, (99):1--14, 2016.Google Scholar
- K. Georgiev and P. Nakov. A non-iid framework for collaborative filtering with restricted Boltzmann machines. In ICML, pages 1148--1156, 2013.Google ScholarDigital Library
- A. Gionis, H. Mannila, T. Mielik\"ainen, and P. Tsaparas. Assessing data mining results via swap randomization. TKDD, 1(3), 2007. Google ScholarDigital Library
- I. Guy. Social recommender systems. In Recommender Systems Handbook, pages 511--543. Springer, 2015.Google ScholarCross Ref
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. TOIS, 22(1):5--53, 2004. Google ScholarDigital Library
- G. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:2002, 2000. Google ScholarDigital Library
- G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, 29(6):82--97, 2012.Google ScholarCross Ref
- M. Jahrer and A. Töscher. Collaborative filtering ensemble. In KDD Cup, pages 61--74, 2012. Google ScholarDigital Library
- M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In RecSys, pages 135--142, 2010. Google ScholarDigital Library
- D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender systems: an introduction. Cambridge University Press, 2010. Google ScholarDigital Library
- R. Kiros, R. Salakhutdinov, and R. S. Zemel. Unifying visual-semantic embeddings with multimodal neural language models. TACL, 2015.Google Scholar
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, (8):30--37, 2009. Google ScholarDigital Library
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1097--1105, 2012. Google ScholarDigital Library
- H. Lee, C. Ekanadham, and A. Y. Ng. Sparse deep belief net model for visual area v2. In NIPS, pages 873--880, 2008. Google ScholarDigital Library
- H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In SIGIR, pages 203--210, 2009. Google ScholarDigital Library
- H. Ma, I. King, and M. R. Lyu. Learning to recommend with explicit and implicit social relations. TIST, 2(3):29, 2011. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: social recommendation using probabilistic matrix factorization. In CIKM, pages 931--940, 2008. Google ScholarDigital Library
- H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, pages 287--296, 2011. Google ScholarDigital Library
- C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, 2008. Google ScholarDigital Library
- M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, pages 415--444, 2001.Google ScholarCross Ref
- P. Pu, L. Chen, and R. Hu. A user-centric evaluation framework for recommender systems. In RecSys, pages 157--164, 2011. Google ScholarDigital Library
- S. Purushotham, Y. Liu, and C.-c. J. Kuo. Collaborative topic regression with social matrix factorization for recommendation systems. In ICML, pages 759--766, 2012.Google Scholar
- S. Reed, K. Sohn, Y. Zhang, and H. Lee. Learning to disentangle factors of variation with manifold interaction. In ICML, pages 1431--1439, 2014.Google Scholar
- R. Salakhutdinov and G. E. Hinton. Deep boltzmann machines. In AISTATS, pages 448--455, 2009.Google Scholar
- R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In ICML, pages 791--798, 2007. Google ScholarDigital Library
- S. Sedhain, A. K. Menon, S. Sanner, and L. Xie. AutoRec: Autoencoders meet collaborative filtering. In WWW, pages 111--112, 2015. Google ScholarDigital Library
- N. Srivastava and R. R. Salakhutdinov. Multimodal learning with deep Boltzmann machines. In NIPS, pages 2222--2230, 2012. Google ScholarDigital Library
- T. Tieleman. Training restricted boltzmann machines using approximations to the likelihood gradient. In ICML, pages 1064--1071, 2008. Google ScholarDigital Library
- T. Tran, D. Phung, and S. Venkatesh. Thurstonian Boltzmann machines: Learning from multiple inequalities. In ICML, pages 46--54, 2013.Google Scholar
- C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In KDD, pages 448--456, 2011. Google ScholarDigital Library
- H. Wang, B. Chen, and W.-J. Li. Collaborative topic regression with social regularization for tag recommendation. In IJCAI, 2013. Google ScholarDigital Library
- H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In KDD, pages 1235--1244, 2015. Google ScholarDigital Library
- E. P. Xing, R. Yan, and A. G. Hauptmann. Mining associated text and images with dual-wing harmoniums. In UAI, 2005.Google ScholarDigital Library
- X. Yang, Y. Guo, Y. Liu, and H. Steck. A survey of collaborative filtering based social recommender systems. Computer Communications, 41:1--10, 2014. Google ScholarDigital Library
Index Terms
- Representation Learning for Homophilic Preferences
Recommendations
Scalable learning of users' preferences using networked data
HT '14: Proceedings of the 25th ACM conference on Hypertext and social mediaUsers' personal information such as their political views is important for many applications such as targeted advertisements or real-time monitoring of political opinions. Huge amounts of data generated by social media users present opportunities and ...
Investigating Homophily in Online Social Networks
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01Similarity breeds connections, the principle of homophily, has been well studied in existing sociology literature. %Several studies have observed this phenomena by conducting surveys on human subjects. These studies have concluded that new ties are ...
On commenting behavior of Facebook users
HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social MediaFacebook treats friends as a single homogeneous group even though people on Facebook are possibly acquainted with diverse group of individuals and perceive their friends as representatives of different groups. It is a common observation that people tend ...
Comments