A hybrid recommendation system based on profile expansion technique to alleviate cold start problem

TLDR: A hybrid recommendation method based on profile expansion technique to alleviate cold start problem in recommender systems and can achieve better performance than the other recommendation methods in terms of accuracy and rate coverage measures.

Abstract: Recommender systems are one of the information filtering tools which can be employed to find interest items of users. Collaborative filtering is one of the recommendation methods to provide suggestions for target users based on the ratings of like-interest users. This method suffers from some shortcomings such as cold start problem leading to reduce the performance of recommender system in predicting unseen items. In this paper, we propose a hybrid recommendation method based on profile expansion technique to alleviate cold start problem in recommender systems. For this purpose, we take into consideration user’s demographic data (e.g. age, gender, and occupation) beside user’s rating data in order to enrich the neighborhood set of users. Specifically, two different strategies are used to enrich the rating profile of users by adding some additional ratings to them. The proposed rating profile expansion mechanism has a significant effect on the performance improvement of recommender systems especially when they are facing with cold start problem. The reason behind this claim is that the proposed mechanism makes a denser user-item rating matrix than the original one by adding some additional ratings to it. Obviously, providing a rating profile with further ratings for the target user leads to alleviate cold start problem in recommender systems. The expanded rating profiles are used to calculate similarity values between users and predict unseen items. The results of experiments demonstrate that the proposed method can achieve better performance than the other recommendation methods in terms of accuracy and rate coverage measures.