Opinions matter: a general approach to user profile modeling for contextual suggestion

      Department of Systems Engineering and Engineering Management
                             The Chinese University of Hong Kong
Title:  Opinions matter: a general approach to user profile modeling for contextual suggestion 
Time: Friday, March 11, 2016, 16:30pm - 17:30pm
Abstract: The increasing use of mobile devices enables an information retrieval (IR) system to capitalize on various types of contexts (e.g., temporal and geographical infor- mation) about its users. Combined with the user preference history recorded in the system, a better understanding of users’ information need can be achieved and it thus leads to improved user satisfaction. More importantly, such a system could proactively recommend suggestions based on the contexts. User profiling is essential in contextual suggestion. However, given most users’ observed behaviors are sparse and their preferences are latent in an IR system, constructing accurate user profiles is generally difficult. In this task, I will present our recent work on location-based contextual suggestion.  In particular, we propose to leverage users’ opinions to construct the profiles. Instead of simply recording ‘‘what places a user likes or dislikes’’ in the past (i.e., description-based profile), we want to construct a profile to identify ‘‘why a user likes or dislikes a place’’ so as to better predict whether the user would like a new candidate suggestion of place. Candidate suggestions are represented in the same fashion and ranked based on their similarities with respect to the user profiles. Moreover, we also develop a novel summary generation method that utilizes the opinion-based user profiles to generate personalized and high-quality summaries for the suggestions.  The systems developed based on the proposed methods have been ranked as top 1 in both TREC 2013 and 2014 contextual suggestion tracks.
Bio: Hui Fang is an Associate Professor in the Department of Electrical and Computer Engineering at University Delaware. She received her M.S. and Ph.D. degree from University of Illinois at Urbana-Champaign in 2004 and 2007, respectively, and B.S degree from Tsinghua University in 2001. Her primary research interest is information retrieval, with focus on enterprise search, search results diversification and axiomatic retrieval models. She received the ACM SIGIR 2004 Best Paper Award and 2010-2011 HP Labs Innovation Research Awards. 
Friday, March 11, 2016 - 08:30 to 09:30