A Multi-objective Learning Algorithm for Related Recommendations

Abstract

In this paper, we introduce a novel multi-objective learning algorithm for related recommendations on industrial video sharing platforms. As an indispensable part in recommender system, the related video recommender system faces several realworld challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. To solve these, we largely extended model-based collaborative filtering algorithm by adding related candidate generation stage, Two-tower DNN structure and a multi-task learning mechanism. Compared with typical baseline solutions, our proposed algorithm can capture both linear and non-linear relationships from user-item interactions, and live experiments demonstrate that it can significantly advance the state of the art on related recommendation quality.