Marie Al-Ghossein
ABSTRACT
The recommendation problem in the hotel industry introduces several interesting and unique challenges leading to the insufficiency of classical approaches. Traveling is not a frequent activity and users tend to have multifaceted behaviors affected by their specific context. While context-aware recommender systems are a promising way to address this problem, the context's dimensions do not contribute equally to the decision-making process and users are not equally sensible to all of the dimensions. In this paper, we propose novel context-aware methods for addressing the hotel recommendation problem, taking into account geography, temporality, textual reviews extracted from social media, and the trip's intent. We present the architecture of the system developed in industry, combining the proposed approaches and addressing each user segment differently. Our experiments show the impact of considering contextual data, external data, and user segmentation on improving the quality of recommendation.
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Index Terms
- Exploiting Contextual and External Data for Hotel Recommendation
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