Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Specifying preferences based on user history
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings
Proceedings of the 2004 ACM symposium on Applied computing
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
A study of methods for normalizing user ratings in collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Locally linear reconstruction for instance-based learning
Pattern Recognition
An empirical study on effectiveness of temporal information as implicit ratings
Expert Systems with Applications: An International Journal
A Collaborative Filtering Algorithm with Phased Forecast
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
A Similarity Measure for Collaborative Filtering with Implicit Feedback
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Analysis on repeat-buying patterns
Knowledge-Based Systems
Simple time-biased KNN-based recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Design and evaluation of a command recommendation system for software applications
ACM Transactions on Computer-Human Interaction (TOCHI)
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
Group-aware prediction with exponential smoothing for collaborative filtering
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
A heuristic approach to identifying the specific household member for a given rating
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
A recommendation model for handling dynamics in user profile
ICDCIT'12 Proceedings of the 8th international conference on Distributed Computing and Internet Technology
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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Collaborative filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for collaborative filtering do not take concept drift into account. For example, user purchase interests may be volatile. A new mother may be interested in baby toys, although previously she had no interest in these. A man may like romantic films while he preferred action movies one year ago. Collaborative filtering is characterized by concept drift in the real world. To make time-critical predictions, we argue that the target users' recent ratings reflect his/her future preferences more than older ratings. In this paper, we present a novel algorithm namely recency-based collaborative filtering to explore the weights for items based on their expected accuracy on the future preferences. Our proposed approach is based on item-based collaborative filtering algorithms. Specifically, we design a new similarity function to produce similarity scores that better reflect the reality. Our experimental results have shown that the new algorithm substantially improves the precision of traditional collaborative filtering algorithms.