Comparison of Sentiment Analysis and User Ratings in Venue Recommendation

Wang, X., Ounis, I. and Macdonald, C. (2018) Comparison of Sentiment Analysis and User Ratings in Venue Recommendation. In: 41st European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, 14-18 Apr 2019, (Accepted for Publication)

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Abstract

Venue recommendation aims to provide users with venues to visit, taking into account historical visits to venues. Many venue recom- mendation approaches make use of the provided users' ratings to elicit the users' preferences on the venues when making recommendations. In fact, many also consider the users' ratings as the ground truth for as- sessing their recommendation performance. However, users are often re- ported to exhibit inconsistent rating behaviour, leading to less accurate preferences information being collected for the recommendation task. To alleviate this problem, we consider instead the use of the sentiment in- formation collected from comments posted by the users on the venues as a surrogate to the users' ratings. We experiment with various sentiment analysis classifers, including the recent neural networks-based sentiment analysers, to examine the effectiveness of replacing users' ratings with sentiment information. We integrate the sentiment information into the widely used matrix factorization and GeoSoCa multi feature-based venue recommendation models, thereby replacing the users' ratings with the ob- tained sentiment scores. Our results, using three Yelp Challenge-based datasets, show that it is indeed possible to effectively replace users' rat- ings with sentiment scores when state-of-the-art sentiment classifers are used. Our findings show that the sentiment scores can provide accurate user preferences information, thereby increasing the prediction accuracy. In addition, our results suggest that a simple binary rating with `like' and `dislike' is a sufficient substitute of the current used multi-rating scales for venue recommendation in location-based social networks.

Item Type:Conference Proceedings
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Wang, Xi
Authors: Wang, X., Ounis, I., and Macdonald, C.
College/School:College of Science and Engineering
College of Science and Engineering > School of Computing Science
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