research-article

Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)

Authors:

Alexander J. Smola,

Chong WangAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 193 - 202

https://doi.org/10.1145/2623330.2623758

Published: 24 August 2014 Publication History

Abstract

Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings.

The ability to answer questions such as "Does this user care more about the plot or about the special effects?" or "What is the quality of the movie in terms of acting?" helps us to understand why certain ratings are generated. This can be used to provide more meaningful recommendations.

In this work we propose a probabilistic model based on collaborative filtering and topic modeling. It allows us to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on a per-aspect basis. Unlike prior work our approach is entirely unsupervised and does not require knowledge of the aspect specific ratings or genres for inference.

We evaluate our model on a live copy crawled from IMDb. Our model offers superior performance by joint modeling. Moreover, we are able to address the cold start problem -- by utilizing the information inherent in reviews our model demonstrates improvement for new users and movies.

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Cited By

Chen LMankad S(2024)A Structural Topic and Sentiment-Discourse Model for Text AnalysisSSRN Electronic Journal10.2139/ssrn.4020651Online publication date: 2024
https://doi.org/10.2139/ssrn.4020651
Lu JSu R(2024)Graph neural network news recommendation based on weight learning and preference decompositionJournal of Electronic Imaging10.1117/1.JEI.33.1.01100233:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JEI.33.1.011002
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https://doi.org/10.1109/eSmarTA62850.2024.10639015
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Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 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: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.2139/ssrn.4020651
Lu JSu R(2024)Graph neural network news recommendation based on weight learning and preference decompositionJournal of Electronic Imaging10.1117/1.JEI.33.1.01100233:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JEI.33.1.011002
Zubair SAl Sabri MAlmahri F(2024)Development of Content-Based Filtering Model for Recommendation System Using Multiple Factors related to object Preference2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)10.1109/eSmarTA62850.2024.10639015(1-12)Online publication date: 6-Aug-2024
https://doi.org/10.1109/eSmarTA62850.2024.10639015
Wu CChen YYeh Y(2024)A Deep Recommendation Model Considering the Impact of Time and Individual DiversityIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.327263311:2(2558-2569)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3272633
Mukherjee AM U(2024)Resume Ranking and Shortlisting with DistilBERT and XLM2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)10.1109/ICWITE59797.2024.10502523(301-304)Online publication date: 16-Feb-2024
https://doi.org/10.1109/ICWITE59797.2024.10502523
Ou WHuynh V(2024)Aspect-level Item Recommendation Based on User Reviews with Variational AutoencodersInformation Sciences10.1016/j.ins.2024.120655(120655)Online publication date: Apr-2024
https://doi.org/10.1016/j.ins.2024.120655
Sun GCheng YZhang ZTong XChai T(2024)Text classification with improved word embedding and adaptive segmentationExpert Systems with Applications10.1016/j.eswa.2023.121852238(121852)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121852
Gheewala SXu SYeom S(2024)Deep shared learning and attentive domain mapping for cross-domain recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-024-09416-yOnline publication date: 27-Sep-2024
https://doi.org/10.1007/s11257-024-09416-y
Yan HKong LGui LChi YXing EHe YZhang KOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Counterfactual generation with identifiability guaranteesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668576(56256-56277)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668576
Chopra ADixit V(2023)Detecting biased user-product ratings for online products using opinion miningJournal of Intelligent Systems10.1515/jisys-2022-903032:1Online publication date: 26-Jan-2023
https://doi.org/10.1515/jisys-2022-9030
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