Multi-task learning for recommendation over heterogeneous information network

Hui Li, Yanlin Wang, Ziyu Lyu, Jieming Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Traditional recommender systems (RS) only consider homogeneous data and cannot fully model heterogeneous
information of complex objects and relations. Recent advances in the study of Heterogeneous Information Network (HIN) have shed
some light on how to leverage heterogeneous information in RS. However, existing HIN-based recommendation models assume HIN is invariable and merely use HIN as a data source for assisting recommendation, which limits their performance. In this paper, we propose a multi-task learning framework, called MTRec, for recommendation over HIN. MTRec relies on 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 interpretabilities 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 superiority of MTRec over state-of-the-art
HIN-based recommendation models, and the case studies we provide illustrate that MTRec enhances the explainability of RS.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Publication statusPublished - 2020
Externally publishedYes


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