Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
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
A personalized television listings service
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Integrating information sources for recommender systems
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Recommender systems have traditionally made use of the all variety of sources to obtain the suitable information to make recommendations. There are costs associated with the use of information sources those costs are an important determinant in the choice of which information sources are finally used. For example recommendation can be better if the recommender knows where is the suitable information to predict user's preferences to offer products. Sources that provide in-formation that is timely, accurate and relevant are expected to be used more often than sources that provide irrelevant information. This paper shows how the precision of the recommendations using either Collaborative Filtering (CF) or Content-Base Filtering (CBF) increases by selecting the most relevant information sources based on their intrinsic characteristics.