Unsupervised P2P Rental Recommendations Via Integer Programming

Due to the sparseness of quality rating data, unsupervised recommender systems are used in many applications in Peer to Peer (P2P) rental marketplaces such as Airbnb, FlipKey, and HomeAway. We present an integer programming based recommender systems, where both accommodation benefits and community risks of lodging places are measured and incorporated into an objective function as utility measurements. More specifically, we first present an unsu-pervised fused scoring method for quantifying the accommodation benefits and community risks of a lodging with crowd-sourced geo-tagged data. In order to the utility of recommendations, we formulate the unsupervised P2P rental recommendations as a constrained integer programming problem, where the accommodation benefits of recommendations are maximized and the community risks of recommendations are minimized, while maintaining constraints on personalization. Furthermore, we provide an eficient solution for the optimization problem by developing a learning-to-integer-programming method for combining aggregated listwise learning to rank into branching variable selection. We apply the proposed approach to the Airbnb data of New York City and provide lodging recommendations to travelers. In our empirical experiments, we demonstrate both the eficiency and effectiveness of our method in terms of striving a trade-off between the user satisfaction, time on market, and the number of reviews, and achieving a balance between positive and negative sides

Penalty-Reward Contrast Analysis of Airbnb\u27s Properties in Tennessee; A Focus on Quality Ratings

Shared Economy (SE) is a growing concept in modern times, and it is having a radical impact on the hospitality industry, especially the lodging industry. The primary purpose of this research was to perform an empirical analysis of the relative importance of the standard quality attributes used to evaluate service quality of Airbnb properties by its guests. This research paper uses Penalty-Reward Contrast Analysis to assess Airbnb’s six quality attribute scores with the guests’ overall quality scores. The research categorized the quality attributes into Basic, Performance and Excitement factors. This research found that the overall ratings for Airbnb properties for the six standard quality attributes in Tennessee were very high, ranging between 9.0 and 9.8 on a 10-point scale (1 = poor; 10 = excellent). However, significant differences existed between the six quality attribute ratings by property type leading to different profiles in terms of the factors being Basic, Performance or Excitement in status