Rene Theuerkauf
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.
- Daniel Billsus and Michael J. Pazzani. 2000. User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction 10, 2–3, 147–180. DOI: https://doi.org/10.1023/A:1026501525781.Google Scholar
Digital Library
- Erion Çano and Maurizio Morisio. 2017. Hybrid recommender systems: A systematic literature review. IDA 21, 6, 1487–1524. DOI: https://doi.org/10.3233/IDA-163209.Google ScholarDigital Library
- 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. DOI: https://doi.org/10.1016/j.ins.2017.09.050.Google ScholarDigital Library
- 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.Google Scholar
- 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).Google Scholar
- 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. DOI: https://doi.org/10.1145/138859.138867.Google ScholarDigital Library
- Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4. DOI: https://doi.org/10.1145/2843948.Google ScholarDigital Library
- F. M. Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4. DOI: https://doi.org/10.1145/2827872.Google ScholarDigital Library
- 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. DOI: https://doi.org/10.1145/963770.963772.Google ScholarDigital Library
- Alan R. Hevner, Salvatore T. March, Jinsoo Park, and Sudha Ram. 2004. Design Science in Information Systems Research. MIS Q 28, 1, 75–105.Google ScholarCross Ref
- 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. DOI: https://doi.org/10.1007/s12599-010-0092-x.Google ScholarCross Ref
- 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.Google Scholar
- 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. DOI: https://doi.org/10.1145/2043932.2043984.Google ScholarDigital Library
- Yehuda Koren. 2010. Factor in the neighbors. ACM Trans. Knowl. Discov. Data 4, 1, 1–24. DOI: https://doi.org/10.1145/1644873.1644874.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Paul R. Milgrom and Robert J. Weber. 1982. A Theory of Auctions and Competitive Bidding. Econometrica 50, 5, 1089–1122.Google ScholarCross Ref
- 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. DOI: https://doi.org/10.1016/j.eswa.2017.12.020.Google ScholarCross Ref
- 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. DOI: https://doi.org/10.1007/978-1-4899-7637-6_1.Google ScholarCross Ref
- Paul A. Samuelson. 1937. A Note on Measurement of Utility. The Review of Economic Studies 4, 2, 155. DOI: https://doi.org/10.2307/2967612.Google ScholarCross Ref
- Dipanjan Sarkar, Raghav Bali, and Tushar Sharma. 2018. Practical Machine Learning with Python. Apress, Berkeley, CA.Google Scholar
- 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.Google Scholar
- Barry Schwartz. 2007. The paradox of choice. Why more is less. HarperCollins e-books, Pymble, NSW, New York.Google Scholar
- 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. DOI: https://doi.org/10.1145/3409929.3414744.Google ScholarDigital Library
- 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/.Google Scholar
- Gal Zauberman and Oleg Urminsky. 2016. Consumer intertemporal preferences. Current Opinion in Psychology 10, 136–141. DOI: https://doi.org/10.1016/j.copsyc.2016.01.005.Google ScholarCross Ref
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System. ACM Comput. Surv. 52, 1, 1–38. DOI: https://doi.org/10.1145/3285029.Google ScholarDigital Library
Recommendations
Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis
Advances in Social MediaThe motivation of collaborative filtering CF comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we ...
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
The role of sentiment analysis in a recommender system: a systematic survey
Currently, fields like e-commerce, education, social media, tourism, and the entertainment industry rely on recommender systems to provide personalised services to their clients. The most common and widely accepted technique - collaborative filtering, ...
Comments