Pairwise Preference Regression on Movie Recommendation System

• Published in: Indonesian journal on computing Vol. 4; no. 1; p. 57
• Main Authors: Rismala, Rita, Prabowo, Rudy, Wibowo, Agung Toto
• Format: Journal Article
• Language: English
• Published: 22.03.2019
• ISSN: 2460-9234; 2460-9056
• DOI: 10.21108/INDOJC.2019.4.1.255

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.