Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Personalizing web sites for mobile users
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
A recommender system based on tag and time information for social tagging systems
Expert Systems with Applications: An International Journal
A web search-centric approach to recommender systems with URLs as minimal user contexts
Journal of Systems and Software
A case study in a recommender system based on purchase data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Engineering Applications of Artificial Intelligence
Property-based collaborative filtering for health-aware recommender systems
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Intelligent media indexing and television recommender systems
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems
Journal of Information Science
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Hi-index | 12.06 |
Collaborative filtering is a widely used and proven method of building recommender systems, which provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an especially important factor in some e-commerce environments (such as a mobile environment, due to limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy. This paper presents a variety of temporal information including item launch time, user buying time, the time difference between the two, as well as several combinations of these three. We conducted an empirical study on how temporal information affects the accuracy of a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results show the degree of effectiveness of a variety of temporal information. The empirical results give insight on how to incorporate temporal information to maximize the effectiveness of collaborative filtering in various e-commerce environments.