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Unsupervised P2P Rental Recommendations via Integer Programming

Published: 04 August 2017 Publication History

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 unsupervised 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 efficient 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 efficiency 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.

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MP4 File (fu_rental_recommendations.mp4)

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  • (2024)Implementing A/B Testing and Hypothesis-driven Development for Product Performance OptimizationInternational Journal for Research Publication and Seminar10.36676/jrps.v15.i4.1615:4(96-107)Online publication date: 26-Dec-2024
  • (2024)Symmetrical Adoption Pattern of the Digital Sharing EconomyAdvances in Hospitality and Tourism Research (AHTR)10.30519/ahtr.120663712:1(62-92)Online publication date: 29-Mar-2024
  • (2024)Accommodation Recommendation on Shared Platforms Considering Bidirectional Selection and Review MechanismsInternational Journal on Artificial Intelligence Tools10.1142/S021821302350051333:01Online publication date: 23-Feb-2024
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    cover image ACM Conferences
    KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2017
    2240 pages
    ISBN:9781450348874
    DOI:10.1145/3097983
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 04 August 2017

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    Author Tags

    1. integer programming
    2. learning to optimize
    3. unsupervised recommendations

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    • University of Missouri Research Board

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    KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    View all
    • (2024)Implementing A/B Testing and Hypothesis-driven Development for Product Performance OptimizationInternational Journal for Research Publication and Seminar10.36676/jrps.v15.i4.1615:4(96-107)Online publication date: 26-Dec-2024
    • (2024)Symmetrical Adoption Pattern of the Digital Sharing EconomyAdvances in Hospitality and Tourism Research (AHTR)10.30519/ahtr.120663712:1(62-92)Online publication date: 29-Mar-2024
    • (2024)Accommodation Recommendation on Shared Platforms Considering Bidirectional Selection and Review MechanismsInternational Journal on Artificial Intelligence Tools10.1142/S021821302350051333:01Online publication date: 23-Feb-2024
    • (2021)Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling ApproachInformation Systems Research10.1287/isre.2020.0981Online publication date: 2-Apr-2021
    • (2021)Collaborative Neural Social RecommendationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2018.287284251:1(464-476)Online publication date: Jan-2021

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