research-article

H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation

Authors:

Xi ZhuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 3

Article No.: 63, Pages 1 - 30

https://doi.org/10.1145/3630002

Published: 30 December 2023 Publication History

Abstract

Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each session consists of a series of items, and each user owns a series of sessions. However, the existing PSBR methods can merely capture the pairwise relation information within items and users. To effectively capture the hierarchical information, we propose a novel hierarchical hypergraph neural network to model the hierarchical architecture. Moreover, considering that the items in sessions are sequentially ordered, while the hypergraph can only model the set relation, we propose a directed graph aggregator (DGA) to aggregate the sequential information from the directed global item graph. By attentively combining the embeddings of the above two modules, we propose a framework dubbed H3GNN (Hybrid Hierarchical HyperGraph Neural Network). Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed model compared to the state-of-the-art methods, and ablation experiment results validate the effectiveness of all the proposed components.

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Cited By

Sun JZhu JWang CWang YNiu T(2025)Dual channel representation-learning with dynamic intent aggregation for session-based recommendationExpert Systems with Applications10.1016/j.eswa.2024.125273259(125273)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125273

Index Terms

H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 3

May 2024

721 pages

EISSN:1558-2868

DOI:10.1145/3618081

Editor:
Min Zhang
Tsinghua University, China

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 December 2023

Online AM: 23 October 2023

Accepted: 26 September 2023

Revised: 05 August 2023

Received: 23 August 2022

Published in TOIS Volume 42, Issue 3

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National Natural Science Foundation of China (NSFC)
Guangzhou Municipal Nansha District Science and Technology Bureau

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Cited By

Sun JZhu JWang CWang YNiu T(2025)Dual channel representation-learning with dynamic intent aggregation for session-based recommendationExpert Systems with Applications10.1016/j.eswa.2024.125273259(125273)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125273

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