scholarly journals Social Networks and Recommender Systems: A World of Current and Future Synergies

2012 ◽  
pp. 445-465 ◽  
Author(s):  
Kanna Al Falahi ◽  
Nikolaos Mavridis ◽  
Yacine Atif
Keyword(s):  
Social Networks ◽  
Recommender Systems

scholarly journals Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

2022 ◽  
Author(s):  
Pablo Sánchez ◽  
Alejandro Bellogín
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Information Sources ◽  
Learning Approaches ◽  
Performance Improvements ◽  
Point Of Interest ◽  
Real Performance ◽  
Research Questions ◽  
Developing Area ◽  
Location Based Social Networks

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

scholarly journals Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 155 ◽  
Author(s):  
Christos Sardianos ◽  
Grigorios Ballas Papadatos ◽  
Iraklis Varlamis
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Filtering ◽  
Prediction Performance ◽  
Research Interest ◽  
Product Review ◽  
Time Efficiency ◽  
The Past ◽  
Product Rating

Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark.

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

Exploration of methodologies to improve job recommender systems on social networks

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Mamadou Diaby ◽  
Emmanuel Viennet ◽  
Tristan Launay
Keyword(s):  
Social Networks ◽  
Recommender Systems

Marketing based on user behavior on Facebook social network through recommender system design

2015 ◽  
Vol 3 ◽  
pp. 27 ◽  
Author(s):  
Bahareh Shadi Shams Zamenjani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
User Behavior ◽  
Real Data ◽  
User Interests ◽  
Second Stage ◽  
Commercial Use ◽  
High Level

t— the influence of social networks among people and at the same time inevitable spread of commercial use of them. Accordingly, in order to sell products, recommender systems designed based on user behavior on social networks, providing a variety of commercial offers tailored to the user. The accuracy of recommender systems that make recommendations to users, and how many of the proposals are accepted by the users is important. In this paper, a recommender system is designed based on user behavior in social network Facebook in two acts and suggests that users purchase their favorite products. The first step is to examine user behavior based on user interests will be given an offer to buy products. In the second stage recommender system uses data mining techniques and suggestions to the user that is associated with their previous purchases. This is real data and the real results of it and it is valid, as well as the results show a high level of accuracy recommender system is designed to offer suggestions to users.

Sign in / Sign up

Export Citation Format

Share Document