基于多社交数据源的协同推荐方法研究

doi:10.11959/j.issn.1000-0801.2015113

Abstract

Abstract:

As an effective recommendation method，collaborative filtering typically has the data sparsity and cold-start problems. It was proposed that using multiple data sources of social network to overcome the above problems. First of a11，both the rating similarity and the social trust between users were considered to resolve the data sparsity problem. Then a simple and effective trust reasoning method was proposed to identify the implicit trust relationship between users. In order to solve the cold-start problem，information of the category of items and domain experts was used for joint recommendation. Experimental results show that the proposed algorithm has significantly better precision and reca11 than existing methods.

Key words: social network, personalized recommendation, trust inference, multiple data source, domain expert

Ruiqin Wang,Jun Pan,Yixiao Li. Research on Collaborative Recommendation Method Based on Multiple Data Sources of Social Network[J]. Telecommunications Science, 2015, 31(6): 68-74.

Figures/Tables 4

References 17

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	1-step	2-step	3-step	4-step	5-step	6-step
绝对平均误差	0.69	0.62	0.74	0.88	1.13	1.98
召回率	0.28	0.44	0.45	0.45	0.46	0.46
F1	0.42	0.59	0.58	0.58	0.58	0.52

方法	绝对平均误差	召回率	F1
BASE	2.71	0.94	0.62
CF	0.80	0.16	0.27
TCF	0.54	0.12	0.21
STCF	0.71	0.20	0.32
STCF1	0.69	0.20	0.32
STCF2	0.62	0.44	0.59
STCF3	0.65	0.70	0.78
STCF4	0.66	0.72	0.79

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Research on Collaborative Recommendation Method Based on Multiple Data Sources of Social Network

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