Uncertainly measures of rough set prediction
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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
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)
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders
IEEE Transactions on Knowledge and Data Engineering
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Using contextual information and multidimensional approach for recommendation
Expert Systems with Applications: An International Journal
Location-based service with context data for a restaurant recommendation
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Collaboration-based medical knowledge recommendation
Artificial Intelligence in Medicine
Soft fuzzy rough sets and its application in decision making
Artificial Intelligence Review
Artificial Intelligence Review
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Context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware recommendation. However, in current approaches, the weights of contextual information are the same, which limits the accuracy of the results. This paper aims to propose a context-aware recommender system by extracting, measuring and incorporating significant contextual information in recommendation. The approach is based on rough set theory and collaborative filtering. It involves a three-steps process. At first, significant attributes to represent contextual information are extracted and measured to identify recommended items based on rough set theory. Then the users' similarity is measured in a target context consideration. Furthermore collaborative filtering is adopted to recommend appropriate items. The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.