research-article

Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations

Authors:

Stefan Hagen Weber,

Mark Christoph Müller,

Iryna GurevychAuthors Info & Claims

TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion

Pages 57 - 64

https://doi.org/10.1145/1651461.1651473

Published: 06 November 2009 Publication History

Abstract

In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.

References

[1]

S. Aciar, D. Zhang, S. Simoff, and J. Debenham. Informed recommender: Basing recommendations on consumer product reviews. IEEE Intelligent Systems, 22(3):39--47, May/June 2007.

Digital Library

[2]

N. Bhatti and S. H. Weber. Handbook of Research on Social Dimensions of Semantic Technologies and Web Services, chapter Semantic Visualization to Support Knowledge Discovery in Multi-Relational Service Communities, pages 281--303. IGI Global, 2009.

[3]

S. Blair-Goldensohn, K. Hannan, R. McDonald, T. Neylon, G. Reis, and J. Reynar. Building a sentiment summarizer for local service reviews. In Proceedings of the WWW2008 Workshop: NLP in the Information Explosion Era, Beijing, China, April 2008.

[4]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, March 2003.

Digital Library

[5]

J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 43--52, Madison, WI, USA, 1998.

Digital Library

[6]

X. Cheng and F. Xu. Fine-grained opinion topic and polarity identification. In Proceedings of LREC, pages 2710--2714, Marrekech, Morocco, May 2008.

[7]

T. Fawcett. Roc graphs: Notes and practical considerations for researchers. Technical report, HP Laboratories, 2004.

[8]

L. Ferreira, N. Jakob, and I. Gurevych. A comparative study of feature extraction algorithms in customer reviews. In Proceedings of ICSC, pages 144--151, Santa Clara, CA, USA, August 2008.

Digital Library

[9]

E. Gabrilovich and S. Markovitch. Computing semantic relatedness using wikipedia-based explicit semantic analysis. In Proceedings of IJCAI, pages 1606--1611, Hyderabad, India, January 2007.

Digital Library

[10]

M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger. Pulse: Mining customer opinions from free text. In Advances in Intelligent Data Analysis VI, volume 3646 of Lecture Notes in Computer Science, pages 121--132. Springer Berlin, August 2005.

Digital Library

[11]

J. L. Herlocker, J. A. Konstan, L. G. Terveen, John, and T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22:5--53, 2004.

Digital Library

[12]

M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of KDD-04, pages 168--177, Seattle, WA, USA, August 2004.

Digital Library

[13]

S.-M. Kim and E. Hovy. Extracting opinions, opinion holders, and topics expressed in online news media text. In Proceedings of the ACL Workshop on Sentiment and Subjectivity in Text, pages 1--8, Sydney, Australia, July 2006.

Digital Library

[14]

C. Lippert, S. H. Weber, Y. Huang, V. Tresp, M. Schubert, and H.-P. Kriegel. Relation-prediction in multi-relational domains using matrix-factorization. In Proceedings of the NIPS 2008 Workshop: Structured Input - Structured Output, Vancouver, Canada, December 2008.

[15]

B. Long, X. Wu, Z. M. Zhang, and P. S. Yu. Unsupervised learning on k-partite graphs. In Proceedings of the KDD 2006, pages 317--326, Philadelphia, PA, USA, 2006.

Digital Library

[16]

B. Long, Z. M. Zhang, X. Wu, and P. S. Yu. Spectral clustering for multi-type relational data. In Proceedings of the ICML 2006, pages 585--592, Pittsburgh, PA, USA, 2006.

Digital Library

[17]

Y. Lu and C. Zhai. Opinion integration through semi-supervised topic modeling. In Proceedings of WWW'08, pages 121--130, Beijing, China, April 2008.

Digital Library

[18]

A.-M. Popescu and O. Etzioni. Extracting product features and opinions from reviews. In Proceedings of HLT and EMNLP, pages 339--346, Vancouver, Canada, October 2005.

Digital Library

[19]

J. B. Schafer, D. Frankowski, J. L. Herlocker, and S. Sen. Collaborative filtering recommender systems. In The Adaptive Web, volume 4321 of Lecture Notes in Computer Science, pages 291--324. Springer, 2007.

Digital Library

[20]

N. Srebro, J. D. M. Rennie, and T. S. Jaakkola. Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems 17, pages 1329--1336, 2005.

[21]

G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. On the gravity recommendation system. In Proceedings of KDD Cup Workshop at SIGKDD'07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 22--30, San Jose, CA, USA, 2007.

[22]

I. Titov and R. McDonald. Modeling online reviews with multi-grain topic models. In Proceedings of WWW ’08, pages 111--120, Beijing, China, April 2008.

Digital Library

[23]

T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT and EMNLP, pages 347--354, Vancouver, Canada, October 2005.

