Pairwise Preference Regression on Movie Recommendation System

  • Rita Rismala Universitas Telkom
  • Rudy Prabowo Universitas Telkom
  • Agung Toto Wibowo Universitas Telkom
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Abstract

Recommendation System is able to help users to choose items, including movies, that match their interests. One of the problems faced by recommendation system is cold-start problem. Cold start problem can be categorized into three types, they are: recommending existed item for new user, recommending new item for existed user, and recommending new item for new user. Pairwise preference regression is a method that directly deals with cold-start problem. This method can suggest a recommendation, not only for users who have no historical rating, but also for those who only have less demographic info. From the experiment result, the best score of Normalized Discounted Cumulative Gain (nDGC) from the system is 0.8484. The standard deviation of rating resulted by the recommendation system is 1.24, the average is 3.82. Consequently, the distribution of recommendation result is around rating 5 to 3. Those results mean that this recommendation system is good to solving cold-start problem in movie recommendation system.

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References

Ricci, Francesco, Lior Rokach, and Bracha Shapira. Introduction to recommender systems handbook. Springer US, 2011.

Adomavicius, Gediminas, and Alexander Tuzhilin. "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions." Knowledge and Data Engineering, IEEE Transactions on 17.6 (2005): 734-749.

Park, Seung-Taek, and Wei Chu. "Pairwise preference regression for cold-start recommendation." Proceedings of the third ACM conference on Recommender systems. ACM, 2009.

Pazzani, Michael J. "A framework for collaborative, content-based and demographic filtering." Artificial Intelligence Review 13.5-6 (1999): 393-408.

Basilico, Justin, and Thomas Hofmann. "A joint framework for collaborative and content filtering." Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2004.

Billsus, Daniel, et al. "Adaptive interfaces for ubiquitous web access."Communications of the ACM 45.5 (2002): 34-38.

Järvelin, Kalervo, and Jaana Kekäläinen. "IR evaluation methods for retrieving highly relevant documents." Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2000.

Published
2019-03-22
How to Cite
Rismala, R., Prabowo, R., & Wibowo, A. T. (2019). Pairwise Preference Regression on Movie Recommendation System. Indonesian Journal on Computing (Indo-JC), 4(1), 57-64. https://doi.org/10.21108/INDOJC.2019.4.1.255
Section
Computer Science