Diversifying the predictions in the recommender systems

Bam Bahadur Sinha, R. Dhanalakshmi and Vinnakota Saran Chaitanya

International Journal of Business Information Systems, 2021, vol. 38, issue 2, 168-178

Abstract: In pursuance of building a recommender system, the existing collaborative filtering model often fails to provide a diversified list of recommendation to the end user. Most of the existing models target accuracy and thus fails in avoiding the uniformity dullness from the recommended list. In our paper, we have made use of imputation technique to cut-off sparsity and employed graph-based algorithm to generate a diversified list of recommendations in order to prevent the aforementioned problem of over specialisation. A substantial coverage evaluation on MovieLens dataset demonstrates the fruitfulness of our proposed graph-based model.

Keywords: aggregate diversity; graph-based algorithm; collaborative filtering; data sparsity; imputation; over specialisation; coverage. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=119183 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:38:y:2021:i:2:p:168-178

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().