Multi-task Learning for Recommendation over Heterogeneous Information Network (Extended abstract)

Hui Li, Yanlin Wang, Ziyu Lyu, Jieming Shi

Abstract: Heterogeneous Information Network based Recommender Systems (HIN-based RS) can model the complex interactions between different objects in RS. However, existing models assume HIN is invariable and merely use HIN as a data source for assisting recommendation. In this paper, we summarize our multi-task learning framework MTRec for recommendation over HIN. MTRec relies on the self-attention mechanism to learn the semantics of meta-paths in HIN and jointly optimizes the tasks of both recommendation and link prediction. Using a Bayesian task weight learner, MTRec is able to achieve the balance of two tasks during optimization automatically. Moreover, MTRec provides good interpretability of recommendation through a “translation” mechanism which is used to model the three-way interactions among users, items and the meta-paths connecting them. Experimental results demonstrate the effectiveness and the robustness of MTRec over state-of-the-art models.