How Users Evaluate Things and Each Other in Social Media

In a variety of domains, mechanisms for evaluation allow one user to say whether he or she trusts another user, or likes the content they produced, or wants to confer special levels of authority or responsibility on them. We investigate a number of fundamental ways in which user and item characteristics affect the evaluations in online settings. For example, evaluations are not unidimensional but include multiple aspects that all together contribute to user’s overall
rating. We investigate methods for modeling attitudes and attributes from online reviews that help us better understand user’s individual preferences. We also examine how to create a composite description of evaluations that accurately reflects some type of cumulative opinion of a community. Natural applications of these investigations include
predicting the evaluation outcomes based on user characteristics and to estimate the chance of a favorable overall evaluation from a group knowing only the attributes of the group’s members, but not their expressed opinions.

发言人详细信息

Jure Leskovec is assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining large social and information networks.
Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including best paper awards at KDD (2005, 2007, 2010), WSDM (2011), ICDM (2011) and ASCE J. of Water Resources Planning and Management (2009), ACM KDD dissertation award (2009), Microsoft Research Faculty Fellowship (2011), Alfred P. Sloan Fellowship (2012) and NSF Early Career Development (CAREER) Award (2011). He received his bachelor’s degree in computer science from University of Ljubljana, Slovenia, Ph.D. in machine learning from the Carnegie Mellon University and postdoctoral training from Cornell University. You can follow him on Twitter @jure.

日期：
演讲者：
Jure Leskovec
所属机构：
Stanford