Unsupervised P2P Rental Recommendations Via Integer Programming

Abstract

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.

Meeting Name

23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 (2017: Aug. 13-17, Halifax, Canada)

Department(s)

Computer Science

Comments

This is research was partially supported University of Missouri Research Board (UMRB) via proposal number: 4991.

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)

978-1-4503-4887-4

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Aug 2017

Link to Full Text

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