Prediction on recommender system based on bi-clustering and moth flame optimization

Concerning the problems of weak scalability of traditional collaborative filtering recommender systems, a scalable recommender system based on bi-clustering and moth flame optimization algorithm is proposed. First of all, the users-items scoring matrix is filtered and cleaned in order to reduce the computational overhead, afterwards the bi-clustering data structures are constructed for the processed matrix, and the algorithm searches for bi-cluster containing the target user. Then, the results of biclustering are set as the initial population, and the moth flame optimization algorithm is applied to deeply optimize the similar users. Finally, the unrated items are predicted for the target user, and the recommendation list is generated for the target user. Validation experiments are carried on different scales of datasets; the results show that the proposed system achieves a good scalability, and also good recommendation performance. (c) 2022 Elsevier B.V. All rights reserved.