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.
Y. Fu et al., "Unsupervised P2P Rental Recommendations Via Integer Programming," Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017, Halifax, Canada), pp. 165-173, Association for Computing Machinery (ACM), Aug 2017.
The definitive version is available at https://doi.org/10.1145/3097983.3098044
23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 (2017: Aug. 13-17, Halifax, Canada)
Keywords and Phrases
Data mining; Economic and social effects; Optimization; Peer to peer networks; Risk assessment; Empirical experiments; Integer programming problems; Learning to optimize; Objective functions; Optimization problems; Unsupervised recommendations; User satisfaction; Variable selection; Integer programming
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Aug 2017