<abstract xmlns="http://www.w3.org/1999/xhtml"><p>User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we propose a novel user similarity measure which takes into account the degree of rating the level gap that users could accept. The results of collaborative filtering recommendation based on User Acceptable Rating Radius (UARR) on a real movie rating data set, the MovieLens data set, prove to generate more accurate prediction results compared to the traditional similarity methods.</p></abstract>

User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we propose a novel user similarity measure which takes into account the degree of rating the level gap that users could accept. The results of collaborative filtering recommendation based on User Acceptable Rating Radius (UARR) on a real movie rating data set, the MovieLens data set, prove to generate more accurate prediction results compared to the traditional similarity methods.

UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, P. R. China

Cybernetics and Information Technologies

{"article-title":"UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation"}

User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems....

UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

Online veröffentlicht: 31. Dez. 2013

Seitenbereich: 122 - 130

DOI: https://doi.org/10.2478/cait-2013-0043

This content is open access.