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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (2): 311-319    DOI: 10.3785/j.issn.1008-973X.2020.02.012
Computer Technology, Information Engineering     
Top-N recommendation algorithm combining user trust and influence
Xue-feng ZHANG(),Xiu-li CHEN,De-wen SENG*()
Key Laboratory of Complex Systems Modeling and Simulation, Hangzhou Dianzi University, Hangzhou 310018, China
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Abstract  

A hybrid recommendation algorithm with the incorporation of user trust and social influence was proposed for top-N item recommendation, in view of the existing trust-aware recommendation systems, which directly use the binary trust relationship of social networks to improve the quality of recommendation, and less consider the difference of trust intensity and potential impact between users. The auto-encoder is used to perform unsupervised initial feature optimization on user behavior, and the high-dimensional and sparse user behaviors are compressed into low dimensional and dense users and item feature vectors. A novel trust value measurement model that combines user interaction information, preferences, and trust is brought up to explore the implicit trust relationship between users in social networks and reconstruct the social trust network. The improved structure hole algorithm is used to identify the influential users in the network and improve the top-N item recommendation performance, which integrates the topological structure of the social trust network and the user's interactive information. Comparison verification was conducted on three standard datasets, FilmTrust, Epinions and Ciao, and experimental results demonstrated the effectiveness of the proposed algorithm.

Key words： social recommendation      user trust      influence      matrix factorization      auto-encoder     
Received: 05 January 2019      Published: 10 March 2020
CLC:  TP 391  
Corresponding Authors: De-wen SENG     E-mail: zhangxf@hdu.edu.cn;sengdw@hdu.edu.cn
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Xue-feng ZHANG
Xiu-li CHEN
De-wen SENG
Cite this article:

Xue-feng ZHANG,Xiu-li CHEN,De-wen SENG. Top-N recommendation algorithm combining user trust and influence. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 311-319.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.02.012     OR     http://www.zjujournals.com/eng/Y2020/V54/I2/311


融合用户信任和影响力的top-N推荐算法

针对现有基于信任的推荐方法通常直接利用社交网络的二值信任关系来提高推荐质量，较少考虑用户间信任强度的差异和潜在影响的问题,提出结合用户信任和影响力的混合推荐算法进行top-N项目推荐. 采用自动编码器对用户行为进行无监督的初始特征优化，将高维、稀疏的用户行为压缩成低维、稠密的用户及项目特征向量；提出融合用户交互信息、偏好度和信任的新型信任度量模型，发掘社交网络中用户间的隐含信任关系，重构社会信任网络；将社会信任网络的拓扑结构和用户的交互信息融入结构洞算法，通过改进的结构洞算法来识别网络中的影响力用户，提高top-N项目推荐性能. 实验在FilmTrust、Epinions、Ciao这3个标准数据集上进行对比验证，实验结果证明了所提算法的有效性.


关键词： 社会化推荐,  用户信任,  影响力,  矩阵分解,  自动编码器 
Fig.1 Flow chart of feature extraction
数据集 用户数量 项目数量 评分记录 评分稀疏度/%
Epinions 40 163 139 738 664 824 0.01
Ciao 7 375 99 746 139 738 0.04
FilmTrust 1 508 2 071 40 163 1.14
Tab.1 Dataset sparsity analysis
Fig.2 Correlation analysis between rankings obtained by each algorithm and actual influence
排名 Epinions Ciao FilmTrust
P@10 $\alpha $ $\beta $ $z$ $\mu $ P@10 $\alpha $ $\beta $ $z$ $\mu $ P@10 $\alpha $ $\beta $ $z$ $\mu $
1 0.010 486 0.5 2.0 2.0 0.5 0.023 78 0.5 2.0 2.0 2.0 0.352 258 2.0 1.0 1.0 0.5
2 0.010 467 1.0 2.0 0.5 0.5 0.023 609 0.5 0.5 0.5 0.5 0.351 964 2.0 0.5 2.0 2.0
3 0.010 379 2.0 1.0 2.0 0.5 0.023 411 1.0 2.0 1.0 1.0 0.351 819 0.5 1.0 0.5 1.0
4 0.010 243 1.0 2.0 1.0 1.0 0.023 396 0.5 2.0 1.0 0.5 0.351 819 1.0 1.0 0.5 0.5
5 0.010 231 0.5 2.0 0.5 2.0 0.023 36 2.0 0.5 0.5 1.0 0.351 775 0.5 0.5 0.5 0.5
Tab.2 Top 5 parameter configurations for precision on three datasets
Fig.3 Precision results of parameter $s$ and $\delta $ on three datasets
Fig.4 Precision results of parameter $k$ on three datasets
数据集 方法 N=5 N=10
$P@N$ $F1@N$ ${\rm{NDCG}}@N$ $P@N$ $F1@N$ ${\rm{NDCG}}@N$
Epinions MostPop 0.011 690 0.012 98 0.012 334 0.009 171 0.013 05 0.016 238
GBPR 0.009 353 0.011 03 0.012 296 0.007 560 0.011 11 0.016 095
FISM 0.011 470 0.013 07 0.012 808 0.009 020 0.013 15 0.016 361
FST 0.011 790 0.013 30 0.013 988 0.009 187 0.013 28 0.016 930
FSTID- 0.012 310 0.014 02 0.014 355 0.010 240 0.014 59 0.017 588
FSTID 0.012 430 0.014 15 0.014 470 0.010 480 0.014 76 0.017 832
Ciao MostPop 0.026 770 0.024 36 0.025 906 0.021 420 0.026 62 0.033 443
GBPR 0.022 280 0.020 63 0.022 319 0.018 270 0.021 16 0.028 759
FISM 0.027 040 0.024 95 0.026 185 0.021 410 0.026 87 0.032 510
FST 0.027 410 0.025 23 0.027 240 0.021 740 0.027 20 0.034 910
FSTID- 0.028 300 0.026 44 0.027 389 0.023 290 0.029 14 0.035 503
FSTID 0.029 240 0.026 82 0.027 634 0.023 610 0.029 50 0.035 932
FilmTrust MostPop 0.417 000 0.409 50 0.409 529 0.350 300 0.451 80 0.538 924
GBPR 0.412 400 0.405 10 0.372 923 0.347 000 0.445 80 0.500 997
FISM 0.417 100 0.408 70 0.413 404 0.350 300 0.451 60 0.540 511
FST 0.419 100 0.409 90 0.419 351 0.351 400 0.452 10 0.545 109
FSTID- 0.419 800 0.411 60 0.426 273 0.353 200 0.454 10 0.547 688
FSTID 0.420 500 0.412 40 0.427 569 0.353 300 0.454 80 0.551 260
Tab.3 Top-N item recommendation comparison experiment results
数据集 MostPop GBPR FISM FST FSTID
Epinions 8.98 97.20 106.20 47.00 63.60
Ciao 0.38 8.86 7.70 20.00 11.61
FilmTrust 0.05 0.92 1.34 5.00 3.83
Tab.4 Actual runtime of each algorithm on three datasets min
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