Academic Journal of Computing & Information Science, 2020, 3(1); doi: 10.25236/AJCIS.2020.030103.
Mingming Qi1,*, WenMing Ma1, Rongjie Shan1
1. School of Computer and Control Engineering, Yantai University, Yantai 264005, China
*Corresponding Author
With the development of economy, tourism is favored by more and more people, and location-based point-of-interest recommendation (POI) begins to emerge. This paper proposes the lstm-based recommendation algorithm for cross-attention mechanism (L-Attention). Embedding method was used to accurately learn the location sequence and location information of users, and feature information of location data was extracted through LSTM network. At the same time, the cross-attention mechanism is introduced to conduct dynamic modeling of personalized time check-in sequence, which can integrate the user's behavioral preference, location sequence information and the influence of sequential location on next-poi, so as to improve the accuracy of recommendation. This article uses Foursquare as an experimental data set. The results show that compared with the traditional recommendation algorithm which only considers the popularity of POI and the deep learning recommendation algorithm which only considers the timing, the accuracy of the proposed algorithm is greatly improved.
Recommendation, points of interest, time series, LSTM, attention mechanism
Mingming Qi, WenMing Ma, Rongjie Shan. Cross-Attention Mechanism Recommendation Algorithm Based on LSTM. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 1: 24-33. https://doi.org/10.25236/AJCIS.2020.030103.
[1] Berjani. B, & Strufe. T. (2011). A recommendation system for spots in location-based online social networks. In Proceedings of the 4th workshop on social network systems(p. 4). ACM.
[2] Ye. M, Yin. P. F, Lee. W. C, & Lee. D. L. (2011). Exploiting Geographical Influence for Collaborative Point-of-interest Recommendation International Acm Sigir Conference on Research & Development in Information Retrieval. ACM.
[3] Liu. B, & Xiong. H. (2013). Point-of-interest recommendation in location based social networks with topic and location awareness. In Proceedings of the 2013 SIAM International Conference on Data Mining (pp. 396-404). Society for Industrial and Applied Mathematics.
[4] Gao. H, Tang. J, Hu. X, & Liu. H. (2015). Content-aware point of interest recommendation on location-based social networks. In Twenty-Ninth AAAI Conference on Artificial Intelligence.
[5] Yin. H, Cui. B, Sun. Y, Hu. Z, & Chen. L. (2014). LCARS: A spatial item recommender system. ACM Transactions on Information Systems (TOIS), 32(3), 11.
[6] Mikolov. T, Chen. K, Corrado. G, & Dean. J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
[7] Cheng. C, Yang. H, Lyu. M. R, & King. I. (2013). Where you like to go next: Successive point-of-interest recommendation. In Twenty-Third international joint conference on Artificial Intelligence.
[8] Liu. Q, Wu. S, Wang. L, & Tan. T. (2016). Predicting the next location: A recurrent model with spatial and temporal contexts. In Thirtieth AAAI Conference on Artificial Intelligence.
[9] He. C, Liu. Y, Guo. Q, & Miao. C. (2019). Multi-Scale Quasi-RNN for Next Item Recommendation. arXiv preprint arXiv:1902.09849.
[10] Wang. Li, Zhang. Ye. (2018). LSTM-based POI personalized recommendation framework [J]. Computer System Applications, 27(12): 56-61.
[11] Zhao. P, Zhu. H, Liu. Y, Li. Z, Xu. J, & Sheng. V. S. (2018). Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation. arXiv preprint arXiv:1806.06671.
[12] Zhou. Y, Huang. C, Hu. Q, Zhu. J, & Tang. Y. (2018). Personalized learning full-path recommendation model based on LSTM neural networks. Information Sciences, 444, 135-152.