報(bào)告人：西班牙哈恩大學(xué) Luis Martínez教授

報(bào)告題目：Group Recommender Systems: A challenge for Consensus Reaching Processes

報(bào)告時(shí)間：2017年11月22日上午10:00至11:30

報(bào)告地點(diǎn)：九里校區(qū)信息樓01020

報(bào)告人簡(jiǎn)介：Luis Martínez is a Full Professor of Computer Science Department and Head of ICT Research Centre at the University of Jaén. His current research interests are linguistic preference modelling, decision making, fuzzy logic based systems, sensory evaluation, recommender systems and electronic commerce.

Luis Martínez is a member of the European Society for Fuzzy Logic and Technology, IEEE. He is the Co-Editor in Chief of the International Journal of Computational Intelligence Systems and an Associated Editor of the journals IEEE Transactions on Fuzzy Systems, Information Fusion, the International Journal of Fuzzy Systems, Journal of Intelligent & Fuzzy Systems, the Scientific World Journal, Journal of Fuzzy Mathematics and serves as member of the journal Editorial Board of the Journal of Universal Computer Sciences.

Prof. Luis is the ESI Most Cited Scientists in Engineering as well as the ESI Most Cited Scientists in Computer Science. He co-edited nine journal special issues on fuzzy preference modelling, soft computing, linguistic decision making and fuzzy sets theory and published more than 70 papers in journals indexed by the SCI as well as 30 book chapters and more than 100 contributions in International Conferences related to his areas. It is remarkable that he has been main researcher in 12 R&D projects. He received twice the IEEE Transactions on Fuzzy Systems Outstanding Paper Award.

報(bào)告摘要：Due to the fact that in the contemporary E-commerce customers demand quick and easy access to products, and there is an overwhelming amount of information that leads customers into the difficult task of filtering information that meets their actual needs. To address this problem, recommender systems were proposed to filter information, thus delivering to users only the information that meets their preferences or needs. Recommender systems are probably the most successful tool to support personalised recommendations.

Most recommender systems research has been focused on the accuracy improvement of recommendation algorithms. Despite this, recently new trends in recommender systems have become important research topics such as, cold start, context awareness, group recommendations, etc. Group recommendations are very challenging because of its own features, the use of group decision techniques based on consensus reaching processes can provide important improvements an open a line for improvement such a type of recommendations.

This talk will review the basics of the most wide-spread type of Recommender Systems such as Collaborative Filtering models. Afterwards, it will be focused on important results obtained in group recommendations based on group decision negotiation processes. Eventually it will be pointed out different open research lines in the topic.