How users evaluate things and each other in social media
by Jure Leskovec (Stanford University)
Recommender systems are inherently driven by evaluations and reviews provided by the users of these systems. Understanding ways in which users form judgments and produce evaluations can provide insights for modern recommendation systems. Many on-line social applications include mechanisms for users to express evaluations of one another, or of the content they create. 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.
About The Speaker
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). The research has also been mentioned in popular press outlets such as the New York Times, the Wallstreet Journal, the Washington Post, MIT Technology review, NBC, BBC, CBC and Wired. Jure has authored the Stanford Network Analysis Platform (SNAP), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges.
Jure serves on the editorial board of ACM Transactions of the Web and chaired ACM WWW Social networks track (2010, 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.