Transfer Learning from APP Domain to News Domain for Dual Cold-Start Recommendation.
Jixiong Liu, Jiakun Shi, Wanling Cai, Bo Liu, Weike Pan, Qiang Yang, Zhong Ming · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2017
News recommendation has been a must-have service for most mobile device users to know what has happened in the world. In this paper, we focus on recommending latest news articles to new users, which consists of the new user coldstart challenge and the new item (i.e., news article) coldstart challenge, and is thus termed as dual cold-start recommendation (DCSR). As a response, we propose a solution called neighborhood-based transfer learning (NTL) for this new problem. Specifically, in order to address the new user cold-start challenge, we propose a cross-domain preference assumption, i.e., users with similar app-installation behaviors are likely to have similar tastes in news articles, and then transfer the knowledge of neighborhood of the coldstart users from an APP domain to a news domain. For the new item cold-start challenge, we design a category-level preference to replace the traditional item-level preference because the latter is not applicable for the new items in our problem. We then conduct empirical studies on a real industry data with both users' app-installation behaviors and news-reading behaviors, and find that our NTL is able to deliver the news articles more accurately than other methods on different ranking-oriented evaluation metrics.