Temporal dynamics of changes in group user's preferences in recommender systems
Using contextual information in recommender systems is a subject of continuous improvement of rating prediction accuracy. Among others, information on temporal rating dynamics contain valuable data that establish foundation for discovering changes in both individual and group user's preferences. Such changes can be caused by multiple factors such as changes of individual user interests, changes in item popularity or other hidden patterns or events. In this paper an improved user-based collaborative filtering algorithm is presented that utilizes changes of group user's preferences over time. We also investigate temporal dynamics of changes in user's preferences within different item categories and propose time weight function that improves prediction accuracy of recommender systems.