基于信任传递机制的三支推荐

doi:10.16451/j.cnki.issn1003-6059.202007003

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (594 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To solve the sparseness problem of direct trust relationships in social networks and reduce the recommendation cost of traditional collaborative filtering algorithms, a three-way recommendation algorithm based on trust transfer mechanism is proposed. Firstly, a trust transfer mechanism is built to obtain the user's indirect trust relationships to expand the user's social networks. Secondly, the bipartite graph network structure is applied to calculate the bidirectional influence factors between users. Then, the bidirectional influence factors are regarded as the constraint term to design a new objective function to participate in the matrix factorization. Finally, the misclassification cost and promotion cost are taken into account in the recommendation process by introducing three-way decision, and thus a three-way recommendation algorithm based on objective function is presented. Experimental results on Filmtrust and Epinions datasets indicate that the proposed algorithm is superior to the traditional collaborative filtering algorithms.

Key words： Three-Way Recommendation Trust Transfer Mechanism Indirect Trust Relationship Bipartite Graph Matrix Factorization

Received: 15 June 2020

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61902328), Applied Basic Research Programs of Science and Technology Department of Sichuan Province (2019YJ0314), Scientific Innovation Group for Youths of Sichuan Province (2019JDTD0017), College Students Innovation and Entrepreneurship Training Program of Sichuan Province (S20190615090), Southwest Petroleum University Undergraduate Teaching Reform Research Project (X2018KZ077)

Corresponding Authors: ZHANG Hengru, Ph.D., professor. His research interests include recommender system, granular computing and artificial intelligence security.

About author:: QIN Qin, master student. Her research interests include recommender system.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	QIN Qin
	ZHANG Hengru

Cite this article:

QIN Qin,ZHANG Hengru. Three-Way Recommendation Based on Trust Transfer Mechanism[J]. , 2020, 33(7): 600-609.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202007003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I7/600

