《计算机应用研究》|Application Research of Computers

基于差异路径权重的二部图网络推荐算法

Recommendation algorithm for bipartite graph network structure based on differential path weight

免费全文下载 (已被下载 次)  
获取PDF全文
作者 高长元,段文彬,张树臣
机构 哈尔滨理工大学 a.经济与管理学院;b.高新技术产业发展研究中心,哈尔滨 150040
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)03-015-0716-04
DOI 10.19734/j.issn.1001-3695.2017.09.0905
摘要 针对二部图网络结构推荐算法中资源分配不合理的现象,同时为了丰富推荐结果多样性和促进冷门物品的推荐,提出了一种利用差异路径权重改变资源传递的二部图网络结构算法。利用用户相似性构造路径权重改变第一阶段资源传递规则,使资源较多地流向与目标用户相似的用户节点。通过物品属性相似的构造路径权重,使第二阶段资源更多地流向与目标用户已购物品具有相似属性的物品。实验结果表明,该算法相对于其他流行网络结构算法提高了推荐的综合性能,并且更好地解决了推荐中的相关问题。
关键词 二部图网络结构;差异路径权重;推荐算法;用户相似性;物品属性
基金项目 国家自然科学基金资助项目(71272191,71672050)
黑龙江省哲学社会科学研究规划项目(16GLC07)
黑龙江省自然科学基金资助项目(F2017016)
黑龙江省普通本科高等学校青年创新人才培养计划资助项目(UNPYSCT-2016038)
本文URL http://www.arocmag.com/article/01-2019-03-015.html
英文标题 Recommendation algorithm for bipartite graph network structure based on differential path weight
作者英文名 Gao Changyuan, Duan Wenbin, Zhang Shuchen
机构英文名 a.SchoolofEconomic&Management,b.HightechIndustrialDevelopmentResearchCenter,HarbinUniversityofScience&Technology,Harbin150040,China
英文摘要 Aiming at the unreasonable allocation of resource in the recommendation algorithm of bipartite network, and in order to enrich the diversity of recommended results and promote the recommendation of unpopular items, this paper proposed the algorithm for bipartite graph network structure, which used differential path weight to change resource delivery. It changed the first stage of resource transfer rules by user similarity to construct path weights and made the resources flow more towards the user nodes similar to the target users. Through the consideration of constructing path weight of the articles attributes’ similarity, the second stage resources were more likely to flow to the objects which had similar attributes with the target users’ purchased products. Experimental results show that the proposed algorithm improves the combination property of recommendation, and better solves the related problems in recommendation, which outperforms other popular network structure algorithms.
英文关键词 bipartite network structure; differential path weight; recommendation algorithm; user similarity; articles attri-butes
参考文献 查看稿件参考文献
  [1] 赵宏晨, 翟丽丽, 张树臣. 基于灰色关联度聚类与标签重叠因子结合的协同过滤推荐方法研究[J] . 计算机工程与科学, 2016, 38(1):171-176. (Zhao Hongchen, Zhai Lili, Zhang Shuchen. A collaborative filtering recommendation method based on clustering of gray association degree and factors of tag overlap[J] . Computer Engineering & Science, 2016, 38(1):171-176. )
[2] Pazzani M J, Billsus D. Content-based recommendation systems[M] //Adaptive Web. Berlin:Springer-Verlag, 2007:325-341.
[3] Trewin S. Knowledge-based recommender systems[J] . Encyclopedia of Library and Information Science, 2000, 69(32):180-186.
[4] Zhou Tao, Ren Jie, Medo M, et al. Bipartite network projection and personal recommendation[J] . Physical Review E, 2007, 76(4):046115.
[5] Zanker M, Jessenitschnig M. Case-studies on exploiting explicit customer requirements in recommender systems[J] . User Modeling and User-Adapted Interaction, 2008, 19(1-2):133-166.
[6] Zhang Yicheng, Blattner M, Yu Yikuo. Heat conduction process on community networks as a recommendation model[J] . Physical Review Letters, 2007, 99(15):154301.
[7] Zhou Yanbo, Lyu Linyuan, Liu Weiping, et al. The power of ground user in recommender systems[J] . PLoS ONE, 2013, 8(8):e70094.
[8] Zhou Tao, Jiang Luoluo, Su Riqi, et al. Effect of initial configuration on network-based recommendation[J] . Euro Physics Letters, 2008, 81(5):58004.
[9] 孙玉华, 曾庆铎. 二阶段网络系统的全局DEA模型[J] . 统计与决策, 2014(11):43-46. (Sun Yuhua, Zeng Qingduo. Global DEA model of two stage network system[J] . Statistics and Decision, 2014(11):43-46. )
[10] 张新猛, 蒋盛益, 张倩生, 等. 基于用户偏好加权的混合网络推荐算法[J] . 山东大学学报:理学版, 2015, 50(9):29-35, 41. (Zhang Xinmeng, Jiang Shengyi, Zhang Qiansheng, et al. Hybrid recommendation by combining network-based algorithm and user preference[J] . Journal of Shandong University:Natural Science, 2015, 50(9):29-35, 41. )
[11] 熊湘云. 基于二分网络的多维度推荐技术研究[D] . 苏州:苏州大学, 2013. (Xiong Xiangyun. Multi-dimensional recommendation algorithm based on bipartite network projection[D] . Suzhou:Soochow University, 2013. )
[12] Koren Y. Collaborative filtering with temporal dynamics[J] . Communications of the ACM, 2010, 53(4):89-97.
[13] 王茜, 段双艳. 一种改进的基于二部图网络结构的推荐算法[J] . 计算机应用研究, 2013, 30(3):771-774. (Wang Qian, Duan Shuangyan. Improved recommendation algorithm based on bipartite networks[J] . Application Research of Computers, 2013, 30(3):771-774. )
[14] 葛志鹏, 严广乐, 张国亮. 基于蚁群聚类的二部图网络推荐算法[J] . 信息技术, 2016(3):57-61. (Ge Zhipeng, Yan Guangle, Zhang Guoliang. Bipartite network recommendation algorithm based on ant colony clustering[J] . Information Technology, 2016(3):57-61. )
[15] 王桐远. 基于二分K-均值聚类的二部图网络推荐算法[J] . 经营管理者, 2015(25):3. (Wang Tongyuan. Bigraph network recommendation algorithm based on two points K-means value[J] . Manager Journal, 2015(25):3. )
[16] 肖扬, 王道平, 杨岑. 基于三部图网络结构的知识推荐算法[J] . 计算机应用研究, 2015, 32(2):386-390. (Xiao Yang, Wang Daoping, Yang Cen. Study on knowledge recommendation algorithm based on tripartite graphs network structure[J] . Application Research of Computers, 2015, 32(2):386-390. )
[17] 胡吉明, 林鑫. 基于用户—资源—词汇三部图的社会化推荐算法设计与实现[J] . 情报理论与实践, 2016, 39(3):130-134. (Hu Jiming, Lin Xin. Design and implementation of social recommendation algorithm based on user-object-topic tripartite[J] . Information Stu-dies:Theory & Application, 2016, 39(3):130-134. )
[18] Zhou Tao, Kuscsik Z, Liu Jianguo, et al. Solving the apparent diversity-accuracy dilemma of recommender systems[J] . Proceedings of the National Academy of Sciences, 2010, 107(10):4511-4515.
收稿日期 2017/9/1
修回日期 2017/10/20
页码 716-719,771
中图分类号 TP301.6
文献标志码 A