Visual Recommendation for Peer-To-Peer Accommodation with Online Reviews based on Sentiment Analysis and Topic Models

Peer-to-peer accommodation is developing rapidly in the era of sharing economy, and the visual recommendation of accommodation is also an urgent problem to be solved. Meanwhile, user-generated content is critical in P2P accommodations, because they contain a wealth of information about the opinions and experiences of users, which helps understand consumer decisions and improve products and services better. However, the huge volume of reviews makes it difficult for potential customers to gain useful insights and for managers to track customer opinions. In this paper, we propose a complete pipeline for recommending personalized accommodations for consumers, while also providing insights for managers. First, we use topic modeling techniques to mining opinions from review. Second, we build a deep learning network for review sentiment analysis. Third, we perform sentiment analysis of the reviews at the aspect level to obtain the sentiment vector representation of the accommodation. Finally, we propose a personalized accommodation recommendation method based on the above work. Moreover, we design a visual analytic system with a user-friendly interface to facilitate interactive analysis. Evaluation including user and case studies demonstrates the usefulness and effectiveness of our method and system.

Automated summary

This paper proposes a complete pipeline for recommending personalized accommodations in the peer-to-peer accommodation industry. It uses topic modeling and deep learning techniques for sentiment analysis of user-generated content to improve the understanding of consumer decisions and enhance products and services. The paper also introduces a visual analytic system with a user-friendly interface to facilitate interactive analysis. Evaluation results demonstrate the usefulness and effectiveness of the proposed method and system.