A Clustering-Based Similarity Measurement for Collaborative Filtering

2014 ◽  
Author(s):  
Liang Gu ◽  
Peng Yang ◽  
Yongqiang Dong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measurement

scholarly journals UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 122-130
Author(s):  
Yue Huang ◽  
Xuedong Gao ◽  
Shujuan Gu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Accurate Prediction ◽  
Similarity Measurement ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Level Difference

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.

Similarity measurement based on user interest in collaborative filtering

2010 ◽  
Vol 30 (10) ◽  
pp. 2618-2620 ◽  
Author(s):  
Xiao-sheng JI ◽  
Yan-bing LIU ◽  
Lai-ming LUO
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measurement ◽  
User Interest

JacUOD: A New Similarity Measurement for Collaborative Filtering

2012 ◽  
Vol 27 (6) ◽  
pp. 1252-1260 ◽  
Author(s):  
Hui-Feng Sun ◽  
Jun-Liang Chen ◽  
Gang Yu ◽  
Chuan-Chang Liu ◽  
Yong Peng ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measurement

Collaborative Filtering for Creation of Recommendations on Base of Order Data

2018 ◽  
Vol 16 (2) ◽  
pp. 62-69
Author(s):  
A. A. Knyazeva ◽  
◽  
O. S. Kolobov ◽  
I. Yu. Turchanovsky ◽  
A. M. Fedotov ◽  
...  
Keyword(s):  
Collaborative Filtering
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