Exploiting guest preferences with aspect-based sentiment analysis for hotel recommendation

Abstract

This paper presents a collaborative filtering method for hotel recommendation incorporating guest preferences. We used the results of aspect-based sentiment analysis to recommend hotels because whether or not the hotel can be recommended depends on the guest preferences related to the aspects of a hotel. For each aspect of a hotel, we identified the guest preference by using dependency triples extracted from the guest reviews. The triples represent the relationship between aspect and its preference. We calculated transitive association between hotels by using the positive/negative preference on some aspect. Finally, we scored hotels by Markov Random Walk model to explore transitive associations between the hotels. The empirical evaluation showed that aspect-based sentiment analysis improves overall performance. Moreover, we found that it is effective for finding hotels that have never been stayed at but share the same neighborhoods.