Session 1b: Recommender Systems and Social Networks
Date: Wednesday, Sept 16, 2015, 16:00-18:00
Location: HS 5
Chair: Bamshad Mobasher
- Overlapping Community Regularization for Rating Prediction in Social Recommender Systems
by Hui Li, Dingming Wu, Wenbin Tang and Nikos Mamoulis
Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user’s rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recent approaches use data from social networks to improve accuracy. However, most of the social-network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this paper, we propose two alternative models that incorporate the overlapping community regularization into the matrix factorization framework. Our empirical study on four real datasets shows that our approaches outperform the state-of-the-art algorithms in both traditional and social-network based recommender systems regarding both cold-start users and normal users.
- Preference-oriented Social Networks: Group Recommendation and Inference
by Amirali Salehi-Abari and Craig Boutilier
Social networks facilitate a variety of social, economic, and political interactions. Homophily and social influence suggest that preferences (e.g., over products, services, political parties) are likely to be correlated among people whom directly interact in a social network. We develop a model, preference-oriented social networks, that captures such correlations of individual preferences, where preferences take the form of rankings over a set of options. We develop probabilistic inference methods for predicting individual preferences given observed social connections and partial observations of the preferences of others in the network. We exploit these predictions in a social choice context to make group decisions or recommendations even when the preferences of some group members are unobserved. Experiments demonstrate the effectiveness of our algorithms and the improvements made possible by accounting for social ties.
- A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
by Allison J.B. Chaney, David M. Blei and Tina Eliassi-Rad
Preference-based recommendation systems have transformed how we consume media. By analyzing usage data, these methods uncover our latent preferences for items (such as articles or movies) and form recommendations based on the behavior of others with similar tastes. But traditional preference-based recommendations do not account for the social aspect of consumption, where a trusted friend might point us to an interesting item that does not match our typical preferences. In this work, we aim to bridge the gap between preference- and social-based recommendations. We develop social Poisson factorization (SPF), a probabilistic model that incorporates social network information into a traditional factorization method; SPF introduces the social aspect to algorithmic recommendation. We develop a scalable algorithm for analyzing data with SPF, and demonstrate that it outperforms competing methods on six real-world datasets; data sources include a social reader and Etsy.
- PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations
by Rana Forsati, Iman Barjasteh, Farzan Masrour, Abdol-Hossein Esfahanian and Hayder Radha
The significance of social-enhanced recommender systems is increasing, along with its practicality, as online reviews, ratings, friendship links, and follower relationships are increasingly becoming available. In recent years, there has been an upsurge of interest in exploiting social information, such as trust and distrust relations in recommendation algorithms. The goal is to improve the quality of suggestions and mitigate the data sparsity and the cold-start users problems in existing systems. In this paper, we introduce a general collaborative social ranking model to rank the latent features of users extracted from rating data based on the social context of users. In contrast to existing social regularization methods, the proposed framework is able to simultaneously leverage trust, distrust, and neutral relations, and has a linear dependency on the social network size. By integrating the ranking based social regularization idea into the matrix factorization algorithm, we propose a novel recommendation algorithm, dubbed PushTrust. Our experiments on the Epinions dataset demonstrate that collaboratively ranking the latent features of users by exploiting trust and distrust relations leads to a substantial increase in performance, and to effectively deal with cold-start users problem.