scholarly journals Guest editor’s introduction: special issue on analyzing and mining social networks for decision support and recommender systems

2016 ◽  
Vol 47 (2) ◽  
pp. 193-194
Author(s):  
I-Hsien Ting
Author(s):  
Robin Burke ◽  
Michael D. Ekstrand ◽  
Nava Tintarev ◽  
Julita Vassileva

2006 ◽  
Vol 11 (2) ◽  
pp. 5-9 ◽  
Author(s):  
Francesco Ricci ◽  
Hannes Werthner

2022 ◽  
Author(s):  
Pablo Sánchez ◽  
Alejandro Bellogín

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.


Data Mining ◽  
2013 ◽  
pp. 231-250
Author(s):  
T. T. Wong ◽  
Loretta K.W. Sze

Enterprises are now facing growing global competition and the continual success in the marketplace depends very much on how efficient and effective companies are able to respond to customer demands. Business social network sites (BSNS) have provided a powerful tool to link up manufacturers, suppliers, distributors, and customers. Among the emerging business social networks, decision support functionality addressing the issue of selecting business partners is an important domain to be studied, and it is the objective of this chapter to propose a practical partner selection decision support system. Essentially, a neural-network data mining system is used to generate information for subsequent fuzzy multi-objective analysis. It demonstrates the benefits of integrating information technology, artificial intelligence, and multi-objective decision making to form a practical aid that capitalizes on the merits of BSNS. A special feature is that the trust among companies can be incorporated as an evaluation criterion.


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