Multi-role social behavior modeling in content-free news recommendation

Yihong Zhang, Takahiro Hara

Abstract: Recommendation systems nowadays are deployed everywhere to solve the information overload problem. Thousands of news items that appear on online news platforms also need recommendations. However, news recommendation differs from product recommendation because of its focus on public interest. In this paper, we propose a multi-role social behavior model that includes a chaser model, which describes behavior patterns of chasing popular news, and a sider model, which describes behavior patterns of seeking news from a similar ideology standpoint. We propose a framework that integrates this model into a graph-based recommendation system. Unlike other intent disentangling techniques, our model explicit models social behavior patterns originated from public interest. We test our framework with two real-world news recommendation datasets. Compared to state-of-the-art baseline news recommendation models, our method achieves significantly higher recommendation accuracy. By analyzing the model outputs, we also gain a better understanding of news-seeking social behavior.

External IDs:dblp:journals/www/ZhangH25