Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
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
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
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
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Recommender systems have become a crucial tool for electronic commerce. These systems help in improving customer satisfaction by providing personalized services. Much of the research in recommender systems is done in solving the top--N recommendation problem where the objective is to identify a set of N items the user will most likely prefer. In this paper, we propose a weighted class based hybrid algorithm to solve this problem. Our algorithm combines the strength of both content based and collaborative filtering approaches. In this method, firstly, classes are defined by applying content based and collaborative filtering techniques on ratings data. Subsequently, user's existing profile of items is examined to determine the weightage of each class. Items are then recommended to the user on the basis of weightage attached to each class. We show through experimental results that our algorithm consistently outperforms existing algorithms when applied on MovieLens data set.