Learning Hierarchical Preferences for Recommendation with Mixture Intention Neural Stochastic Processes

Huafeng Liu*, Mingjie Zhou, Mingyang Song, Deqiang Ouyang, Yawen Li, Liping Jing, Jian Yu, Michael K. Ng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)

Abstract

User preferences behind users’ decision-making processes are highly diverse and may range from lower-level concepts with more specific intentions and higher-level concepts with more general intentions. In this case, user preferences tend to be expressed hierarchically. However, learning such intentions with different levels from user behaviors is challenging, and remains largely neglected by the existing literature. Meanwhile, user behavior data tends to be sparse because of the limited user response and the vast combinations of users and items, which results in cold-start problems with unclear user intentions. In this paper, we propose a mixture intention neural stochastic process (MINSP), a new view of the stochastic processes family using a general meta-learning mechanism and mixture strategy for robust recommendation with hierarchical preferences modeling. By considering the recommendation process for each user as a stochastic process, MINSP defines distributions over functions and is capable of rapid adaptation to different users. To capture the user's intention on different levels, an iterative additive algorithm is proposed that minimizes the approximation error by backfitting the residuals of previous approximations. In this case, the induced tree intention hierarchies serve as an aggregated structured representation of the whole preference, summarizing the gist for convenient navigation and better generalization. Furthermore, we theoretically analyze the generalization error bound of the proposed MINSP to guarantee the model performance. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines in terms of recommendation performance, and obtain an interpretable hierarchical intention structure.

Original languageEnglish
Pages (from-to)3237-3251
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume36
Issue number7
Early online date1 Jan 2024
DOIs
Publication statusPublished - Jul 2024

Scopus Subject Areas

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

User-Defined Keywords

  • Hierarchical preferences
  • Recommendation system
  • Stochastic process
  • User preference modeling

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