Please wait a minute...
Advanced Search
现代图书情报技术  2014, Vol. 30 Issue (10): 70-75     https://doi.org/10.11925/infotech.1003-3513.2014.10.11
  情报分析与研究 本期目录 | 过刊浏览 | 高级检索 |
一种在信任网络中随机游走的推荐算法
原福永, 蔡红蕾
燕山大学信息科学与工程学院 秦皇岛 066004
河北省计算机虚拟技术与系统集成重点实验室 秦皇岛 066004
A Recommendation Algorithm Based on Random Walk in Trust Network
Yuan Fuyong, Cai Honglei
College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China
全文: PDF (531 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

[目的] 为改善基于信任的推荐算法中显式信任值不够精确、隐式信任值难以度量、信任传播路径不易确定等问题, 提出一种在信任网络中随机游走的推荐算法。[方法] 利用二部图网络结构的一维投影度量用户间的信任值并形成用户间直接信任的矩阵, 把该矩阵作为转移概率矩阵, 用于投影后的用户网中进行带重启动的随机游走, 游走过程直至网络中的信任分布趋于稳定, 即信任熵最大时停止。稳定后的信任分布即为全局信任分布。[结果] 通过在MovieLens数据集上的实验表明, 该算法相比于其他算法, 可以显著提高平均绝对误差(MAE)、平均排序倒数(MRR)、标准化折扣增益值(nDCG)。[局限] 由于二部图网络结构算法固有的冷启动问题, 因此本算法受到新用户/新项目的限制。[结论] 该算法能使推荐更精确并且命中的对象排在列表的前端, 具有很强的应用价值。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
蔡红蕾
原福永
关键词 信任二部图网络随机游走推荐算法    
Abstract

[Objective] The current recommendation algorithm based on trust confronted with these issues: the explicit trust value is not accurate enough, the implicit trust value is hard to measure, the trust propagation path is not easy to determine. For that this paper presents a recommendation algorithm based on random walk in the trust network. [Methods] The algorithm uses the Bipartite network-based projection structure measure trust value between users, then these values are the formation of the transition probability matrix for random-walk with restart in the users projection network, random walk process does not stop until the trust distribution tends to be steady, namely the trust maximum entropy is achieved. The trust distribution at this time is the final trust matrix. [Results] The experiments on MovieLens dataset show that the improved Bipartite recommendation algorithm by adding user preferences significantly improves the Mean Absolute Error (MAE), Mean Reciprocal Rank(MRR) and normalize Discounted Cumulative Gain (nDCG) compared to other algorithms. [Limitations] Due to the cold start problems in the Bipartite network-based projection algorithm, this algorithm suffers from the new user/new item problem also. [Conclusions] That is to say, this algorithm can make the recommendation more accurate and successfully recommended objects rank in the front of the list, so this algorithm has a strong application value.

Key wordsTrust    Bipartite network-based    Random-walk    Recommendation algorithm
收稿日期: 2014-02-25      出版日期: 2014-11-28
:  TP301  
基金资助:

本文系国家自然科学基金项目"融合推荐攻击在线集成检测和多维信任机制的可信推荐模型及关键技术研究" (项目编号:61379116)的研究成果之一。

通讯作者: 蔡红蕾 E-mail: caihonglei870325@126.com     E-mail: caihonglei870325@126.com
作者简介: 作者贡献声明: 原福永: 提出研究思路, 设计研究方案; 蔡红蕾: 采集、清洗和分析数据, 进行实验, 论文起草和最终 版本修订。
引用本文:   
原福永, 蔡红蕾. 一种在信任网络中随机游走的推荐算法[J]. 现代图书情报技术, 2014, 30(10): 70-75.
Yuan Fuyong, Cai Honglei. A Recommendation Algorithm Based on Random Walk in Trust Network. New Technology of Library and Information Service, 2014, 30(10): 70-75.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.10.11      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2014/V30/I10/70

