Social Recommendation with Strong and Weak Ties

By Xin Wang, Wei Lu, Martin Ester, C. Wang, Chun Chen. 2016

151 citations recorded by the source corpus.

With the explosive growth of online social networks, it is now well understood that social information is highly helpful to recommender systems. Social recommendation methods are capable of battling the critical cold-start issue, and thus can greatly improve prediction accuracy. The main intuition is that through trust and influence, users are more likely to develop affinity toward items consumed by their social ties. Despite considerable work in social recommendation, little attention has been paid to the important distinctions between strong and weak ties, two well-documented notions in social sciences. In this work, we study the effects of distinguishing strong and weak ties in social recommendation. We use neighbourhood overlap to approximate tie strength and extend the popular Bayesian Personalized Ranking (BPR) model to incorporate the distinction of strong and weak ties. We present an EM-based algorithm that simultaneously classifies strong and weak ties in a social network w.r.t. optimal recommendation accuracy and learns latent feature vectors for all users and all items. We conduct extensive empirical evaluation on four real-world datasets and demonstrate that our proposed method significantly outperforms state-of-the-art pairwise ranking methods in a variety of accuracy metrics.

Subfields: Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Chuan Shi
2. Philip S. Yu
3. Binbin Hu
4. Jian Tang
5. Laurent Charlin
6. Yifan Wang
7. Weiping Song
8. Wayne Xin Zhao

In Statistical and Nonlinear Physics

1. Chuan Shi
2. Philip S. Yu
3. Jian Tang
4. Binbin Hu
5. Wayne Xin Zhao
6. Laurent Charlin
7. Ming Zhang
8. Jure Leskovec

Citations in the corpus

• Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (2018, background): “Since CF methods usually suffer from cold-start problem, many works attempt to leverage additional information for recommendation, such as social information [33, 42, 43], location information [39], and heterogeneous information [5].”
• Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, background): “rom the user’s preferences. Tang et al. [31] and Tang et al. [30] proposed multi-facet trust relations, which relies on additional side information (e.g., item category) to define facets. Wang et al. [35] and Wang et al. [34] distinguished strong and weak ties among users for recommendation in socia”