A Social Trust Path Recommendation System in Contextual Online Social Networks

2014 ◽  
pp. 652-656 ◽  
Author(s):  
Guohao Sun ◽  
Guanfeng Liu ◽  
Lei Zhao ◽  
Jiajie Xu ◽  
An Liu ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Social Trust

Learning to Suggest Hashtags

2019 ◽  
pp. 302-325
Author(s):  
Fahd Kalloubi ◽  
El Habib Nfaoui
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Semantic Features ◽  
Short Messages

Twitter is one of the primary online social networks where users share messages and contents of interest to those who follow their activities. To effectively categorize and give audience to their tweets, users try to append appropriate hashtags to their short messages. However, the hashtags usage is very small and very heterogeneous and users may spend a lot of time searching the appropriate hashtags. Thus, the need for a system to assist users in this task is very important to increase and homogenize the hashtagging usage. In this chapter, the authors present a hashtag recommendation system on microblogging platforms by leveraging semantic features. Furthermore, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Moreover, they propose a linear and a machine learning based combination of these ranking strategies. The experiment results show that their approach improves content-based recommendations, achieving a recall of more than 47% on recommending 5 hashtags.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals Risk Weighted Social Trust Index for Online Social Networks

2016 ◽  
Vol 78 ◽  
pp. 307-313 ◽  
Author(s):  
Jayprakash Lalchandani ◽  
Hari Bhaskar Sankaranarayanan
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Social Trust

A triadic closure and homophily-based recommendation system for online social networks

2015 ◽  
Vol 18 (6) ◽  
pp. 1579-1601 ◽  
Author(s):  
Giuliana Carullo ◽  
Aniello Castiglione ◽  
Alfredo De Santis ◽  
Francesco Palmieri
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Triadic Closure

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2021 ◽  
pp. 521-540
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
Named Entities ◽  
User Models ◽  
Proposed Model ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

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