Collaborative Filtering Algorithm Based on User Clustering
2013 ◽
Vol 411-414
◽
pp. 1044-1048
Keyword(s):
To overcome the uncertainty of the users neighborhoods in the recommendation algorithm of nearest neighbor, an improved collaborative filtering algorithm based on user clustering is proposed. This improved algorithm filters the users by their features, and the improved cosine similarity algorithm is used for the item similarity computation. Experiments on the MovieLens dataset showed that, compared with Lis collaborative filtering algorithm, the recommendation quality of the improved algorithm is more accurate and the category coverage is larger.
2014 ◽
Vol 513-517
◽
pp. 1878-1881
2013 ◽
Vol 462-463
◽
pp. 856-860
2014 ◽
Vol 1044-1045
◽
pp. 1484-1488
2020 ◽
Vol 34
(10)
◽
pp. 2059033
2013 ◽
Vol 411-414
◽
pp. 2223-2228
2010 ◽
Vol 21
(10)
◽
pp. 1217-1227
◽