Digital Library

[24]

J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of ICDM, pages 427--434, Melbourne, FL, USA, December 2003.

Digital Library

[25]

K. Yu, S. Yu, and V. Tresp. Multi-label informed latent semantic indexing. In SIGIR'05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 258--265, Salvador, Brazil, 2005. ACM Press.

Digital Library

[26]

L. Zhuang, F. Jing, and X.-Y. Zhu. Movie review mining and summarization. In Proceedings of CIKM, pages 43--50, Arlington, VA, USA, November 2006.

Digital Library

Cited By

Majhi SSahani PPradhan RAcharya BGrivokostopoulou FKanavos AGerogiannis V(2024)Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786717(1-8)Online publication date: 17-Jul-2024
https://doi.org/10.1109/IISA62523.2024.10786717
Wang Z(2024)Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced RepresentationsIEEE Access10.1109/ACCESS.2024.336802712(29144-29153)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368027
Xiao BXie XYang CWang Y(2022)RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network RecommendationIEEE Access10.1109/ACCESS.2022.321888210(114165-114177)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3218882
Show More Cited By

Index Terms

Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

Recommendations

Collaborative Filtering with Aspect-Based Opinion Mining: A Tensor Factorization Approach
ICDM '12: Proceedings of the 2012 IEEE 12th International Conference on Data Mining

Collaborative filtering (CF) aims to produce user specific recommendations based on other users' ratings of items. Most existing CF methods rely only on users' overall ratings of items, ignoring the variety of opinions users may have towards different ...
A new similarity function for selecting neighbors for each target item in collaborative filtering

As one of the collaborative filtering (CF) techniques, memory-based CF technique which recommends items to users based on rating information of like-minded users (called neighbors) has been widely used and has also proven to be useful in many practices ...
Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering
Abstract
Nowadays e-commerce has spread all over the world. The e-shops are not similar to the physical shops. The e-shops can have hundreds or thousands of items independent of physical boundaries. The information about all these products is available on ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion

November 2009

94 pages

ISBN:9781605588056

DOI:10.1145/1651461

General Chairs:
Maojin Jiang
Illinois Institute of Technology, USA
,
Bei Yu
Syracuse University, USA
,
Program Chair:
Bei Yu
Syracuse University, USA

Copyright © 2009 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '09

Sponsor:

CIKM '09: Conference on Information and Knowledge Management

November 6, 2009

Hong Kong, China

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

88
Total Citations
View Citations
1,299
Total Downloads

Downloads (Last 12 months)38
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Majhi SSahani PPradhan RAcharya BGrivokostopoulou FKanavos AGerogiannis V(2024)Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786717(1-8)Online publication date: 17-Jul-2024
https://doi.org/10.1109/IISA62523.2024.10786717
Wang Z(2024)Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced RepresentationsIEEE Access10.1109/ACCESS.2024.336802712(29144-29153)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368027
Xiao BXie XYang CWang Y(2022)RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network RecommendationIEEE Access10.1109/ACCESS.2022.321888210(114165-114177)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3218882
Shrivastava RSisodia DNagwani NBP U(2022)An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendationsPattern Recognition Letters10.1016/j.patrec.2022.05.003159:C(91-99)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.patrec.2022.05.003
Duan RJiang CJain H(2022)Combining review-based collaborative filtering and matrix factorizationDecision Support Systems10.1016/j.dss.2022.113748156:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.dss.2022.113748
Moqurrab SAnjum ATariq NSrivastava G(2022)An efficient framework for semantically-correlated term detection and sanitization in clinical documentsComputers and Electrical Engineering10.1016/j.compeleceng.2022.107985100:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.compeleceng.2022.107985
Hassan FSanchez DDomingo-Ferrer J(2021)Utility-Preserving Privacy Protection of Textual Documents via Word EmbeddingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3076632(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3076632
Sadiq SUmer MUllah SMirjalili SRupapara VNappi M(2021)Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learningExpert Systems with Applications10.1016/j.eswa.2021.115111181(115111)Online publication date: Nov-2021
https://doi.org/10.1016/j.eswa.2021.115111
Bathla GSingh PKumar SVerma MGarg DKotecha K(2021)Recop: fine-grained opinions and sentiments-based recommender system for industry 5.0Soft Computing10.1007/s00500-021-06590-827:7(4051-4060)Online publication date: 1-Dec-2021
https://doi.org/10.1007/s00500-021-06590-8
Shafiul Alam Forhad MArefin M(2021)Developing a Framework for Generating Hotel RecommendationProgress in Advanced Computing and Intelligent Engineering10.1007/978-981-33-4299-6_21(255-267)Online publication date: 16-Apr-2021
https://doi.org/10.1007/978-981-33-4299-6_21
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten