Personalized Recommendation of Location-Based Services Using Spatio-Temporal-Aware Long and Short Term Neural Network

• Published in: IEEE access Vol. 10; pp. 39864 - 39874
• Main Authors: Sheng, Xiaoshuang, Wang, Fan, Zhu, Yifan, Liu, Tengfei, Chen, Huan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Artificial neural networks; Biological system modeling; Context modeling; Customization; Data models; Deep learning; Encoding; feature interactions; Location based services; Neural network; Neural networks; recommendation system; Recommender systems; Spatiotemporal data; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3166185

User behavioral data are critical for predicting the next item in a recommendation system, which can be acquired by location-based services. However, existing approaches directly use latitude and longitude information, rather than fully exploitation of the location information. In order to better utilize location information and user behavior data, considering the shortcomings of existing models, a spatial-temporal long and short-term neural network(SLSTNN) is proposed in this paper for location-based personalized service recommendation. SLSTNN is the first attempt to comprehensively characterize the long and short term sequences of users, which is achieved by a double-layer attention mechanism and integrated with a deep neural network to improve the representation of spatio-temporal data. In addition, the explicit feature cross-network is employed to characterize user profiles and context features. Experimental results demonstrate that the proposed SLSTNN framework outperforms the state-of-the-art methods with an improvement of online conversion rate by 2.14%. SLSTNN addresses the insufficient feature crossing problem in the simple sequence model and can be potentially used in many recommendation systems.