A Method of User Recommendation in Social Networks Based on Trust Relationship and Topic Similarity

Ma, Yinghe; Yu, Zhensheng; Ding, Jun

doi:10.1007/978-3-662-45558-6_22

Cited by 4 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed model captures the bilateral role of user interactions and two similarity measures were considered: users can be similar to other users who contact them in terms of similar “tastes” or similar “attractiveness”. We point out that in our contribution, we have not considered this information since it is not included in the Yelp database. Ma et al (2014) proposed a method of user recommendation in social networks based on trust relationship and topic similarity. They first detect communities based on trust‐propagation and thus model social relationships.…”

Section: Related Workmentioning

confidence: 99%

“…However, in our research, we cannot use the concept of trust propagation since the trust metric we defined calculates the degree of trust of a user in the social network. Trust information between users is not provided in the Yelp database as is the case in the work of Ma et al (2014). Wang, Liao, Cao, Hairong, and Wang (2015) developed a semantic‐based friend recommendation system for social networks, named Friendbook.…”

Section: Related Workmentioning

confidence: 99%

“…A review of the literature in this area shows a significant number of works that deal with various issues including recommendation of items in a social context (Chang & Chu, 2013), recommendation of users (Ma, Yu, & Ding, 2014) or services (Li, Liao, Song, Zhang, & Wang, 2016), as well as the community recommendation in online social networks (Chen, Geyer, Dugan, Muller, & Guy, 2009; Hana et al, 2016; Liu, Hai‐hong, Tong, & Song, 2014). Furthermore, as social recommendation involves the social network analysis research field, some works are based, for example, on community detection (Tang & Liu, 2010), one of the research fields of social network analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recommendation of users in social networks: A semantic and social based classification approach

et al. 2020

View full text Add to dashboard Cite

Recently, the study of social network‐based recommender systems has become an active research topic. The integration of the social relationships that exist between users can improve the accuracy of recommendation results since the users' preferences are similar or influenced by their connected friends. We focus in this article on the recommendation of users in social networks. Our approach is based on semantic and social representations of the users' profiles. We have formalized and illustrated these two dimensions using the Yelp social network. The novelty of our approach concerns the modelling of the credibility of the user, through his/her trust and commitment in the social network. Moreover, in order to optimize the performance of the recommendation process, we have used two classification techniques: an unsupervised technique that uses the K‐means algorithm (applied initially to all users); and a supervised technique that uses the K‐Nearest Neighbours algorithm (applied to newly added users). A recommendation algorithm has been proposed taking into account the cold‐start and sparsity problems. A prototype of a recommender system has been developed and tested using two publicly available datasets: the Yelp database and the Rich Epinions database. The comparative evaluation results show the effectiveness of combining the semantic, the social and the credibility information in an approach that appropriately uses the K‐means and K‐Nearest Neighbours algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recommendation of users in social networks: A semantic and social based classification approach

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Corresponding keywords can be set at will and filtered in the detection process of email contents. In the experiment, unified keywords set has been used to avoid influencing results, and times that they appear are taken interval partition as 3 intervals: (0, 5), [5,10), [10, +∞).…”

Section: Extraction and Classification Of Characteristic Attributes 1mentioning

confidence: 99%

The Analysis of E-mail Communication Security Based on Naïve Bayes

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

View full text Add to dashboard Cite

In the rapid development of information age, email is still an important tool of Internet information communication. It is significant to determine the behavioral relationship of communication on both sides quickly and accurately. By means of applying steganalysis and malware analysis technology, this paper analyzes the content of email and attachments, conducts feature extraction; establishes a behavior model based on naive Bayesian theory, and then proposes communication security behavior analysis method. The experimental results show that this method has an 83% rate of accuracy on communication behavioral relationship recognition.

show abstract

A semantic and social‐based collaborative recommendation of friends in social networks

Berkani

2020

Softw Pract Exp

View full text Add to dashboard Cite

SummaryThe development of social media technologies has greatly enhanced social interactions. The proliferation of social platforms has generated massive amounts of data and a considerable number of persons join these platforms every day. Therefore, one of the current issues is to facilitate the search for the most appropriate friends for a given user. We focus in this article on the recommendation of users in social networks. We propose a novel approach which combines a user‐based collaborative filtering (CF) algorithm with semantic and social recommendations. The semantic dimension suggests the close friends based on the calculation of the similarity between the active user and his friends. The social dimension is based on some social‐behavior metrics such as friendship and credibility degree. The novelty of our approach concerns the modeling of the credibility of the user, through his/her trust and commitment in the social network. A social recommender system based on this approach is developed and experiments have been conducted using the Yelp social network. The evaluation results demonstrated that the proposed hybrid approach improves the accuracy of the recommendation compared with the user‐based CF algorithm and solves the sparsity and cold start problems.

show abstract

A Method of User Recommendation in Social Networks Based on Trust Relationship and Topic Similarity

Cited by 4 publications

References 7 publications

Recommendation of users in social networks: A semantic and social based classification approach

Recommendation of users in social networks: A semantic and social based classification approach

The Analysis of E-mail Communication Security Based on Naïve Bayes

A semantic and social‐based collaborative recommendation of friends in social networks

Contact Info

Product

Resources

About