[1] 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法.软件学报, 2003, 14(9): 1621-1628
(DENG A L, ZHU Y Y, SHI B L. A Collaborative Filtering Reco-mmendation Algorithm Based on Item Rating Prediction. Journal of Software, 2003, 14(9): 1621-1628)
[2] BOKDE D K, GIRASE S, MUKHOPADHYAY D. An Item-Based Collaborative Filtering Using Dimensionality Reduction Techniques on Mahout Framework[J/OL]. [2020-05-25]. https://arxiv.org/ftp/arxiv/papers/1503/1503.06562.pdf.
[3] SHI Y, LARSON M, HANJALIC A. Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges. ACM Computing Surveys, 2014, 47(1): DOI:10.1145/2556270.
[4] ZHANG H R, MIN F, ZHANG Z H, et.al. Efficient Collaborative Filtering Recommendations with Multi-channel Feature Vectors. International Journal of Machine Learning and Cybernetics, 2019, 10(5): 1165-1172.
[5] YANG B, LEI Y, LIU J M, et al. Social Collaborative Filtering by Trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633-1647.
[6] FAN C C, LI Y, YAO J. A Latent Variable Bayesian Network Reco-mmendation Model for Product Scoring Prediction // Proc of the 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference. Washington, USA: IEEE, 2018: 971-975.
[7] KOREN Y, BELL R M, VOLINSKY C. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 2009, 42(8): 30-37.
[8] 吴清春,贾彩燕.一种融合社交关系的矩阵分解型推荐方法[J/OL]. [2019-07-26]. http://kns.cnki.net/kcms/detail/detail.aspx?FileName=JSJC20190725000&DbName=CAPJ2019.
(WU Q C, JIA C Y. A Matrix Factorization Recommendation Method Integrating Social Relations[J/OL]. [2019-07-26]. http://kns.cnki.net/kcms/detail/detail.aspx?FileName=JSJC20190725000&DbName=CAPJ2019.)
[9] 张紫茵,张恒汝,徐媛媛,等.用户非对称信任关系的推荐算法.计算机科学, 2018, 45(10): 37-42.
(ZHANG Z Y, ZHANG H R, XU Y Y, et al. Recommendation Algorithm for User's Asymmetric Trust Relationship. Computer Science, 2018, 45(10): 37-42.)
[10] MARSDEN P V, FRIEDKIN N. Network Studies of Social Influence. Sociological Methods and Research, 1993, 22(1): 127-151.
[11] WASSERMAN S, FAUST K. Social Network Analysis: Methods and Applications. Cambridge, UK: Cambridge University Press, 1994.
[12] MA H, YANG H X, LYU M R, et al. SoRec: Social Recommendation Using Probabilistic Matrix Factorization // Proc of the 17th ACM International Conference on Information and Knowledge Ma-nagement. New York, USA: ACM, 2008: 931-940.
[13] 熊丽荣,沈树茂,范菁.融合社区结构和社交影响力的矩阵分解推荐.小型微型计算机系统, 2019, 40(11): 2411-2417.
(XIONG L R, SHEN S M, FAN J. Matrix Factorization Reco-mmendation by Integrating Community Structure and Social Influ-ence. Journal of Chinese Computer Systems, 2019, 40(11): 2411-2417).
[14] YAO Y Y. Three-Way Decisions and Cognitive Computing. Cognitive Computation, 2016, 8(4): 543-554.
[15] ZHANG Q H, XIA D Y, LIU K X, et al. A General Model of Decision-Theoretic Three-Way Approximations of Fuzzy Sets Based on a Heuristic Algorithm. Information Sciences, 2020, 507: 522-539.[16] HU B Q. Three-Way Decisions Space and Three-Way Decisions. Information Sciences, 2014, 281: 21-52.
[17] ZHANG H R, MIN F, SHI B. Regression-Based Three-Way Re-commendation. Information Sciences, 2017, 378: 444-461.
[18] YU H, WANG X C, WANG G Y, et al. An Active Three-Way Clustering Method via Low-Rank Matrices for Multi-view Data. Information Sciences, 2020, 507: 823-839.
[19] MIN F, LIU F L, WEN L Y, et al. Tri-partition Cost-Sensitive Active Learning through KNN. Soft Computing, 2019, 23(5): 1557-1572.
[20] MIN F, ZHANG Z H, ZHAI W J, et al. Frequent Pattern Disco-very with Tri-partition Alphabets. Information Sciences, 2020, 507: 715-732.
[21] LI J H, HUANG C C, QI J J, et al. Three-Way Concept Learning via Multi-granularity. Information Sciences, 2017, 378: 244-263.
[22] 李金海,邓硕.概念格与三支决策及其研究展望.西北大学学报(自然科学版), 2017, 47(3): 321-329.
(LI J H, DENG S. Concept Lattice, Three-Way Decision and Its Research Outlooks. Journal of Northwest University(Natural Science Edition), 2017, 47(3): 321-329.)
[23] LIU D, LI T R, RUAN D. Probabilistic Model Criteria with Decision Theoretic Rough Sets. Information Sciences, 2011, 181 (17): 3709-3722.
[24] ZHOU B, YAO Y Y, LUO J G. Cost-Sensitive Three-Way Email
Spam Filtering. Journal of Intelligent Information Systems, 2014, 42(1): 19-45.
[25] LIU D, LI T R, LIANG D C. Three-Way Government Decision Analysis with Decision-Theoretic Rough Sets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2012, 20(S1): 119-132.
[26] YU H, ZHANG C, WANG G Y. A Tree-Based Incremental Overlapping Clustering Method Using the Three-Way Decision Theory. Knowledge-Based Systems, 2016, 91: 189-203.
[27] ZHANG H R, MIN F. Three-Way Recommender Systems Based on Random Forests. Knowledge-Based Systems, 2016, 91: 275-286.
[28] 徐媛媛,张恒汝,闵帆,等.三支交互推荐.南京大学学报(自然科学版), 2019, 55(6): 973-983.
(XU Y Y, ZHANG H R, MIN F, et al. Three-Way Interactive Recommendations. Journal of Nanjing University(Natural Science), 2019, 55(6): 973-983)
[29] ZHOU T, REN J, MEDO M, et al. Bipartite Network Projection and Personal Recommendation. Physical Review E, 2007. DOI: 10.1103/PhysRevE.76.046115.
[30] ZHANG Y C, BLATTNER M, YU Y K. Heat Conduction Process on Community Networks as a Recommendation Model. Physical Review Letters, 2007. DOI: 10.1103/PhysRevLett.99.154301.