An approach to design of time-aware recommender system based on changes in group user´s preferences

Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user´s preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user´s preferences, but also group user´s preferences affecting prediction of ratings for individual users. In this work is presented improved user-based collaborative filtering algorithm using temporal contextual information. With difference to other approaches, we propose using weight function based on changes in the group user´s preferences over time that increases prediction accuracy of collaborative filtering prediction algorithm.