An Empirical Study on Effect of Semantic Measures in Cross-Domain Recommender System in User Cold-Start Scenario

2021 ◽  
pp. 264-278
Author(s):  
Yuhan Wang ◽  
Qing Xie ◽  
Lin Li ◽  
Yongjian Liu
Keyword(s):  
Empirical Study ◽  
Recommender System ◽  
Cold Start ◽  
Cross Domain

scholarly journals A DNN-Based Cross-Domain Recommender System for Alleviating Cold-Start Problem in E-Commerce

2020 ◽  
Vol 1 ◽  
pp. 194-206
Author(s):  
Hanxin Wang ◽  
Daichi Amagata ◽  
Takuya Makeawa ◽  
Takahiro Hara ◽  
Niu Hao ◽  
...  
Keyword(s):  
Recommender System ◽  
Cold Start ◽  
Cross Domain ◽  
Cold Start Problem

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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