research-article

Improving Transformer-based Sequential Recommenders through Preference Editing

Authors:

Huasheng Liang,

Maarten De RijkeAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 71, Pages 1 - 24

https://doi.org/10.1145/3564282

Published: 10 April 2023 Publication History

Abstract

One of the key challenges in sequential recommendation is how to extract and represent user preferences. Traditional methods rely solely on predicting the next item. But user behavior may be driven by complex preferences. Therefore, these methods cannot make accurate recommendations when the available information user behavior is limited. To explore multiple user preferences, we propose a transformer-based sequential recommendation model, named MrTransformer (Multi-preference Transformer). For training MrTransformer, we devise a preference-editing-based self-supervised learning (SSL) mechanism that explores extra supervision signals based on relations with other sequences. The idea is to force the sequential recommendation model to discriminate between common and unique preferences in different sequences of interactions. By doing so, the sequential recommendation model is able to disentangle user preferences into multiple independent preference representations so as to improve user preference extraction and representation.

We carry out extensive experiments on five benchmark datasets. MrTransformer with preference editing significantly outperforms state-of-the-art sequential recommendation methods in terms of Recall, MRR, and NDCG. We find that long sequences of interactions from which user preferences are harder to extract and represent benefit most from preference editing.

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Index Terms

Improving Transformer-based Sequential Recommenders through Preference Editing
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

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Publication History

Published: 10 April 2023

Online AM: 24 September 2022

Accepted: 30 August 2022

Revised: 28 August 2022

Received: 20 July 2021

Published in TOIS Volume 41, Issue 3

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National Key R&D Program of China
Natural Science Foundation of China
Key Scientific and Technological Innovation Program of Shandong Province
Natural Science Foundation of Shandong Province
Tencent WeChat Rhino-Bird Focused Research Program
Fundamental Research Funds of Shandong University
Hybrid Intelligence Center
Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research

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