Quantifying predictability of sequential recommendation via logical constraints

En Xu, Zhiwen Yu, Nuo Li, Helei Cui, Lina Yao, Bin Guo

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The sequential recommendation is a compelling technology for predicting users’ next interaction via their historical behaviors. Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation. To this end, we consider applying the popular predictability theory of human movement behavior to this recommendation context. Still, it would incur serious bias in the next moment measurement of the candidate set size, resulting in inaccurate predictability. Therefore, determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations. Here, different from the traditional approach that utilizes topological constraints, we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints. Then, we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior. Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors. Finally, a prediction rate between 64% and 80% has been obtained by testing on five classical datasets in three domains of the recommender system. This provides a guideline to optimize the recommendation algorithm for a given dataset.

Original languageEnglish
Article number175612
JournalFrontiers of Computer Science
Volume17
Issue number5
DOIs
StatePublished - Oct 2023

Keywords

  • information theory
  • predictability
  • sequential recommendation

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