short-paper

Attribute-enhanced Dual Channel Representation Learning for Session-based Recommendation

Authors:

Zhiying DengAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 3793 - 3797

https://doi.org/10.1145/3583780.3615245

Published: 21 October 2023 Publication History

Abstract

Session-based recommendation (SBR) aims to predict the anonymous user's next-click items by modeling the short-term sequence pattern. As most existing SBR models generally generate item representations based only on information propagation over the short sequence while ignoring additional valuable knowledge, their expressive abilities are somewhat limited by data sparsity caused by short sequence. Though there have been some attempts on utilizing items' attributes, they basically embed attributes into items directly, ignoring the fact that 1) there is no contextual relationship among attributes; and 2) users have varying levels of attention to different attributes, which still leads to unsatisfactory performance. To tackle the issues, we propose a novel Attribute-enhanced Dual Channel Representation Learning (ADRL) model for SBR, in which we independently model session representations in attribute-related pattern and sequence-related pattern. Specifically, we learn session representations with sequence patterns from the session graph, and we further design an frequency-driven attribute aggregator to generate the attribute-related session representations within a session. The proposed attribute aggregator is plug-and-play, as it can be coupled with most existing SBR models. Extensive experiments on three real-world public datasets demonstrate the superiority of the proposed ADRL over several state-of-the-art baselines, as well as the effectiveness and efficiency of our attribute aggregator module.

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Cited By

Sun ZLiu HQu XFeng KWang YOng YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Models for Intent-Driven Session RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657688(324-334)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657688

Index Terms

Attribute-enhanced Dual Channel Representation Learning for Session-based Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

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Cited By

Sun ZLiu HQu XFeng KWang YOng YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Models for Intent-Driven Session RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657688(324-334)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657688

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