An agent-based approach for privacy-preserving recommender systems

2007 ◽  
Author(s):  
Richard Cissée ◽  
Sahin Albayrak
Keyword(s):  
Recommender Systems ◽  
Privacy Preserving ◽  
Agent Based

Trust-Aware Recommender Systems for Open and Mobile Virtual Communities

2010 ◽  
pp. 326-345
Author(s):  
N. Sahli ◽  
G. Lenzini
Keyword(s):  
Recommender Systems ◽  
Virtual Communities ◽  
Multi Agent Systems ◽  
Artificial Intelligent ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent

This chapter surveys and discusses relevant works in the intersection among trust, recommendations systems, virtual communities, and agent-based systems. The target of the chapter is showing how, thanks to the use of trust-based solutions and artificial intelligent solutions like that understanding agents-based systems, the traditional recommender systems can improve the quality of their predictions. Moreover, when implemented as open multi-agent systems, trust-based recommender systems can efficiently support users of mobile virtual communities in searching for places, information, and items of interest.

Privacy-Preserving Collaborative Recommender Systems

2010 ◽  
Vol 40 (4) ◽  
pp. 472-476 ◽  
Author(s):  
Justin Zhan ◽  
Chia-Lung Hsieh ◽  
I-Cheng Wang ◽  
Tsan-Sheng Hsu ◽  
Churn-Jung Liau ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Privacy Preserving

Privacy preserving data release for tagging recommender systems

2015 ◽  
Vol 13 (4) ◽  
pp. 229-246
Author(s):  
Tianqing Zhu ◽  
Gang Li ◽  
Yongli Ren ◽  
Wanlei Zhou ◽  
Ping Xiong
Keyword(s):  
Recommender Systems ◽  
Privacy Preserving ◽  
Data Release

scholarly journals A Smart Privacy-Preserving Learning Method by Fake Gradients to Protect Users Items in Recommender Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guixun Luo ◽  
Zhiyuan Zhang ◽  
Zhenjiang Zhang ◽  
Yun Liu ◽  
Lifu Wang
Keyword(s):  
Time Delay ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Privacy Preserving ◽  
Learning Method ◽  
The Real ◽  
Good Convergence ◽  
Factorization Algorithm

In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, we show that our algorithm can be reduced to a time-delay SGD, which can be proved to have a good convergence so that the accuracy will not decline. Our algorithm achieves a good tradeoff between the privacy and accuracy.

Trust-Aware Recommender Systems for Open and Mobile Virtual Communities

2010 ◽  
pp. 135-153
Author(s):  
N. Sahli ◽  
G. Lenzini
Keyword(s):  
Recommender Systems ◽  
Virtual Communities ◽  
Multi Agent Systems ◽  
Artificial Intelligent ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent

This chapter surveys and discusses relevant works in the intersection among trust, recommendations systems, virtual communities, and agent-based systems. The target of the chapter is showing how, thanks to the use of trust-based solutions and artificial intelligent solutions like that understanding agents-based systems, the traditional recommender systems can improve the quality of their predictions. Moreover, when implemented as open multi-agent systems, trust-based recommender systems can efficiently support users of mobile virtual communities in searching for places, information, and items of interest.

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