基于加权二部图的个性化方案推荐
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摘要
针对传统协同过滤算法难以解决数据稀疏性、冷启动及用户兴趣各异的问题,提出了基于加权二部图的个性化推荐方法,解决个性化设计方案推荐问题。采用加权二部图,基于用户特征和方案特征的评分,对用户和方案分类,减轻数据稀疏性,形成用户-方案规则库;采用加权网络的协同过滤算法,计算新用户特征与用户-方案规则库中用户特征的改进相似度,通过Top-N方法筛选高相似的方案集进行推荐,解决冷启动和用户兴趣各异的问题。最后与传统协同过滤算法、加权二部图个性化推荐进行比较,证明该方法的有效性和实用性。
As the most widely used recommendation algorithm, the traditional collaborative filtering can hardly solve the problems of data sparsity, cold start and different user interests. Aiming at these three problems, a personalized recommendation method based on weighted bipartite graphs was proposed to solve the problem of recommendation of personalized design scheme. A weighted bipartite graph was used to classify users and schemes based on user characteristics and scoring features to reduce the data sparsity and form a user-scheme rule base. A collaborative filtering algorithm based on weighted networks was used to calculate the improved similarity of user features between new user characteristics and user-scheme rules in the library, and recommended by the Top-N method to screen high similar solution sets to solve the problems of cold starting and different user interests. Finally, compared with the traditional collaborative filtering algorithm and weighted bipartite graph, the validity and practicability of the method were proved.
As the most widely used recommendation algorithm, the traditional collaborative filtering can hardly solve the problems of data sparsity, cold start and different user interests. Aiming at these three problems, a personalized recommendation method based on weighted bipartite graphs was proposed to solve the problem of recommendation of personalized design scheme. A weighted bipartite graph was used to classify users and schemes based on user characteristics and scoring features to reduce the data sparsity and form a user-scheme rule base. A collaborative filtering algorithm based on weighted networks was used to calculate the improved similarity of user features between new user characteristics and user-scheme rules in the library, and recommended by the Top-N method to screen high similar solution sets to solve the problems of cold starting and different user interests. Finally, compared with the traditional collaborative filtering algorithm and weighted bipartite graph, the validity and practicability of the method were proved.
引文
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[2]ZHANG J,LIN Y J,LIN M L,et al.An effective collaborative filtering algorithm based on user preference clustering[J].Applied Intelligence,2016,45(2):230-240.
[3]贾忠涛,吴颖川,刘志勤.一种协同过滤算法在网络干扰过滤中的应用[J].计算机仿真,2016,33(1):284-287.
[4]罗辛,欧阳元新,熊璋,等.通过相似度支持度优化基于K近邻的协同过滤算法[J].计算机学报,2010,33(8):1437-1445.
[5]CHEN H,LI Z K,HU W.An improved collaborative recommendation algorithm based on optimized user similarity[J].The Journal of Supercomputing,2016,72(7):2565-2578.
[6]王鑫,黄忠义.网络资源中基于K-Means聚类的个性化推荐[J].北京邮电大学学报,2014,37(S1):120-124.
[7]姜波,张晓筱,潘伟丰.基于二部图的服务推荐算法研究[J].华中科技大学学报(自然科学版),2013,41(S2):93-99.
[8]李霞,李守伟.面向个性化推荐系统的二分网络协同过滤算法研究[J].计算机应用研究,2013,30(7):1946-1949.
[9]HU X H,MAI Z C,ZHANG H L,et al.A hybrid recommendation model based on weighted bipartite graph and collaborative filtering[C]//Proceedings of 2016IEEE/WIC/ACM International Conference on WEBIntelligence Workshops.Omaha,NE,USA:IEEE,2016:119-122.
[10]CHEN D E,YING Y L.A collaborative filtering recommendation algorithm based on bipartite graph[J].Advanced Materials Research,2013,756-759:3865-3868.
[11]ZHAN W P,LI Q.An improved collaborative filtering based on a weighted network and triadic closure[C]//Proceedings of 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops.Beijing,China:IEEE,2015:1760-1763.
[12]GAN M X,JIANG R.Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities[J].Decision Support Systems,2013,55(3):811-821.
[13]ZHANG J,PENG Q K,SUN S Q,et al.Collaborative filtering recommendation algorithm based on user preference derived from item domain features[J].Physica A:Statistical Mechanics and Its Applications,2014,396:66-76.