TY - JOUR
T1 - Travel recommendation via fusing multi-auxiliary information into matrix factorization
AU - Chen, Lei
AU - Wu, Zhiang
AU - Cao, Jie
AU - Zhu, Guixiang
AU - Ge, Yong
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB1000901, in part by the National Natural Science Foundation of China (NSFC) under Grant 71571093, Grant 91646204 and Grant 71801123, and in part by Industry Projects in Jiangsu S&T Pillar Program under Grant BE2019110. Authors’ addresses: L. Chen and G. Zhu, 200, Xiaolingwei Street, Nanjing University of Science and Technology, Nanjing, 210094, China; emails: {chenleinjust, zgx881205}@gmail.com; Z. Wu and J. Cao, 128, North Railway Street, Nanjing University of Finance and Economics, Nanjing, 210003, China; emails: [email protected], [email protected]; Y. Ge, McClelland Hall 430V1, 1130 E. Helen St., P.O. Box 210108, Tucson, Arizona, USA; email: [email protected]. 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]. © 2020 Association for Computing Machinery. 2157-6904/2020/01-ART22 $15.00 https://doi.org/10.1145/3372118
Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/1/10
Y1 - 2020/1/10
N2 - As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products.
AB - As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products.
KW - Linear regression
KW - Multiple auxiliary information
KW - Probabilistic matrix factorization
KW - Recommender systems
KW - Travel product recommendation
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U2 - 10.1145/3372118
DO - 10.1145/3372118
M3 - Article
AN - SCOPUS:85078836597
SN - 2157-6904
VL - 11
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
IS - 2
M1 - 22
ER -