A Novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering

Data sparsity is a widespread problem of collaborative filtering (CF) recommendation algorithms. However, some common CF methods cannot adequately utilize all user rating information; they are only able to use a small part of the rating data, depending on the co-rated items, which leads to low predi...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 175; pp. 96 - 106
Main Authors: Deng, Jiangzhou, Guo, Junpeng, Wang, Yong
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.07.2019
Elsevier Science Ltd
Subjects:
Accuracy
Algorithms
Clustering
Collaboration
Collaborative filtering
Distribution
Divergence
Euclidean geometry
Filtration
K-medoids algorithm
Kullback–Leibler (KL) divergence
Novels
Probability distribution
Ratings & rankings
Sparsity
Kullback–Leibler (KL) divergence
Probability distribution
Collaborative filtering
Clustering
K-medoids algorithm
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:Data sparsity is a widespread problem of collaborative filtering (CF) recommendation algorithms. However, some common CF methods cannot adequately utilize all user rating information; they are only able to use a small part of the rating data, depending on the co-rated items, which leads to low prediction accuracy. To alleviate this problem, a novel K-medoids clustering recommendation algorithm based on probability distribution for CF is proposed. The proposed scheme makes full use of all rating information based on Kullback–Leibler (KL) divergence from the perspective of item rating probability distribution, and distinguishes different items efficiently when selecting the cluster centers. Meanwhile, the distance model breaks the symmetric mode of classic geometric distance methods (such as Euclidean distance) and considers the effects of different rating numbers between items to emphasize their asymmetric relationship. Experimental results on different datasets show that the proposed clustering algorithm outperforms other compared methods in various evaluation metrics; this approach enhances the prediction accuracy and effectively deals with the sparsity problem. •An improved Kullback–Leibler (KL) divergence is introduced to calculate item similarity.•The optimum center of our algorithm is obtained by maximizing the contribution sum of distance.•An asymmetric mode is used to emphasize the asymmetric relationship between items.•Results show that our scheme improves the effectiveness of recommendation systems.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.03.009