A combination of individual model on memory-based group recommender system to the books domain

Abstract

A recommender system has an important role in decision support by identifying items that will be useful to users in various domains. Development of recommender systems does not only focus on individual users but also groups in various domains. In this paper we present group recommender systems with a collaborative filtering method. Collaborative filtering is a widely used method for recommender systems, easily found in previous studies, that finds similar preferences on the basis of other users' explicit feedback for recommendations. The problem with recommendations for groups using collaborative filtering still is finding the best aggregation technique for group recommender systems. Aggregation techniques in group recommender systems are used to construct an individual model. There are several aggregation techniques that we discuss in this research, such as average, least misery and most happiness. Observations of the recommendation give us the conclusion that the use of least-misery techniques results in the best recommendations according to F1. Furthermore, giving more weight to group neighborhood can also improve the recommendation.