Comparing product specifications to solve the cold start problem in a recommender system

Publication Type:
Conference Proceeding
Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016, 2017
Issue Date:
Filename Description Size
07833354.pdfPublished version386.15 kB
Adobe PDF
Full metadata record
© 2016 IEEE. Recommender systems are widely used applications to solve the problems of information overload, usually on websites. A well-known problem of recommender systems is the problem of cold start, which is caused by the lack of data. A recommendation system can only produce good recommendations after it has accumulated enough data The problem becomes even more challenging when the recommender system comes to deal with new products or the products have not been evaluated by consumers. This paper addresses this problem based on a comparison of product specifications, experiments were conducted in the recommendation domain of digital cameras.
Please use this identifier to cite or link to this item: