Combining Opinion Profile Modeling with Complex Context Filtering for Contextual Suggestion
University of Delaware Newark United States
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In this paper we describe our effort on TREC 2015 Contextual Suggestion Track. Using opinions from online resources to model both user profile and candidate profile has been proven to be effective on previous TREC. This year we also leverage the power of building profile based on opinions. Opinions from well known commercial online resources are collected in order to build the profiles. Two kinds of opinion representations are used for the two submitted runs. Linear interpolation is leveraged to rank the candidate suggestions. Additionally, an advanced context filter which considers all possible factors such as trip type and trip duration is applied to the ranking results so that unwanted venues are removed from the final ranking list. Official results of our submitted runs show the effectiveness of the proposed method.