A Locality-Sensitive Hashing-based Automatic Group Identification Approach in Group Recommendation
Yuqing Wang, Yuwen Liu, Yihong Yang, Lingzhen Kong, Ying Miao · 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · 2022
Group recommendations often include two processes, dividing users into groups and aggregating group members’ preferences for recommendation. Because of the increasing number of users and the fact that it is more cost-effective to recommend to homogeneous groups than to heterogeneous groups, the group recommendation prefers to use the automatic identification group method to divide users into groups. However, with the continuous increase of the number of items and users, the time cost required for the process of dividing users into groups also increases sharply. Therefore, in order to effectively deal with massive high-dimensional data, this paper proposes an LSH-based automatic identification group approach called GRLSH. Extensive experiments on the movielens 100k dataset prove that GRLSHcan greatly reduce the time cost of the process while ensuring the accuracy.