scholarly journals Sentiment and Preference Guided Social Recommendation

2014 ◽  
pp. 79-94 ◽  
Author(s):  
Yoke Yie Chen ◽  
Xavier Ferrer ◽  
Nirmalie Wiratunga ◽  
Enric Plaza
Keyword(s):  
Social Recommendation

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.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

scholarly journals Graph Neural Networks for Social Recommendation

2019 ◽  
Author(s):  
Wenqi Fan ◽  
Yao Ma ◽  
Qing Li ◽  
Yuan He ◽  
Eric Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Recommendation ◽  
Graph Neural Networks

scholarly journals Dual Side Deep Context-aware Modulation for Social Recommendation

2021 ◽  
Author(s):  
Bairan Fu ◽  
Wenming Zhang ◽  
Guangneng Hu ◽  
Xinyu Dai ◽  
Shujian Huang ◽  
...  
Keyword(s):  
Context Aware ◽  
Social Recommendation
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