Collaborative filtering-based recommendations against shilling attacks with particle swarm optimiser and entropy-based mean clustering

Recommender system (RS) in the present web environment is required to gain the knowledge of the users and their commitments such as like and dislike about any items available on the e-commerce sites. Movie recommendations are one of such type in which shilling attack is increasing day by day, this will destroy or abruptly disturb the meaning of the data when recommended to others. Also, the hazards of shilling attacks degrade the performance of web recommendations. Hence, to address this issue the paper, collaborative filtering (CF)-based hybrid model is proposed for movie recommendations. The entropy-based mean (EBM) clustering technique is used to filter out the different clusters out of which the top-N profile recommendations have been taken and then applied with particle swarm optimisation (PSO) technique to get the more optimised recommendations. This research is focused is on getting secure recommendations from different recommender systems.