Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Understanding and Using Context
Personal and Ubiquitous Computing
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Pagerank-based collaborative filtering recommendation
ICICA'10 Proceedings of the First international conference on Information computing and applications
Personalized context-aware QoS prediction for web services based on collaborative filtering
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Userrank for item-based collaborative filtering recommendation
Information Processing Letters
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Currently, context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware collaborative filtering (CF) recommendation, but the contextual parameters in current approaches have same weights for all users. In this paper we propose an approach to learn the weights of contextual parameters for every user based on back-propagation (BP) neural network (NN). Then we present how to predict ratings based on well-known Slope One CF to achieve personalized contextaware (PC-aware) recommendation. Finally, we experimentally evaluate our approach and compare it to Slope One and context-aware CF. The experiment shows that our approach provide better recommendation results than them.