An overlapping clustering approach for precision, diversity and novelty-aware recommendations

•Scalability is improved using an overlapped clustering.•Clusters of diverse and similar members are identified using a genetic algorithm.•Recommendation relevance and diversity trade-off is controlled using a new encoding.•Our approach improves accuracy, coverage, and novelty of recommendations.•Experimentations on real-world datasets. Recommender systems aim to provide users with recommendations of quality. New evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of applications, including the ecommerce, due to their ability to improve online revenues. High recommendation diversity allows a higher chance to satisfy the users’ needs. However, in a large market of users and products, the scalability of the system is questionable because of the required computing resources. We present a scalable evolutionary clustering algorithm that allows to target two objectives. The proposed solution balances between the recommendation accuracy and coverage by making an overlapped clustering. In our approach, we use a Genetic Algorithm to assign each user to a main cluster from which he gets his recommendations and to secondary clusters as a candidate neighbor. The performance comparison of our algorithm against classic well-known approaches, such as k-NN based Collaborative Filtering, showed a significant improvement.