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学术报告348-基于模糊集理论的选择推荐机制(推荐系统、人工智能)

发布日期: 2016/11/4  投稿: 李成范    部门:    浏览次数: 4148   返回


 

报 告 人:Professor Marek Reformat(University of Alberta, Canada)
报告时间:11月7日(周一下午)14:00~15:30
报告地点:宝山校区计算机大楼1001室
邀 请 人:岳晓冬 博士


报告摘要:
Every day, the users use the Web for things of their interest. They expect to find items that precisely, to the highest possible degree, match the items they are looking for. Quite often this is not enough, they would like to be exposed to things that provide them with some novelty. Systems that support users in their search activities provide them with some kind of variation, but it is not a controlled process. Diversity is accidental – the systems try to estimate what items users may like based on similarities between users, users’ activities, or on explicitly specified preferences. The users do not have any influence on conditions governing formation of lists of suggested items.
In this talk, we assert that application of fuzziness in systems supporting users in their search activities will allow the users to overlook and control mechanisms that identify alternatives and options suggested to them, as well as to influence selection of individuals that constitute groups providing suggestions. We focus on two applications of fuzzy methods that ensure controllable selection processes and illustrate benefits of fuzzy-based processing of available information. Firstly, we focus on social networks. A methodology for selecting groups of individuals that satisfy linguistically described requirements regarding the degree of matching between users’ interests and collective interests of groups is presented. Secondly, we offer a novel recommending approach that provides users with a fuzzy-based process aiming at construction of lists of suggested items. This is accomplished via explicit control of requirements regarding rigorousness of identifying users who become a reference base for generating suggestions. A new way of ranking items rated by multiple users based on Pythagorean fuzzy sets (PFS) and taking into account not only assigned rates but also their number is described.

 

报告人简介:
Marek Reformat (IEEE Senior Member) received the M.Sc. degree (Hons.) from the Technical University of Poznan, Poznan, Poland, and the Ph.D. degree from the University of Manitoba, Winnipeg, MB, Canada. He is currently a Professor with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. The goal of his research activities is to develop methods and techniques for intelligent data modeling and analysis leading to translation of data into knowledge, as well as to design systems that possess abilities to imitate different aspects of human behavior. In this context, the concepts of computational intelligence—with fuzzy computing and possibility theory in particular—are key elements necessary for capturing relationships between pieces of data and knowledge, and for mimicking human ways of reasoning about opinions and facts. He also works on computational intelligence-based approaches for dealing with information stored on the web. He applies elements of fuzzy sets to social networks, linked data, and Semantic Web in order to handle inherently imprecise information, and provide users with unique facts retrieved from the data. All his activities focus on introduction of human aspects to web and software systems which will lead to the development of more human-aware and human-like systems.
Dr. Reformat has been a member of program committees of many international conferences related to computational intelligence and software engineering. He is the past president of the North American Fuzzy Information Processing Society (NAFIPS), and the past vice-president of International Fuzzy Societies Association. Currently, he is a chair of IEEE Computational Intelligence Society Task Force: Fuzzy Systems for Web Intelligence, and a member of Strategical Task Force of IFSA and the board of NAFIPS.

个人主页:https://sites.ualberta.ca/~reformat/