A fuzzy aproach for determination trust threshold in recommender systems based on social network

2015 ◽  
Author(s):  
Leily Sheugh ◽  
Sasan Hossein Alizadeh
Keyword(s):  
Social Network ◽  
Recommender Systems

Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems

2018 ◽  
Vol 51 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Zhijun Ding ◽  
Xiaolun Li ◽  
Changjun Jiang ◽  
Mengchu Zhou
Keyword(s):  
Social Network ◽  
Recommender Systems ◽  
State Of The Art ◽  
Location Based Social Network

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

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