Abstract
Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.
Highlights
Recommender systems have become an essential part in many e-commerce sites, entertainment sites, tourism applications and others to address the problem of information overload [1,2]
As the objective of this work is to provide recommender systems with methods that display information about the experiences of other users along with the presentation of recommended products, the method should work with whichever collaborative filtering (CF) technique is currently being used on the recommender platform
singular value decomposition (SVD) achieved the lowest mean square error (MSE) value, as well as a relatively good FScore, Recall and Precision, which shows that the matrix factorization in our data set benefited the CF results
Summary
Recommender systems have become an essential part in many e-commerce sites, entertainment sites, tourism applications and others to address the problem of information overload [1,2]. These systems automatically present products, services, content or information that may be of interest to users [1,2,3,4]. Machine learning methods are used to process the huge preference matrices These methods operate as black boxes that generally receive numbers as input information and output other numbers that represent predictions of preferences on the items to be recommended [3,5]
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