IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Introduction to Information Retrieval
Introduction to Information Retrieval
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Leveraging user comments for aesthetic aware image search reranking
Proceedings of the 21st international conference on World Wide Web
An exploration of ranking heuristics in mobile local search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Predicting the ratings of multimedia items for making personalized recommendations
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system
Proceedings of the sixth ACM conference on Recommender systems
Review quality aware collaborative filtering
Proceedings of the sixth ACM conference on Recommender systems
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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 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.