Toward a User Interest Ontology to Improve Social Network-Based Recommender System

2014 ◽  
pp. 255-264 ◽  
Author(s):  
Mohamed Frikha ◽  
Mohamed Mhiri ◽  
Faiez Gargouri
Keyword(s):  
Social Network ◽  
Recommender System ◽  
User Interest

Personal recommender system based on user interest community in social network model

2019 ◽  
Vol 526 ◽  
pp. 120961 ◽  
Author(s):  
Jianrui Chen ◽  
Bo Wang ◽  
Liji U ◽  
Zhiping Ouyang
Keyword(s):  
Social Network ◽  
Network Model ◽  
Recommender System ◽  
User Interest

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

scholarly journals Ontology based recommender system using social network data

2021 ◽  
Vol 115 ◽  
pp. 769-779
Author(s):  
Mohamad Arafeh ◽  
Paolo Ceravolo ◽  
Azzam Mourad ◽  
Ernesto Damiani ◽  
Emanuele Bellini
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Network Data ◽  
Social Network Data

scholarly journals Social Network Supported Process Recommender System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yanming Ye ◽  
Jianwei Yin ◽  
Yueshen Xu
Keyword(s):  
Social Network ◽  
Structure Analysis ◽  
Business Process ◽  
Recommender System ◽  
Process Modeling ◽  
Network System ◽  
The Social ◽  
Social Features ◽  
Matching Degree ◽  
Process Structure

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

A Recommender System for Connecting Patients to the Right Doctors in the HealthNet Social Network

2015 ◽  
Author(s):  
Fedelucio Narducci ◽  
Cataldo Musto ◽  
Marco Polignano ◽  
Marco de Gemmis ◽  
Pasquale Lops ◽  
...  
Keyword(s):  
Social Network ◽  
Recommender System ◽  
The Right

A novel social network hybrid recommender system based on hypergraph topologic structure

2017 ◽  
Vol 21 (4) ◽  
pp. 985-1013 ◽  
Author(s):  
Xiaoyao Zheng ◽  
Yonglong Luo ◽  
Liping Sun ◽  
Xintao Ding ◽  
Ji Zhang
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Hybrid Recommender System ◽  
Topologic Structure ◽  
Hybrid Recommender
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