User Preference Learning for Online Social Recommendation

2016 ◽  
Vol 28 (9) ◽  
pp. 2522-2534 ◽  
Author(s):  
Zhou Zhao ◽  
Hanqing Lu ◽  
Deng Cai ◽  
Xiaofei He ◽  
Yueting Zhuang
Keyword(s):  
User Preference ◽  
Preference Learning ◽  
Social Recommendation

Social recommendation via multi-view user preference learning

2016 ◽  
Vol 216 ◽  
pp. 61-71 ◽  
Author(s):  
Hanqing Lu ◽  
Chaochao Chen ◽  
Ming Kong ◽  
Hanyi Zhang ◽  
Zhou Zhao
Keyword(s):  
User Preference ◽  
Preference Learning ◽  
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.

scholarly journals User Preference Learning-Based Edge Caching for Fog Radio Access Network

2019 ◽  
Vol 67 (2) ◽  
pp. 1268-1283 ◽  
Author(s):  
Yanxiang Jiang ◽  
Miaoli Ma ◽  
Mehdi Bennis ◽  
Fu-Chun Zheng ◽  
Xiaohu You
Keyword(s):  
Access Network ◽  
User Preference ◽  
Preference Learning ◽  
Radio Access Network ◽  
Radio Access ◽  
Fog Radio Access Network ◽  
Edge Caching

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

MULTIPLE: Multi-level User Preference Learning for List Recommendation

2021 ◽  
pp. 221-236
Author(s):  
Beibei Li ◽  
Beihong Jin ◽  
Xinzhou Dong ◽  
Wei Zhuo
Keyword(s):  
User Preference ◽  
Preference Learning ◽  
Multi Level

Supporting Quality-Based Image Retrieval Through User Preference Learning

2004 ◽  
Vol 70 (8) ◽  
pp. 973-981 ◽  
Author(s):  
Giorgos Mountrakis ◽  
Anthony Stefanidis ◽  
Isolde Schlaisich ◽  
Peggy Agouris
Keyword(s):  
Image Retrieval ◽  
User Preference ◽  
Preference Learning
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