A hybrid recommendation system based on profile expansion technique to alleviate cold start problem
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A hybrid recommendation system based on profile expansion technique to alleviate cold start problem Faryad Tahmasebi 1 & Majid Meghdadi 1 & Sajad Ahmadian 2 & Khashayar Valiallahi 3 Received: 21 May 2019 / Revised: 19 August 2020 / Accepted: 28 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Recommender systems . Collaborative filtering . Cold start . Profile expansion . Demographic data
1 Introduction In recent years, recommender systems are used in different domains particularly in the scope of commercial web sites. The most important task of recommender system is to estimate user’s
* Majid Meghdadi [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
interests about items in the system. To this end, the opinions of users are acquired explicitly or implicitly to find relevant items for recommending to the users. In addition, these systems can use additional information resources such as demographic data and social information [2, 6, 22]. Therefore, these systems help users to find their interest items and prevent from the wasting of user’s time to obtain their relevant information [7, 16, 33, 37]. Collaborative filtering [20, 21, 25, 30] has become one of the most popular techniques which is used in recommender systems. This approach recommends the relevant items t
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