research-article

Double Attention Convolutional Neural Network for Sequential Recommendation

Authors:

Deqing ZouAuthors Info & Claims

ACM Transactions on the Web, Volume 16, Issue 4

Article No.: 19, Pages 1 - 23

https://doi.org/10.1145/3555350

Published: 08 December 2022 Publication History

Abstract

The explosive growth of e-commerce and online service has led to the development of recommender system. Aiming to provide a list of items to meet a user’s personalized need by analyzing his/her interaction history, recommender system has been widely studied in academic and industrial communities. Different from conventional recommender systems, sequential recommender systems attempt to capture the pattern of users’ sequential behaviors and the evolution of users’ preferences. Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence. An item interaction sequence also contains rich contextual information for capturing the item’s dynamic characteristic, since an item’s dynamic characteristic can be reflected by the users who interact with it in a period. Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics. Hence, we propose a novel Double Attention Convolution Neural Network (DACNN), which incorporates user interaction sequence and item interaction sequence into an integrated neural network framework. DACNN leverages the strength of attention mechanism to capture the temporary suitability and adopts CNN to extract local sequential features. Experimental evaluations on the real datasets show that DACNN outperforms the baseline approaches.

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Cited By

Prakash TAbhinav Gnanakumar GMuniyandy E(2025)DACNN based predicting tensile strength of friction stir welded aluminium alloy jointsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02494-9Online publication date: 12-Jan-2025
https://doi.org/10.1007/s13042-024-02494-9
Tang XChen LShi HLyu D(2024)DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge GraphsACM Transactions on Information Systems10.1145/365301542:5(1-23)Online publication date: 29-Apr-2024
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Liu YWalder CXie LLiu YBaeza-Yates RBonchi F(2024)Probabilistic Attention for Sequential RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671733(1956-1967)Online publication date: 25-Aug-2024
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Index Terms

Double Attention Convolutional Neural Network for Sequential Recommendation
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 16, Issue 4

November 2022

165 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3571715

Editor:
Ryen White
Microsoft Research, USA

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Publication History

Published: 08 December 2022

Online AM: 09 August 2022

Accepted: 08 July 2022

Revised: 17 June 2022

Received: 11 July 2021

Published in TWEB Volume 16, Issue 4

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Cited By

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https://doi.org/10.1007/s13042-024-02494-9
Tang XChen LShi HLyu D(2024)DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge GraphsACM Transactions on Information Systems10.1145/365301542:5(1-23)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653015
Liu YWalder CXie LLiu YBaeza-Yates RBonchi F(2024)Probabilistic Attention for Sequential RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671733(1956-1967)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671733
Han DHuang YLiu JLiao KLin K(2024)LSAB: User Behavioral Pattern Modeling in Sequential Recommendation by Learning Self-Attention BiasACM Transactions on Knowledge Discovery from Data10.1145/363262518:3(1-20)Online publication date: 13-Jan-2024
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Wu HZhang YMa CLyu FHe BMitra BLiu X(2024)Result Diversification in Search and Recommendation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338226236:10(5354-5373)Online publication date: 1-Oct-2024
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