Author: Sanjay Purushotham, Yan Liu, and C.-C. Jay Kuo

Social networking sites such as Facebook, YouTube, and Lastfm, have become a popular platform for users to connect with friends and share contents (e.g., music, images, and news). The availability of social networks between people has significantly enriched the semantics of links and contents on the web. A fundamental question is whether and how social networks can help to improve recommendation systems, such as product recommendations, advertisement targeting, and scientific paper suggestions. In particular, given the rich content information available, will we have any additional gain by considering social networks? The answer to this question is of great interest to both academia and industries. Our goal in this research is to provide insights into this direction.

Most existing work has been focused on utilizing either content or social network information, but few have considered them jointly. In our work, we propose a hierarchical Bayesian model to integrate social network structure (using matrix factorization) and item content-information (using topic model) for item recommendation. We connect these two data sources through the shared user latent feature space. The matrix factorization of social network will learn the low-rank user latent feature space, while topic modeling provides a content representation of the items in the item latent feature space, in order to make social recommendations.

Fig. 1: Our model seamlessly integrates Content and social network information in collaborative filtering framework

For future work, we will work on incorporating the attention of users into our recommendation system model. We will also work on improving the scalability and interpretability of our model.

For future work, we will work on incorporating the attention of users into our recommendation system model. We will also work on improving the scalability and interpretability of our model.

• [1] Sanjay Purushotham, Yan Liu, C.-C. Jay Kuo, “Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems”, 29th International Conference on Machine Learning, Edinburgh, Scotland, 2012 (ICML 2012)
• [2] Wang, Chong and Blei, David M., “Collaborative topic modeling for recommending scientific articles”, KDD, ACM, 2011.
• [3] Hu, Yifan, Koren, Y., and Volinsky, C. “Collaborative filtering for implicit feedback datasets”. ICDM, 2008
• [4] Ma, Hao, Yang, Haixuan, Lyu, Michael R., and King, Irwin. “Sorec: social recommendation using probabilistic matrix factorization”, CIKM, 2008
• [5] Salakhutdinov R and Mnih, Andriy, “Probabilistic Matrix Factorization”, NIPS, 2008