Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2307
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPathak, A-
dc.contributor.authorPatra, B K-
dc.date.accessioned2015-04-28T06:53:38Z-
dc.date.available2015-04-28T06:53:38Z-
dc.date.issued2015-03-
dc.identifier.citation2nd ACM IKDD Conference on Data Sciences (CoDS 2015), Bangalore, India. 18-21 March,2015.en_US
dc.identifier.urihttp://hdl.handle.net/2080/2307-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractRecommender system (RS) is an important instrument in e-commerce, which provides personalized recommendations to individual user. Classical algorithms in recommender system mainly emphasize on recommendation accuracy in order to match individual user’s past profile. However, recent study shows that novelty and diversity in recommendations are equally important factors from both user and business view points. In this paper, we introduce a knowledge reuse framework to increase novelty and diversity in the recom- mended items of individual users while compromising very little recommendation accuracy. The proposed framework uses features information which have already been extracted by an existing collaborative filtering. Experimental results with real datasets show that our approach outperfoms state- of-the-art solutions in providing novel and diverse recom- mended items to individual users and aggregate diversity gain achieved by our approach is on par with recently pro- posed rank based approach.en_US
dc.language.isoenen_US
dc.subjectCollaborative Filteringen_US
dc.subjectNoveltyen_US
dc.subjectDiversityen_US
dc.subjectKnowledge Reuse Frameworken_US
dc.titleA knowledge reuse framework for improving novelty and diversity in recommendationsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
CODS_2015_submission_36.pdf178.22 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.