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Hierarchical user intention-preference for sequential recommendation with relation-aware heterogeneous information network embedding

  • Fan Yang
    ,
  • Gangmin Li
    ,
  • Yong Yue
  • Xi'an Jiaotong-Liverpool University
Research Output: Contribution to journal Article Peer-review

Abstract

Existing recommender systems usually make recommendations by exploiting the binary relationship between users and items, and assume that users only have flat preferences for items. They ignore the users' intentions as an origin and driving force for users' performance. Cognitive science tells us that users' preference comes from an explicit intention. They first have an intention to possess a particular (type of) item(s) and then their preferences emerge when facing multiple available options. Most of the data used in recommender systems are composed of heterogeneous information contained in a complicated network's structure. Learning effective representations from these heterogeneous information networks (HINs) can help capture the user's intention and preferences, therefore, improving recommendation performance. We propose a hierarchical user's intention and preferences modeling for sequential recommendation based on relation-aware HIN embedding (HIP-RHINE). We first construct a multirelational semantic space of heterogeneous information networks to learn node embedding based on specific relations. We then model user's intention and preferences using hierarchical trees. Finally, we leverage the structured decision patterns to learn user's preferences and thereafter make recommendations. To demonstrate the effectiveness of our proposed model, we also report on the conducted experiments on three real data sets. The results demonstrated that our model achieves significant improvements in Recall and Mean Reciprocal Rank metrics compared with other baselines.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 466-478 (13 pages)

Journal (Volume, Issue Number)

Big Data (Volume 10, Issue 5)

Publication milestones

  • Published - 14/10/2022

Publication status

Published - 14/10/2022

ISSN

2167-6461

External Publication IDs

  • handle.net: 10547/626423
  • Scopus: 85140275499

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