基于KL散度的用户相似性协同过滤算法
|
摘要
大多数用户相似性算法在计算用户相似性时只考虑了用户间的共同评分项,而忽略了用户其他评分中可能隐藏的有价值信息.为了准确评估用户间的相似性,提出了一种基于KL散度的用户相似性协同过滤算法.该算法不仅利用了共同评分项,还考虑了其他非共同评分信息的影响.该算法充分利用了用户的所有评分信息,提高了用户相似性度量的可靠性和准确性.实验结果表明,该算法优于当前主流的用户相似性算法,且在没有共同评分信息的条件下,仍能有效地完成用户相似性度量,解决了对共同评分项的完全依赖问题,具有更好的适应性.
User similarity based collaborative filtering algorithm is one of most widely used technologies.Most of user similarity algorithms only consider the co-rated items between two users,but ignore other ratings that probably hide valuable information. To evaluate user similarity accurately,a user similarity collaborative filtering algorithm based on Kullback – Leibles( KL) divergence was proposed. The proposed algorithm utilizes both the co-rated items and the influence of other no co-rated items. Since the algorithm makes full use of all rating information,it improves the accuracy and reliability of user similarity. Experiments show that the proposed algorithm outperforms other user similarities. Moreover,it can still measure the user similarity effectively,even if no co-rated items exist. Therefore,the presented algorithm solves the problem of full dependence on co-rated items and gains better flexibility.
User similarity based collaborative filtering algorithm is one of most widely used technologies.Most of user similarity algorithms only consider the co-rated items between two users,but ignore other ratings that probably hide valuable information. To evaluate user similarity accurately,a user similarity collaborative filtering algorithm based on Kullback – Leibles( KL) divergence was proposed. The proposed algorithm utilizes both the co-rated items and the influence of other no co-rated items. Since the algorithm makes full use of all rating information,it improves the accuracy and reliability of user similarity. Experiments show that the proposed algorithm outperforms other user similarities. Moreover,it can still measure the user similarity effectively,even if no co-rated items exist. Therefore,the presented algorithm solves the problem of full dependence on co-rated items and gains better flexibility.
引文
[1]Liu Haifeng,Hu Zheng,Mian Ahmad,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge Based Systems,2014,56(11):156-166.
[2]王永,邓江洲,邓永恒,等.基于项目概率分布的协图2 F1值结果比较同过滤推荐算法[J].现代图书情报技术,2016,32(6):73-79.Wang Yong,Deng Jiangzhou,Deng Yongheng,et al.A collaborative filtering recommendation algorithm based on item probability distribution[J].New Technology of Library and Information Service,2016,32(6):73-79.
[3]Patra B K,Launonen R,Ollikainen V,et al.A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data[J].Knowledge-Based Systems,2015,82(3):163-177.
[4]Kullback S,Leibler R A.On information and sufficiency[J].The Annals of Mathematical Statistics,1951(22):79-86.
[5]Huang Anna.Similarity measures for text document clustering[C]∥the New Zealand Computer Science Research Student Conference(NZCSRSC).New Zealand:[s.n.],2008:49-56.
[6]Zhang Jing,Peng Qinke,Sun Shiquan,et al.Collaborative filtering recommendation algorithm based on uses preference derived from item domain features[J].Physica A,2014,396(2):66-76.
[2]王永,邓江洲,邓永恒,等.基于项目概率分布的协图2 F1值结果比较同过滤推荐算法[J].现代图书情报技术,2016,32(6):73-79.Wang Yong,Deng Jiangzhou,Deng Yongheng,et al.A collaborative filtering recommendation algorithm based on item probability distribution[J].New Technology of Library and Information Service,2016,32(6):73-79.
[3]Patra B K,Launonen R,Ollikainen V,et al.A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data[J].Knowledge-Based Systems,2015,82(3):163-177.
[4]Kullback S,Leibler R A.On information and sufficiency[J].The Annals of Mathematical Statistics,1951(22):79-86.
[5]Huang Anna.Similarity measures for text document clustering[C]∥the New Zealand Computer Science Research Student Conference(NZCSRSC).New Zealand:[s.n.],2008:49-56.
[6]Zhang Jing,Peng Qinke,Sun Shiquan,et al.Collaborative filtering recommendation algorithm based on uses preference derived from item domain features[J].Physica A,2014,396(2):66-76.