Abstract
Point of interest (POI) recommendation is of great value for both service providers and users. However, it is hard due to data scarcity. To this end, in this paper, we propose a transfer learning based deep neural model, which fuses valueable cross-domain knowledge to achieve more accurate POI recommendation. We first learn the user’s spatial and non-spatial preferences based on their historical POI interactions. The model further captures user interactions in other domains and introduces useful preferences into POI recommendations, which can address data sparsity problems. Compared to the matrix factorization based cross-domain techniques, our method utilizes deep transfer learning, which can learn complex user-item interaction relationships and accurately capture user general preferences to transfer. Finally, we evaluate the proposed model using three real-world datasets. The experimental results show that our model significantly outperforms the state-of-the-art approaches for POI recommendation.
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References
Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems. In: Recommender Systems Handbook, pp. 919–959 (2015)
Chen, L., Shang, S.: Approximate spatio-temporal top-k publish/subscribe. World Wide Web. 22, 2153–2175 (2019)
Chen, L., Shang, S., Yang, C., Li, J.: Spatial keyword search: a survey. GeoInformatica 24, 85–106 (2020)
Chen, X., Xu, J., Zhou, R., Zhao, P., Liu, C., Fang, J., Zhao, L.: S\({}^{\text{2 }}\)r-tree: a pivot-based indexing structure for semantic-aware spatial keyword search. GeoInformatica 24, 3–25 (2020)
Dziugaite, G.K., Roy, D.M.: Neural network matrix factorization. CoRR abs/1511.06443 (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: Proceedings of the WWW, pp. 173–182 (2017)
Hu, G., Zhang, Y., Yang, Q.: Conet: Collaborative cross networks for cross-domain recommendation. In: Proceedings of the ACM CIKM, pp. 667–676 (2018)
Li, J., Cai, T., Deng, K., Wang, X., Sellis, T., Xia, F.: Community-diversified influence maximization in social networks. Inf. Syst. 92, 1–12 (2020)
Li, J., Sellis, T., Culpepper, J.S., He, Z., Liu, C., Wang, J.: Geo-social influence spanning maximization. TKDE 29(8), 1653–1666 (2017)
Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the ACM KDD, pp. 1043–1051 (2013)
Liu, J., Zhao, P., Zhuang, F., Liu, Y., Sheng, V.S., Xu, J., Zhou, X., Xiong, H.: Exploiting aesthetic preference in deep cross networks for cross-domain recommendation. In: Proceedings of the WWW, pp. 2768–2774 (2020)
Loni, B., Shi, Y., Larson, M.A., Hanjalic, A.: Cross-domain collaborative filtering with factorization machines. In: Proceedings of the IR ECIR, pp. 656–661 (2014)
Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: Proceedings of the IEEE CVPR, pp. 3994–4003 (2016)
Pan, S.J., Yang, Q.: A survey on transfer learning. TKDE 22, 1345–1359 (2010)
Pan, W., Liu, N.N., Xiang, E.W., Yang, Q.: Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: Proceedings of the IJCAI, pp. 2318–2323 (2011)
Qian, Z., Xu, J., Zheng, K., Zhao, P., Zhou, X.: Semantic-aware top-k spatial keyword queries. World Wide Web. 21, 573–594 (2018)
Shang, S., Chen, L., Jensen, C.S., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. TKDE 29, 1549–1562 (2017)
Shang, S., Chen, L., Wei, Z., Jensen, C.S., Wen, J., Kalnis, P.: Collective travel planning in spatial networks. TKDE 28, 1132–1146 (2016)
Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. VLDB 10, 1178–1189 (2017)
Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Parallel trajectory similarity joins in spatial networks. VLDB J. 27, 395–420 (2018)
Shang, S., Chen, L., Zheng, K., Jensen, C.S., Wei, Z., Kalnis, P.: Parallel trajectory-to-location join. TKDE 31, 1194–1207 (2019)
Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: Proceedings of the EDBT, pp. 156–167 (2012)
Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23, 449–468 (2014)
Shang, S., Zheng, K., Jensen, C.S., Yang, B., Kalnis, P., Li, G., Wen, J.: Discovery of path nearby clusters in spatial networks. TKDE 27, 1505–1518 (2015)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the ACM KDD, pp. 650–658 (2008)
Song, X., Xu, J., Zhou, R., Liu, C., Zheng, K., Zhao, P., Falkner, N.: Collective spatial keyword search on activity trajectories. GeoInformatica 24, 61–84 (2020)
Xu, J., Chen, J., Zhou, R., Fang, J., Liu, C.: On workflow aware location-based service composition for personal trip planning. Future Gener. Comput. Syst. 98, 274–285 (2019)
Xu, J., Zhao, J., Zhou, R., Liu, C., Zhao, P., Zhao, L.: Destination prediction a deep learning based approach. TKDE (2019). https://doi.org/10.1109/TKDE.2019.2932984
Ye, M., Yin, P., Lee, W., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the ACM SIGIR, pp. 325–334 (2011)
Yin, H., Cui, B., Huang, Z., Wang, W., Wu, X., Zhou, X.: Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In: Proceeding of the ACM MM, pp. 819–822 (2015)
Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for POI recommendation. TKDE 29, 2537–2551 (2017)
Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.W.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the ACM CIKM, pp. 1631–1640 (2015)
Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Time-aware point-of-interest recommendation. In: Proceedings of the ACM SIGIR, pp. 363–372 (2013)
Zhao, P., Zhu, H., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V.S., Zhou, X.: Where to go next: A spatio-temporal gated network for next POI recommendation. In: Proceedings of the AAAI, pp. 5877–5884 (2019)
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Zhang, H., Wei, S., Hu, X. et al. On accurate POI recommendation via transfer learning. Distrib Parallel Databases 38, 585–599 (2020). https://doi.org/10.1007/s10619-020-07299-7
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DOI: https://doi.org/10.1007/s10619-020-07299-7