Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Collaborative filtering on streaming data with interest-drifting
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
A recommender system with interest-drifting
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Simple time-biased KNN-based recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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Recommender systems address the issue of information overload by providing personalized recommendations towards a target user based upon a history of his/her likes and dislikes. Collaborative filtering and content-based methods are two most commonly used approaches in most recommender systems. Although each of them has both advantages and disadvantages in providing high quality recommendations, a hybrid recommnedation mechanism incorporating components from both of the methods would yield satisfactory results in many situations. Unfortunately, most hybrid approaches have focused on the contents of items but the temporal feature of them, which is the theme of our study here. In particular, we argue, in this paper in the context of movie recommendation, that movies' production year, which reflects the situational environment where the movies were filmed, might affect the values of the movies being recommended, and in turn significantly affect target users' future preferences. We called it the temporal effects of the items on the performance of the recommender systems. We perform some experiments on the famous MovieLens data sets, and significant results were obtained from our experiments. We believe that the temporal features of items can be exploited to not only scale down the huge amount of data set, especially for web-based recommender system, but also allow us to quickly select high quality candidate sets to make more accurate recommendations.