Abstract
Personalized travel recommendation has become a significant approach for people to find attractions in line with their interests from explosive information. Existing personalized travel recommendation methods always focus on travel history records but attach limited attention to acquire the high-level representation of user’s travel preferences from multi-view heterogeneous information. In this paper, we present a personalized travel recommendation approach based on multi-view representation learning. In the proposed approach, four-view representation obtained from rating, comment, image and regional popularity of attractions are exploited to acquire user’s travel preferences by deep learning and pair-wise optimization. Specially, the aesthetic features are extracted to describe the visual appeal of image, and the regional popularity is introduced to represent the popularities of attractions in a region for personalized recommendation. Finally, an attention module is utilized to automatically learn the significances of four views to the user, and then the predicted preferences is obtained through a weighted average pooling strategy. Extensive experiments constructed on the real-world dataset we collected from tourism websites have demonstrated that the proposed method based on multi-view representation learning is effective and significantly improves the accuracy of personalized travel recommendation.
This work was supported by the National Natural Science Foundation of China (41831072;61572384; 61603233), China’s postdoctoral fund first-class funding (2014M560752), Shannxi province postdoctoral science fund Key Research and Development Program of Shaanxi Province (2017KW-017).
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Change history
31 October 2019
In the original version of the paper the author’s name was incorrectly spelled as Bin Han. It has been corrected to Bing Han.
References
Shi, Y., Serdyukov, P., Hanjalic, A., Larson, M.: Nontrivial landmark recommendation using geotagged photos. ACM Trans. Intell. Syst. Technol. 4(3), 47 (2013)
Shi, Y., Serdyukov, P., Hanjalic, A., Larson, M.: Personalized landmark recommendation based on geotags from photo sharing sites. In: International AAAI Conference on Weblogs and Social Media. AAAI (2011)
Xu, Z., Chen, L., Chen, G.: Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing 155, 99–107 (2015)
Jiang, S., Qian, X., Shen, J., Fu, Y., Mei, T.: Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans. Multimedia 17(6), 907–918 (2015)
Li, Y., Yang, M., Zhang, Z.M.: A survey of multi-view representation learning. IEEE Trans. Knowl. Data Eng. 31, 1863–1883 (2018)
Xu, Z., Chen, L., Dai, Y., Chen, G.: A dynamic topic model and matrix factorization-based travel recommendation method exploiting ubiquitous data. IEEE Trans. Multimedia 19(8), 1933–1945 (2017)
Shen, J., Deng, C., Gao, X.: Attraction recommendation: towards personalized tourism via collective intelligence. Neurocomputing 173, 789–798 (2016)
Kesorn, K., Juraphanthong, W., Salaiwarakul, A.: Personalized attraction recommendation system for tourists through check-in data. IEEE Access 5, 26703–26721 (2017)
Zhao, S., King, I., Lyu, M.R.: Geo-pairwise ranking matrix factorization model for point-of-interest recommendation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 368–377. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_37
Wang, H., Fu, Y., Wang, Q., Yin, H., Du, C., Xiong, H.: A location-sentiment-aware recommender system for both home-town and out-of-town users. In: International Conference on Knowledge Discovery and Data Mining, pp. 1135–1143. ACM (2017)
Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans. Knowl. Data Eng. 29(11), 2537–2551 (2017)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: IEEE International Conference on Data Mining, pp. 263–272. IEEE (2008)
Talebi, H., Milanfar, P.: NIMA: neural image assessment. IEEE Trans. Image Process. 27(8), 3998–4011 (2018)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10
Zhang, Z., Pan, H., Xu, G., Wang, Y., Zhang, P.: A context-awareness personalized tourist attraction recommendation algorithm. Cybern. Inf. Technol. 16(6), 146–159 (2016)
Zhang, Y., Ai, Q., Chen, X., Croft, W.B.: Joint representation learning for top-N recommendation with heterogeneous information sources. In: Conference on Information and Knowledge Management, pp. 1449–1458. ACM (2017)
Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: International Conference on World Wide Web, pp. 1583–1592 (2018)
Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., Liu, H.: What your images reveal: exploiting visual contents for point-of-interest recommendation. In: International Conference on World Wide Web, pp. 391–400 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 5 (2019)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: International Conference on World Wide Web, pp. 173–182 (2017)
He, R., McAuley, J.: VBPR: visual bayesian personalized ranking from implicit feedback. In: AAAI Conference on Artificial Intelligence. AAAI (2016)
Yu, W., Zhang, H., He, X., Chen, X., Xiong, L., Qin, Z.: Aesthetic-based clothing recommendation. In: International Conference on World Wide Web, pp. 649–658 (2018)
Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: ACM Conference on Recommender Systems, pp. 233–240. ACM (2016)
Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: ACM International Conference on Web Search and Data Mining, pp. 425–434. ACM (2017)
He, X., He, Z., Song, J., Liu, Z., Jiang, Y.G., Chua, T.S.: NAIS: Neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)
Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T. S.: Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 335–344. ACM (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc. (2013)
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Zhang, Y., Han, B., Gao, X., Li, H. (2019). Personalized Travel Recommendation via Multi-view Representation Learning. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_9
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