research-article

Disentangling ID and Modality Effects for Session-based Recommendation

Authors:

Fenglong MaAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1883 - 1892

https://doi.org/10.1145/3626772.3657748

Published: 11 July 2024 Publication History

Abstract

Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented by item modalities (e.g., text and images). However, existing methods typically entangle these causes, leading to their failure in achieving accurate and explainable recommendations. To this end, we propose a novel framework DIMO to disentangle the effects of ID and modality in the task. DIMO aims to disentangle these causes at both item and session levels. At the item level, we introduce a co-occurrence representation schema to explicitly incorporate co-occurrence patterns into ID representations. Simultaneously, DIMO aligns different modalities into a unified semantic space to represent them uniformly. At the session level, we present a multi-view self-supervised disentanglement, including proxy mechanism and counterfactual inference, to disentangle ID and modality effects without supervised signals. Leveraging these disentangled causes, DIMO provides recommendations via causal inference and further creates two templates for generating explanations. Extensive experiments on multiple real-world datasets demonstrate the consistent superiority of DIMO over existing methods. Further analysis also confirms DIMO's effectiveness in generating explanations.

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Cited By

Li XZhou JQu CZhang D(2024)Session-based Recommendation Based on Long-term and Short-term Interest Incorporating Social InformationJournal of Computing and Electronic Information Management10.54097/ee4j9y9m13:3(33-37)Online publication date: 29-Jul-2024
https://doi.org/10.54097/ee4j9y9m

Index Terms

Disentangling ID and Modality Effects for Session-based Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

Copyright � 2024 ACM.

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Publication History

Published: 11 July 2024

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Natural Science Foundation of China

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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

Washington DC, USA

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Cited By

Li XZhou JQu CZhang D(2024)Session-based Recommendation Based on Long-term and Short-term Interest Incorporating Social InformationJournal of Computing and Electronic Information Management10.54097/ee4j9y9m13:3(33-37)Online publication date: 29-Jul-2024
https://doi.org/10.54097/ee4j9y9m

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