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dc.contributor.authorBakhshandegan Moghaddam F
dc.contributor.authorElahi M
dc.contributor.editorKhalid O
dc.contributor.editorKhan SU
dc.contributor.editorZomaya AY
dc.date.accessioned2019-10-18T07:59:59Z
dc.date.available2019-10-18T07:59:59Z
dc.date.issued2019
dc.identifier.isbn978-1785615016
dc.identifier.urihttps://bia.unibz.it/handle/10863/11194
dc.description.abstractRecommendation systems are essential tools to overcome the choice overload problem by suggesting items of interest to users. However, they suffer from a major challenge which is the so-called cold-start problem. The cold-start problem typically happens when the system does not have any form of data on new users and on new items. In this chapter, we describe the cold start problem in recommendation systems. We mainly focus on Collaborative Filtering (CF) systems which are the most popular approaches to build recommender systems and have been successfully employed in many real-world applications. Moreover, we discuss multiple scenarios that cold-start may happen in these systems and explain different solutions for them.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherIETen_US
dc.relation
dc.rights
dc.titleCold Start Solutions For Recommendation Systemsen_US
dc.typeBook chapteren_US
dc.date.updated2019-09-29T03:28:29Z
dc.publication.titleBig Data Recommender Systems: Recent Trends and Advances
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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