[1] Pazzani M J, Billsus D. Content-Based Recommendation Systems [A]//Brusilovsky P, Kobsa A, Nejdl W. The Adaptive Web [M]. Berlin: Springer, 2007: 325-341.
[2] Degemmis M, Lops P, Semeraro G. A Content-Collaborative Recommender that Exploits Wordnet-based User Profiles for Neighborhood Formation [J]. User Modeling and User-Adapted Interaction, 2007, 17(3): 217-255.
[3] Chen Y, Cheng L. A Novel Collaborative Filtering Approach for Recommending Ranked Items [J]. Expert Systems with Applications, 2008, 34(4): 2396-2405.
[4] Konstan J A, Miller B N, Maltz D, et al. GroupLens: Applying Collaborative Filtering to Usenet News [J]. Communications of the ACM, 1997, 40(3): 77-87.
[5] Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating Collaborative Filtering Recommender Systems [J]. ACM Transactions on Information System (TOIS), 2004, 22(1): 5-53.
[6] Guo G. Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems[C]. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013: 3217-3218.
[7] Guo G. Resolving Data Sparsity and Cold Start in Recommender Systems [C]. In: Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization. 2012: 361-364.
[8] Zhou T, Ren J, Medo M, et al. Bipartite Network Projection and Personal Recommendation [J]. Physical Review E, 2007, 76: Article No. 046155.
[9] Zhang Y C, Blaitner M, Yu Y. Heat Conduction Process on Community Networks as a Recommendation Model [J]. Physical Review Letters, 2007, 99(15): Article No. 154301.
[10] Guo G, Zhang J, Thalmann D, et al. From Ratings to Trust: An Empirical Study of Implicit Trust in Recommender Systems [C]. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, 2014: 248-253.
[11] Guo G, Erdt M H, Lee B S. A Hybrid Recommender System Based on Material Concepts with Difficulty Levels [C]. In: Proceedings of the 21st International Conference on Computers in Education. 2013.
[12] Morales J M, Peis E, Herrera-Viedma E. A Filtering and Recommender System for E-Scholars [J]. International Journal of Technology of Enhanced Learning, 2010, 2(2): 227-240.
[13] Guo G. Integrating Trust and Similarity to Ameliorate the Data Sparsity and Cold Start for Recommender Systems [C]. In: Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 2013: 451-454.
[14] 刘淇, 陈恩红. 结合二部图投影与排序的协同过滤[J]. 小型微型计算机系统, 2010, 31(5): 835-839. (Liu Qi, Chen Enhong. Collaborative Filtering Through Combining Bipartite Graph Projection and Ranking [J]. Journal of Chinese Computer Systems, 2010, 31(5): 835-839.)
[15] Fouss F, Pirotte A, Renders J M, et al. Random-Walk Computation of Similarities Between Nodes of a Graph with Application to Collaborative Recommendation [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(3): 355-369.
[16] Moffat A, Zobel J. Rank-based Precision for Measurement of Retrieval Effectiveness [J]. Transactions on Information Systems (TOIS), 2008, 27(1): Article No. 2.

[1] 陈文杰,文奕,杨宁. 基于节点向量表示的模糊重叠社区划分算法*[J]. 数据分析与知识发现, 2021, 5(5): 41-50.
[2] 马莹雪,甘明鑫,肖克峻. 融合标签和内容信息的矩阵分解推荐方法*[J]. 数据分析与知识发现, 2021, 5(5): 71-82.
[3] 梁家铭, 赵洁, 郑鹏, 黄流深, 叶敏祺, 董振宁. 特征选择下融合图像和文本分析的在线短租平台信任计算框架 *[J]. 数据分析与知识发现, 2021, 5(2): 129-140.
[4] 杨辰, 陈晓虹, 王楚涵, 刘婷婷. 基于用户细粒度属性偏好聚类的推荐策略*[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[5] 张毅,杨奕,邓雯. 网络在线信任影响因素研究综述*[J]. 数据分析与知识发现, 2020, 4(5): 15-26.
[6] 张纯金,郭盛辉,纪淑娟,杨伟,伊磊. 基于多属性评分隐表征学习的群组推荐算法*[J]. 数据分析与知识发现, 2020, 4(12): 120-135.
[7] 王根生,潘方正. 融合加权异构信息网络的矩阵分解推荐算法*[J]. 数据分析与知识发现, 2020, 4(12): 76-84.
[8] 韩康康,徐建民,张彬. 融合用户兴趣和多维信任度的微博推荐*[J]. 数据分析与知识发现, 2020, 4(12): 95-104.
[9] 丁勇,程璐,蒋翠清. 基于二部图的P2P网络借贷投资组合决策方法 *[J]. 数据分析与知识发现, 2019, 3(12): 76-83.
[10] 高慧颖,魏甜,刘嘉唯. 基于用户聚类与动态交互信任关系的好友推荐方法研究 *[J]. 数据分析与知识发现, 2019, 3(10): 66-77.
[11] 景东, 张大勇. 社交媒体环境下用户信任度评估与传播影响力研究*[J]. 数据分析与知识发现, 2018, 2(7): 26-33.
[12] 侯君, 刘魁, 李千目. 基于ESSVM的分类推荐*[J]. 数据分析与知识发现, 2018, 2(3): 9-21.
[13] 张李义, 李慧然. 基于互动视角的在线医疗问答患者用户使用研究[J]. 数据分析与知识发现, 2018, 2(1): 76-87.
[14] 薛福亮, 刘君玲. 基于用户间信任关系改进的协同过滤推荐方法*[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[15] 陈梅梅, 薛康杰. 基于标签簇多构面信任关系的个性化推荐算法研究*[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn