A Social Network-Based Approach to Expert Recommendation System

2012 ◽  
pp. 91-102 ◽  
Author(s):  
Elnaz Davoodi ◽  
Mohsen Afsharchi ◽  
Keivan Kianmehr
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Expert Recommendation

scholarly journals Expert Recommendation System based on XMDR using Social Network

2011 ◽  
Vol 15 (3) ◽  
pp. 691-699
Author(s):  
Hyo-Sik Joo ◽  
Chi-Gon Hwang ◽  
Hyo-Young Shin ◽  
Gye-Dong Jung ◽  
Young-Keun Choi
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Expert Recommendation

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

scholarly journals PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

News Recommendation for China Sina Weibo Microblog Service Based on User Social Behaviors

2014 ◽  
pp. 220-243
Author(s):  
Zuo Yuchu ◽  
You Fang ◽  
Wang Jianmin ◽  
Zhou Zhengle
Keyword(s):  
Social Network ◽  
Quantitative Analysis ◽  
Recommendation System ◽  
Heuristic Rules ◽  
Network Service ◽  
Social Network Service ◽  
Ranking List ◽  
Sina Weibo ◽  
Definition Of ◽  
News Recommendation

Sina weibo microblog is an increasingly popular social network service in China. In this work, the authors conducted a study of detecting news in Sina weibo microblog. They found the traditional definition for news can be generalized here. They first expanded the definition of news by conducting user surveys and quantitative analysis. The authors built a news recommendation system by modeling the users, classifying them into four different groups, and applying several heuristic rules, which derived from the generalized definition of news. By applying the new recommendation system, people got newsworthy information, while the funny and interesting tweets, which are popular in Sina weibo microblog, were put in the last ranking list. This study helps us achieve better understanding of heuristic rules about news. Some official organizations can also benefit from the work by supervising the most popular news around civilians.

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.

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