research-article

Neural gaussian mixture model for review-based rating prediction

Authors:

Haofei ZhouAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 113 - 121

https://doi.org/10.1145/3240323.3240353

Published: 27 September 2018 Publication History

Abstract

Review has been proven to be an important information in recommendation. Different from the overall user-item rating matrix, it can provide textual information that exhibits why a user likes an item or not. Recently, more and more researchers have paid attention on review-based rating prediction. There are two challenging issues: how to extract representative features to characterize users / items from reviews and how to leverage them for recommendation system. In this paper, we propose a Neural Gaussian Mixture Model (NGMM) for review-based rating prediction task. Among it, the review textual information is used to construct two parallel neural networks for users and items respectively, so that the users' preferences and items' properties can be sufficiently extracted and represented as two latent vectors. A shared layer is introduced on the top to couple these two networks together and model user-item rating based on the features learned from reviews. Specifically, each rating is modeled via a Gaussian mixture model, where each Gaussian component has zero variance, the mean described by the corresponding component in user's latent vector and the weight indicated by the corresponding component in item's latent vector. Extensive experiments are conducted on five real-world Amazon review datasets. The experimental results have demonstrated that our proposed NGMM model achieves the state-of-the-art performance in review-based rating prediction task.

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Cited By

Bai TWang XZhang ZSong WWu BNie J(2024)GPR-OPT: A Practical Gaussian optimization criterion for implicit recommender systemsInformation Processing & Management10.1016/j.ipm.2023.10352561:1(103525)Online publication date: Jan-2024
https://doi.org/10.1016/j.ipm.2023.103525
Bittencourt GFonseca GAndrade YSilva NRocha L(2023)A Survey on Review - Aware Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617050(198-207)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617050
Khan RMannan AAslam N(2022)Prediction of Product Rating based on Polarized Reviews using Supervised Machine LearningVFAST Transactions on Software Engineering10.21015/vtse.v10i4.102410:4(01-09)Online publication date: 1-Nov-2022
https://doi.org/10.21015/vtse.v10i4.1024
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Index Terms

Neural gaussian mixture model for review-based rating prediction
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Conferences

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 ACM.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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New York, NY, United States

Publication History

Published: 27 September 2018

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SIGCHI

RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Bai TWang XZhang ZSong WWu BNie J(2024)GPR-OPT: A Practical Gaussian optimization criterion for implicit recommender systemsInformation Processing & Management10.1016/j.ipm.2023.10352561:1(103525)Online publication date: Jan-2024
https://doi.org/10.1016/j.ipm.2023.103525
Bittencourt GFonseca GAndrade YSilva NRocha L(2023)A Survey on Review - Aware Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617050(198-207)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617050
Khan RMannan AAslam N(2022)Prediction of Product Rating based on Polarized Reviews using Supervised Machine LearningVFAST Transactions on Software Engineering10.21015/vtse.v10i4.102410:4(01-09)Online publication date: 1-Nov-2022
https://doi.org/10.21015/vtse.v10i4.1024
Wang CGuo ZLi JLi GPan P(2022)A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive RecommendationACM/IMS Transactions on Data Science10.1145/35225962:4(1-25)Online publication date: 17-May-2022
https://dl.acm.org/doi/10.1145/3522596
Liu ZYuan BMa Y(2021)A multi-task dual attention deep recommendation model using ratings and review helpfulnessApplied Intelligence10.1007/s10489-021-02666-yOnline publication date: 17-Aug-2021
https://doi.org/10.1007/s10489-021-02666-y
Neves FCampos FStroele VCapretz MJennings MBryant DDantas M(2020)Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in HealthcareIEEE Access10.1109/ACCESS.2020.30423428(217150-217168)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3042342
Khan ZNiu ZSandiwarno SPrince R(2020)Deep learning techniques for rating prediction: a survey of the state-of-the-artArtificial Intelligence Review10.1007/s10462-020-09892-9Online publication date: 19-Aug-2020
https://doi.org/10.1007/s10462-020-09892-9
Chambua JNiu Z(2020)Review text based rating prediction approaches: preference knowledge learning, representation and utilizationArtificial Intelligence Review10.1007/s10462-020-09873-yOnline publication date: 28-Jul-2020
https://doi.org/10.1007/s10462-020-09873-y
Berkani L(2020)A semantic and social‐based collaborative recommendation of friends in social networksSoftware: Practice and Experience10.1002/spe.282850:8(1498-1519)Online publication date: 3-Apr-2020
https://doi.org/10.1002/spe.2828
Feng XZeng Y(2019)Multi-Level Fine-Grained Interactions for Collaborative FilteringIEEE Access10.1109/ACCESS.2019.29412367(143169-143184)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2941236

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