Abstract: Today's online shopping systems enable consumers to sift through a vast amount of information by manipulating combinations of predefined filters. These filters, such as travel dates, price range, and location, are objective attributes that lead to an indisputable set of answers. However, we show that users' search criteria are often subjective and experientially expressed. Hence, to provide consumers with an enhanced search experience, online shopping systems should directly support both subjective and objective search. I will describe how this is done in an experiential search engine that we are currently developing at Megagon Labs; by harnessing information "outside the box", in the text of online reviews or social media, views, and interpreting subjective queries.

Short Bio: Wang-Chiew Tan leads the research efforts at Megagon Labs. Prior to joining Megagon Labs, she was a professor of Computer Science at the University of California, Santa Cruz and she also spent two years at IBM Almaden Research Center. Her research interests include data provenance, data integration, and very recently, natural language processing. She is the recipient of an NSF CAREER award, a Google Faculty Award, and an IBM Faculty Award. She is a co-recipient of the 2014 ACM PODS Alberto O. Mendelzon Test-of-Time Award and a co-recipient of the 2018 ICDT Test-of-Time Award. She was the program committee chair of ICDT 2013 and PODS 2016. She is currently on the VLDB Board of Trustees, a Fellow of the ACM.