Recommender System

Recommender systems are one of the most used tools for knowledge discovery in databases, and they have become extremely popular in recent years. These systems have been applied in many internet-based communities and businesses to make personalized recommendations and acquire higher profits. Core entities in recommender systems are ratings given by users to items. However, there is much additional information which using it can result in better performance. The personality of each user is one of the most useful data that can help the system produce more accurate and suitable recommendations for active users. It is noteworthy that the characteristics of a person can directly affect his/her behavior. Therefore, in this paper, the personality of users is identified, and a novel mathematical and algorithmic approach is proposed in order to utilize this information for making suitable recommendations. The base model in our proposed approach is matrix factorization, which is one of the most powerful methods in model-based recommender systems. Experimental results on MovieLens dataset demonstrate the positive impact of using personality information in the matrix factorization technique, and also reveal better performance by comparing them with the state-of-the-art algorithms.

Abstract We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.