TY - GEN
T1 - Unsupervised P2P rental recommendations via integer programming
AU - Fu, Yanjie
AU - Liu, Guannan
AU - Teng, Mingfei
AU - Aggarwal, Charu
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/8/13
Y1 - 2017/8/13
N2 - Due to the sparseness of quality rating data, unsupervised rec-ommender 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.
AB - Due to the sparseness of quality rating data, unsupervised rec-ommender 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.
KW - Integer programming
KW - Learning to optimize
KW - Unsupervised recommendations
UR - https://www.scopus.com/pages/publications/85029079620
U2 - 10.1145/3097983.3098044
DO - 10.1145/3097983.3098044
M3 - 会议稿件
AN - SCOPUS:85029079620
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 165
EP - 173
BT - KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Y2 - 13 August 2017 through 17 August 2017
ER -