基于信任扩展和列表级排序学习的服务推荐方法

doi:10.11959/j.issn.1000-436x.2018007

Abstract

Abstract:

In view of the problem of trust relationship in traditional trust-based service recommendation algorithm,and the inaccuracy of service recommendation list obtained by sorting the predicted QoS,a trust expansion and listwise learning-to-rank based service recommendation method (TELSR) was proposed.The probabilistic user similarity computation method was proposed after analyzing the importance of service sorting information,in order to further improve the accuracy of similarity computation.The trust expansion model was presented to solve the sparseness of trust relationship,and then the trusted neighbor set construction algorithm was proposed by combining with the user similarity.Based on the trusted neighbor set,the listwise learning-to-rank algorithm was proposed to train an optimal ranking model.Simulation experiments show that TELSR not only has high recommendation accuracy,but also can resist attacks from malicious users.

Key words: service recommendation, learning-to-rank, probabilistic user similarity, trust relationship

CLC Number:

TP393

Chen FANG,Hengwei ZHANG,Ming ZHANG,Jindong WANG. Trust expansion and listwise learning-to-rank based service recommendation method[J]. Journal on Communications, 2018, 39(1): 147-158.

Figures/Tables 10

References 24

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IP地址	服务ID	响应时间/ms	数据块大小/KB	HTTP code	HTTP message
35.9.27.26	8 451	2 736	582	200	OK
35.9.27.26	8 460	804	14 419	200	OK
35.9.27.26	8 953	20 176	2 624	?1	TimeoutException
192.33.90.66	8 712	719	631	200	OK

k取值	NDCG@10	运行时间/s
1	0.732 4	2.2
2	0.745 6	5.5
3	0.753 7	9.7

矩阵密度	NDCG	CF-DNC	TACF	listPMF	listCF	TELSR
0.04	NDCG@10	0.663 4(5.57%)	0.672 7(4.12%)	0.674 7(3.81%)	0.692 9(1.08%)	0.700 4
	NDCG@20	0.684 5(4.78%)	0.697 5(2.83%)	0.705 1(1.72%)	0.721 8(?0.64%)	0.717 2
0.08	NDCG@10	0.685 9(6.78%)	0.698 3(4.89%)	0.705 0(3.89%)	0.727 3(0.70%)	0.732 4
	NDCG@20	0.711 1(5.43%)	0.725 5(3.34%)	0.731 0(2.56%)	0.746 1(0.48%)	0.749 7
0.12	NDCG@10	0.692 4(8.11%)	0.708 0(5.73%)	0.717 3(4.37%)	0.738 7(1.34%)	0.748 6
	NDCG@20	0.719 3(6.06%)	0.733 5(4.01%)	0.737 6(3.43%)	0.755 2(1.02%)	0.762 9
0.16	NDCG@10	0.698 8(9.23%)	0.717 1(6.44%)	0.725 2(5.25%)	0.751 1(1.62%)	0.763 3
	NDCG@20	0.723 4(7.66%)	0.739 8(5.27%)	0.745 9(4.41%)	0.768 3(1.37%)	0.778 8
0.20	NDCG@10	0.708 8(10.25%)	0.729 6(7.10%)	0.735 6(6.22%)	0.762 6(2.47%)	0.781 4
	NDCG@20	0.732 9(8.57%)	0.746 4(6.61%)	0.758 7(4.88%)	0.779 4(2.01%)	0.795 7

Trust expansion and listwise learning-to-rank based service recommendation method

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