GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Selecting relevant instances for efficient and accurate collaborative filtering
Proceedings of the tenth international conference on Information and knowledge management
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Data Mining and Personalization Technologies
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
A Hybrid Approach to Making Recommendations and Its Application to the Movie Domain
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Clustering and Identifying Temporal Trends in Document Databases
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Mood and Recommendations: On Non-cognitive Mood Inducers for High Quality Recommendation
APCHI '08 Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction
An empirical study on effectiveness of temporal information as implicit ratings
Expert Systems with Applications: An International Journal
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
The role of user mood in movie recommendations
Expert Systems with Applications: An International Journal
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
A recommendation model for handling dynamics in user profile
ICDCIT'12 Proceedings of the 8th international conference on Distributed Computing and Internet Technology
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The intensive information overload incurred by the growing interest in the Internet as a medium to conduct business has stimulated the adoption of recommender systems. However, scalability still remains an obstacle to applying recommender mechanism for large-scale web-based systems where thousands of items and transactions are readily available. To deal with this issue, data mining techniques have been applied to reduce the dimensions of candidate sets. In this chapter in the context of movie recommendations, we study a different kind of technique to scale down candidate sets by considering the temporal feature of items. In particular, we argue that movies' production year can be regarded as a “temporal context” to which the value (thus the rating) of the movie can be attached; and thus might significantly affect target users' future preferences. We call it the temporal effects of the items on the performance of the recommender systems. We perform some experiments on the MovieLens data sets. The results show that the temporal feature of items can not only be exploited to scale down the candidate sets, but also increase the accuracy of the recommender systems.