Social Recommendation Based on a Rich Aggregation Model

2012 ◽  
pp. 121-135 ◽  
Author(s):  
Ido Guy
Keyword(s):  
Social Recommendation ◽  
Aggregation Model

A Democratic Preference Aggregation Model

2014 ◽  
Author(s):  
Ismat Beg ◽  
Tabasam Rashid
Keyword(s):  
Preference Aggregation ◽  
Aggregation Model

Social Recommendation with Implicit Social Influence

2021 ◽  
Author(s):  
Changhao Song ◽  
Bo Wang ◽  
Qinxue Jiang ◽  
Yehua Zhang ◽  
Ruifang He ◽  
...  
Keyword(s):  
Social Influence ◽  
Social Recommendation

scholarly journals A social trust and preference segmentation-based matrix factorization recommendation algorithm

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Wei Peng ◽  
Baogui Xin
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
User Preference ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Commercial Activities ◽  
Trust Relationships ◽  
Recommendation Algorithms ◽  
The Difference

AbstractA recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

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