A knowledge reuse framework for improving novelty and diversity in recommendations

Apurva Pathak, Bidyut Kr. Patra

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

17 Citations (Scopus)


Recommender 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 recommended 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 outperforms state-of-the-art solutions in providing novel and diverse recommended items to individual users and aggregate diversity gain achieved by our approach is on par with recently proposed rank based approach.
Original languageEnglish
Title of host publicationCoDS '15 Proceedings of the Second ACM IKDD Conference on Data Sciences
PublisherAssociation for Computing Machinery ACM
ISBN (Print)978-1-4503-3436-5
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
Event2nd IKDD Conference on Data Sciences, CoDS 2015 - Bangalore, India
Duration: 18 Mar 201521 Mar 2015
Conference number: 2


Conference2nd IKDD Conference on Data Sciences, CoDS 2015
Abbreviated titleCoDS 2015


  • collaborative filtering
  • novelty
  • diversity
  • clustering
  • knowledge reuse framework


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