Applying relevant set correlation clustering to multi-criteria recommender systems
Document Type
Conference Proceeding
Publication Date
12-24-2009
Abstract
This thesis investigates application of clustering to multi-criteria ratings as a method of improving the precision of top-N recommendations. With the advent of ecommerce sites that allow multi-criteria rating of items, there is an opportunity for recommender systems to use the additional information to gain a better understanding of user preference. This thesis proposes the use of the relevant set correlation model for a clustering-based collaborative filtering system. It is anticipated this novel system will handle large numbers of users and items without sacrificing the relevance of recommended items. Copyright 2009 ACM.
Identifier
72249112931 (Scopus)
ISBN
[9781605584355]
Publication Title
Recsys 09 Proceedings of the 3rd ACM Conference on Recommender Systems
External Full Text Location
https://doi.org/10.1145/1639714.1639799
First Page
401
Last Page
404
Recommended Citation
Nnadi, Nkechi J., "Applying relevant set correlation clustering to multi-criteria recommender systems" (2009). Faculty Publications. 11662.
https://digitalcommons.njit.edu/fac_pubs/11662
