Quantifying predictability of sequential recommendation via logical constraints

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 sequenti...

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Vydáno v:Frontiers of Computer Science Ročník 17; číslo 5; s. 175612
Hlavní autoři: XU, En, YU, Zhiwen, LI, Nuo, CUI, Helei, YAO, Lina, GUO, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Beijing Higher Education Press 01.10.2023
Springer Nature B.V
Témata:
Algorithms
Behavior
Computer Science
Datasets
Human motion
information theory
predictability
Recommender systems
Research Article
sequential recommendation
predictability
sequential recommendation
information theory
ISSN:2095-2228, 2095-2236
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Shrnutí: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.
Bibliografie:information theory
Document received on :2022-04-14
predictability
Document accepted on :2022-07-20
sequential recommendation
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ISSN:2095-2228
2095-2236
DOI:10.1007/s11704-022-2223-1