Opinion-based User Profile Modeling for Contextual Suggestions

Peilin Yang, Hui Fang
2013 Proceedings of the 2013 Conference on the Theory of Information Retrieval - ICTIR '13  
The problem of contextual suggestion is defined as finding suggested places for a user based on the temporal and geographical context of the user as well as the user's preferences on example places. Existing studies models user preferences based on the descriptive information about the suggestions and might not generalize well. In this paper, we propose to model user profiles based on the opinions about the candidate suggestions. Instead of simply building a profile about "what a user likes or
more » ... at a user likes or dislikes", we want to build the profile based on "why a user likes or dislikes" so that we can make a more accurate prediction on whether a user would like a new candidate suggestion. In particular, we propose to leverage the opinions from the comments posted by other users to estimate a user's profile. The basic assumption is that the reason why a user likes or dislikes a place is likely to be covered by the reviews posted by other users who share the similar opinions as the user. Experiments results over a TREC collection show that the proposed opinion-based user modeling can indeed outperform the existing description-based methods.
doi:10.1145/2499178.2499191 dblp:conf/ictir/YangF13 fatcat:zhvmgzffrnb7hpigqrh6f6rfyu