Recommendation to Groups of Users Using the Singularities Concept (original) (raw)

Recommendation to a group of users is a big challenge for collaborative filtering. The recommendations to groups of users arise from the convenience of being able to recommend a group of users about products or services that satisfy the entire group. In this paper, we propose the similarity measure SMGU, tailored for collaborative filtering recommendations to groups of users. This similarity measure combines both numerical and non-numerical information. Numerical information is weighted attending to the rating singularity of the group members. This paper focuses on the assumption that the singularity of the ratings cast by the users of the group is relevant information for finding suitable neighbors. For each item, we consider that a rating is singular for a group or for a user when that rating is different from the majority of the rating cast by the other users. Non-numerical structural information can be considered as valuable to match group preferences with neighbors preferences. Experiments have been run using open recommender systems data sets. Compared with representative baselines, results show accuracy improvements when the proposed method is used. Additionally, this paper provides a section devoted to the experiments reproducibility issue. Finally, this paper opens opportunities to face new challenges in the recommendation to a group of users: explanation of recommendations, determination of reliability measures, and improvement of accuracy, novelty, and diversity results. INDEX TERMS Recommendation to groups, group of users, collaborative filtering, recommender systems, singularity. I. INTRODUCTION This section is divided into three subsections: 1) Fundamental concepts of RS: recommendation to individual users, 2) Recommendation to groups of users: Objectives and particularities, and 3) General explanation and motivation of the proposed method for recommending to groups of users. A. RECOMMENDATIONS TO INDIVIDUAL USERS Recommender Systems (RS) [1], [2] allow to mitigate part of the Internet information overload problem. From the point of view of an RS user, based on his past preferences, the system automatically recommends a series of items (movies, books, music, electronics, clothing, etc.) that are available and that the user has not consumed. The RS can make recommendations based on various types of information sources; the most common ones are: content-based, demographic, collaborative, social, and context-aware.