New Similarity Measures for Item-based Neighborhood Collaborative Filtering

Similarity measures play an important role in many areas to solve a wide variety of problems. In computer science, these measures are used in decision making, information retrieval, data mining, machine learning, and recommender systems. The recommender systems are tools that have proven their utility in filtering large amounts of information and giving recommendations useful for users. Neighborhood collaborative filtering is the most common recommender system approach implemented by cutting-edge companies. A key element of this approach is the similarity measure, which is used to find neighbors with similar tastes to provide recommendations that satisfy users' needs. A drawback of this approach is the lack of user’s information to generate proper recommendations. For this reason, it is important to design new similarity measures that can find the most relevant neighbors to generate more accurate recommendations for users with little information about them. This paper designs two new similarity measures that can generate good recommendations with little information about users. These similarity measures have been tested using MovieLens datasets and different rating prediction methods, and they have shown a good performance in comparison with other similarity measures designed to address the recommendation problem.