Fab: content-based, collaborative recommendation
Communications of the ACM
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
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
On the Temporal Analysis for Improved Hybrid Recommendations
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
ACM Transactions on Information Systems (TOIS)
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
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
Coping with noisy search experiences
Knowledge-Based Systems
A probabilistic approach to semantic collaborative filtering using world knowledge
Journal of Information Science
An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems
Journal of Information Science
Cross-domain collaborative filtering over time
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A probability-based unified framework for semantic search and recommendation
Journal of Information Science
Prediction of members' return visit rates using a time factor
Electronic Commerce Research and Applications
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Hi-index | 0.00 |
Collaborative filtering and content-based recommendation methods are two major approaches used in recommender systems. These two methods have some drawbacks in dealing with situations such as sparse data and cold start problems. Recently, combined methods were proposed to overcome these problems. However, a highly effective recommender system may still face a new challenge on interest drift. In this case, customer interests may change over time. For example, more recent users' ratings on items may reflect more on users' current interests than those of long time ago. Unfortunately, current available combination approaches do not consider this important factor and training data sets are regarded as static and time-insensitive. In this paper, we present a novel hybrid recommender system to overcome the interest drift problem by embedding the time-sensitive functions into the recommendation process. The users' interests changing behaviours are considered with time function. Our experiments demonstrate a better performance than that of the collaborative filtering approaches considering interests drift and those of the combined approaches without considering interests drift.