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.