A Hybrid Recommendation Algorithm to Address the Cold Start Problem

Abstract

In this age where there are a lot of available data, recommender systems are a way to filter the useful information. A recommender system’s purpose is to recommend relevant items to users, and to do that, it requires information on both, data from users and from items, to better organize and categorize both of them. There are several types of recommenders each best suited for a specific purpose, and with specific weaknesses. Then there are hybrid recommenders, made by combining one or more types of recommenders in a way that each type suppresses or at least limits the weaknesses of the other types. A very important weakness of recommender systems occurs when the system doesn’t have enough information about something and so, it cannot make a recommendation. This problem known as a Cold Start problem is addressed in this study. There are two types of Cold Start problems: those where the lack of information comes from a user (User Cold Start) and those where it comes from an item (Item Cold Start). This article’s main focus is on User Cold Start problems. A novel approach is introduced that combines clients’ segmentation with association rules. Although the proposed solution’s average precision is similar to other main Cold Start algorithms it is a simpler approach to most of them.