skip to main content
10.1145/3466029.3466057acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiceegConference Proceedingsconference-collections
research-article

Improving Recommender Systems by Using Time-Weighted Sentiment Analysis

Published: 27 July 2021 Publication History

Abstract

For users, keeping track of the film industry's ever-growing range of movies and TV series is increasingly difficult and exacerbates the paradox of choice: that too many choices hinder decision-making. As a countermeasure, recommender systems can personalize offers and limit the variety of media to what users find relevant. In this paper, we present an approach to improving the accuracy of a collaborative filtering system that applies auction theory by differentiating data into private versus common value information. Incorporating objective, time-weighted information with reference to discounted utility theory, we tested the approach in a prototype using the MovieLens dataset expanded with movie reviews published in The New York Times. By optimizing the algorithm used, we improved recommendation quality by 0.78% compared with the unmodified collaborative filtering approach. With that novel combination of dataset expansion, auction theory, and discounted utility theory, we have narrowed a particular gap in research on recommender systems.

References

[1]
Daniel Billsus and Michael J. Pazzani. 2000. User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction 10, 2–3, 147–180.
[2]
Erion Çano and Maurizio Morisio. 2017. Hybrid recommender systems: A systematic literature review. IDA 21, 6, 1487–1524.
[3]
Tiago Cunha, Carlos Soares, and André C. de Carvalho. 2018. Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering. Information Sciences 423, 128–144.
[4]
Simon Dooms, Toon de Pessemier, and Luc Martens, Eds. 2013. MovieTweetings: a Movie Rating Dataset Collected From Twitter. Workshop on Crowdsourcing and Human Computation for Recommender Systems.
[5]
Farzad Eskandanian and Bamshad Mobasher. 2018. Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks. Proceedings of Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning (RecSysKTL’18).
[6]
David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM 35, 12, 61–70.
[7]
Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4.
[8]
F. M. Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4.
[9]
Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating Collaborative Filtering Recommender Systems. ACM Trans. Inf. Syst. 22, 1, 5–53.
[10]
Alan R. Hevner, Salvatore T. March, Jinsoo Park, and Sudha Ram. 2004. Design Science in Information Systems Research. MIS Q 28, 1, 75–105.
[11]
Oliver Hinz and Jochen Eckert. 2010. The Impact of Search and Recommendation Systems on Sales in Electronic Commerce. Bus Inf Syst Eng 2, 2, 67–77.
[12]
C. J. Hutto and Eric Gilbert. 2015. VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014.
[13]
Gilad Katz, Nir Ofek, Bracha Shapira, Lior Rokach, and Guy Shani. 2011. Using Wikipedia to Boost Collaborative Filtering Techniques. In Proceedings of the Fifth ACM Conference on Recommender Systems. RecSys ’11. Association for Computing Machinery, New York, NY, USA, 285–288.
[14]
Yehuda Koren. 2010. Factor in the neighbors. ACM Trans. Knowl. Discov. Data 4, 1, 1–24.
[15]
S. Kumar, K. De, and P. P. Roy. 2020. Movie Recommendation System Using Sentiment Analysis From Microblogging Data. IEEE Transactions on Computational Social Systems 7, 4, 915–923.
[16]
Paul R. Milgrom and Robert J. Weber. 1982. A Theory of Auctions and Competitive Bidding. Econometrica 50, 5, 1089–1122.
[17]
Ivens Portugal, Paulo Alencar, and Donald Cowan. 2018. The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications 97, 205–227.
[18]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems: Introduction and Challenges. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach and Bracha Shapira, Eds. Springer US, Boston, MA, 1–34.
[19]
Paul A. Samuelson. 1937. A Note on Measurement of Utility. The Review of Economic Studies 4, 2, 155.
[20]
Dipanjan Sarkar, Raghav Bali, and Tushar Sharma. 2018. Practical Machine Learning with Python. Apress, Berkeley, CA.
[21]
Ingo Schwab, Alfred Kobsa, and Ivan Koychev. 2001. Learning User Interests through Positive Examples Using Content Analysis and Collaborative Filtering. In 30 2001. Internal Memo, GMD.
[22]
Barry Schwartz. 2007. The paradox of choice. Why more is less. HarperCollins e-books, Pymble, NSW, New York.
[23]
Safa Selmene and Zahra Kodia. 2020. Recommender System Based on User's Tweets Sentiment Analysis. International Conference on E-Commerce, E-Business and E-Government, 96–102.
[24]
Amy Watson. 2020. Number of movies released in the United States and Canada from 2000 to 2019 (2020). Retrieved August 20, 2020 from https://www.statista.com/statistics/187122/movie-releases-in-north-america-since-2001/.
[25]
Gal Zauberman and Oleg Urminsky. 2016. Consumer intertemporal preferences. Current Opinion in Psychology 10, 136–141.
[26]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System. ACM Comput. Surv. 52, 1, 1–38.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICEEG '21: Proceedings of the 5th International Conference on E-Commerce, E-Business and E-Government
April 2021
165 pages
ISBN:9781450389495
DOI:10.1145/3466029
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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. MovieLens
  2. collaborative filtering
  3. sentiment analysis
  4. time-dependency
  5. weighted score function

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICEEG '21

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 78
    Total Downloads
  • Downloads (Last 12 months)11
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media