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Joint Deep Modeling of Users and Items Using Reviews for Recommendation

Authors:

Philip S. YuAuthors Info & Claims

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Pages 425 - 434

https://doi.org/10.1145/3018661.3018665

Published: 02 February 2017 Publication History

Abstract

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.

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Cited By

Alrashidi MSelamat AIbrahim RFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://doi.org/10.4018/JCIT.335524
Kim JLi XJin LLi QKim J(2024)Attentive Review Semantics-Aware Recommendation Model for Rating PredictionElectronics10.3390/electronics1314281513:14(2815)Online publication date: 17-Jul-2024
https://doi.org/10.3390/electronics13142815
Cui YZhou PYu HSun PCao HYang P(2024)ASKAT: Aspect Sentiment Knowledge Graph Attention Network for RecommendationElectronics10.3390/electronics1301021613:1(216)Online publication date: 3-Jan-2024
https://doi.org/10.3390/electronics13010216
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Index Terms

Joint Deep Modeling of Users and Items Using Reviews for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

February 2017

868 pages

ISBN:9781450346757

DOI:10.1145/3018661

General Chairs:
Maarten de Rijke
University of Amsterdam
,
Milad Shokouhi
Microsoft
,
Program Chairs:
Andrew Tomkins
Google
,
Min Zhang
Tsinghua University

Copyright © 2017 ACM.

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 ACM 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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Published: 02 February 2017

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National Science Foundation

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WSDM 2017

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WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining

February 6 - 10, 2017

Cambridge, United Kingdom

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WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Alrashidi MSelamat AIbrahim RFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://doi.org/10.4018/JCIT.335524
Kim JLi XJin LLi QKim J(2024)Attentive Review Semantics-Aware Recommendation Model for Rating PredictionElectronics10.3390/electronics1314281513:14(2815)Online publication date: 17-Jul-2024
https://doi.org/10.3390/electronics13142815
Cui YZhou PYu HSun PCao HYang P(2024)ASKAT: Aspect Sentiment Knowledge Graph Attention Network for RecommendationElectronics10.3390/electronics1301021613:1(216)Online publication date: 3-Jan-2024
https://doi.org/10.3390/electronics13010216
Abdulmunim TTao XZhang JYong JYong JZhou X(2024)Movie recommendation and classification system using block chainWeb Intelligence10.3233/WEB-230346(1-22)Online publication date: 10-May-2024
https://doi.org/10.3233/WEB-230346
Lin WZhu MZhou XZhang RZhao XShen SSun L(2024)A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration ApplicationsTsinghua Science and Technology10.26599/TST.2023.901005029:3(897-910)Online publication date: Jun-2024
https://doi.org/10.26599/TST.2023.9010050
Guo SLiao XMeng FZhao QTang YLi HZong Q(2024)FSASA: Sequential recommendation based on fusing session-aware models and self-attention networksComputer Science and Information Systems10.2298/CSIS230522067G21:1(1-20)Online publication date: 2024
https://doi.org/10.2298/CSIS230522067G
Ye FLu XLi HChen Z(2024)Transfer learning from rating prediction to Top-k recommendationPLOS ONE10.1371/journal.pone.030024019:3(e0300240)Online publication date: 28-Mar-2024
https://doi.org/10.1371/journal.pone.0300240
刘世(2024)Multi-Granularity Attention Mechanism for Recommendation Systems Based on Item Descriptions and ReviewsModeling and Simulation10.12677/mos.2024.13322213:03(2429-2440)Online publication date: 2024
https://doi.org/10.12677/mos.2024.133222
Qiu ZCui Y(2024)Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed LearningSage Open10.1177/2158244024124198114:2Online publication date: 23-Apr-2024
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Malitesta DCornacchia GPomo CMerra FDi Noia TDi Sciascio E(2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
https://doi.org/10.1145/3662738
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