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

33 Citations (Scopus)


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)789-802
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number2
Publication statusPublished - 1 Feb 2022
Externally publishedYes


  • heterogeneous information network
  • multi-task learning
  • Recommender systems

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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