Relevant bibliographies by topics / Next POI Recommendation

Academic literature on the topic 'Next POI Recommendation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Next POI Recommendation"

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Zhang, Zhiqian, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, and Xiangyang Luo. "NEXT: a neural network framework for next POI recommendation." Frontiers of Computer Science 14, no. 2 (August 30, 2019): 314–33. http://dx.doi.org/10.1007/s11704-018-8011-2.

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Zhao, Pengpeng, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5877–84. http://dx.doi.org/10.1609/aaai.v33i01.33015877.

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Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.
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Huang, Jianfeng, Yuefeng Liu, Yue Chen, and Chen Jia. "Dynamic Recommendation of POI Sequence Responding to Historical Trajectory." ISPRS International Journal of Geo-Information 8, no. 10 (September 30, 2019): 433. http://dx.doi.org/10.3390/ijgi8100433.

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Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.
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Li, Xin, Dongcheng Han, Jing He, Lejian Liao, and Mingzhong Wang. "Next and Next New POI Recommendation via Latent Behavior Pattern Inference." ACM Transactions on Information Systems 37, no. 4 (December 10, 2019): 1–28. http://dx.doi.org/10.1145/3354187.

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Li, Zheng, Xueyuan Huang, Chun Liu, and Wei Yang. "Spatio-Temporal Unequal Interval Correlation-Aware Self-Attention Network for Next POI Recommendation." ISPRS International Journal of Geo-Information 11, no. 11 (October 29, 2022): 543. http://dx.doi.org/10.3390/ijgi11110543.

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As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users’ historical check-in trajectories. It is well known that spatial–temporal contextual information plays an important role in analyzing users check-in behaviors. Moreover, the information between POIs provides a non-trivial correlation for modeling users visiting preferences. Unfortunately, the impact of such correlation information and the spatio–temporal unequal interval information between POIs on user selection of next POI, is rarely considered. Therefore, we propose a spatio-temporal unequal interval correlation-aware self-attention network (STUIC-SAN) model for next POI recommendation. Specifically, we first use the linear regression method to obtain the spatio-temporal unequal interval correlation between any two POIs from users’ check-in sequences. Sequentially, we design a spatio-temporal unequal interval correlation-aware self-attention mechanism, which is able to comprehensively capture users’ personalized spatio-temporal unequal interval correlation preferences by incorporating multiple factors, including POIs information, spatio-temporal unequal interval correlation information between POIs, and the absolute positional information of corresponding POIs. On this basis, we perform next POI recommendation. Finally, we conduct comprehensive performance evaluation using large-scale real-world datasets from two popular location-based social networks, namely, Foursquare and Gowalla. Experimental results on two datasets indicate that the proposed STUIC-SAN outperformed the state-of-the-art next POI recommendation approaches regarding two commonly used evaluation metrics.
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Liu, Xuebo, Jingjing Guo, and Peng Qiao. "A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment." Electronics 11, no. 23 (November 30, 2022): 3977. http://dx.doi.org/10.3390/electronics11233977.

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The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN.
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Zang, Hongyu, Dongcheng Han, Xin Li, Zhifeng Wan, and Mingzhong Wang. "CHA: Categorical Hierarchy-based Attention for Next POI Recommendation." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–22. http://dx.doi.org/10.1145/3464300.

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Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.
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Li, Quan, Xinhua Xu, Xinghong Liu, and Qi Chen. "An Attention-Based Spatiotemporal GGNN for Next POI Recommendation." IEEE Access 10 (2022): 26471–80. http://dx.doi.org/10.1109/access.2022.3156618.

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Cui, Yue, Hao Sun, Yan Zhao, Hongzhi Yin, and Kai Zheng. "Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–22. http://dx.doi.org/10.1145/3460198.

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Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.
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Gan, Mingxin, and Ling Gao. "Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks." ISPRS International Journal of Geo-Information 8, no. 6 (June 14, 2019): 279. http://dx.doi.org/10.3390/ijgi8060279.

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Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.
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Book chapters on the topic "Next POI Recommendation"

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Massimo, David, and Francesco Ricci. "Next-POI Recommendations Matching User’s Visit Behaviour." In Information and Communication Technologies in Tourism 2021, 45–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_4.

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AbstractWe consider the urban tourism scenario, which is characterized by limited availability of information about individuals’ past behaviour. Our system goal is to identify relevant next Points of Interest (POIs) recommendations. We propose a technique that addresses the domain requirements by using clusters of users’ visits trajectories that show similar visit behaviour. Previous analysis clustered visit trajectories by aggregating trajectories that contain similar POIs. We compare our approach with a next-item recommendation state-of-the-art Neighbour-based model. The results show that customizing recommendations for clusters of users’ with similar behaviour yields superior performance on different quality dimensions of the recommendation.
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Massimo, David, and Francesco Ricci. "Clustering Users’ POIs Visit Trajectories for Next-POI Recommendation." In Information and Communication Technologies in Tourism 2019, 3–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05940-8_1.

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Xie, Min, Hongzhi Yin, Fanjiang Xu, Hao Wang, and Xiaofang Zhou. "Graph-Based Metric Embedding for Next POI Recommendation." In Web Information Systems Engineering – WISE 2016, 207–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48743-4_17.

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Long, Yan, Pengpeng Zhao, Victor S. Sheng, Guanfeng Liu, Jiajie Xu, Jian Wu, and Zhiming Cui. "Social Personalized Ranking Embedding for Next POI Recommendation." In Lecture Notes in Computer Science, 91–105. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68783-4_7.

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Ni, Jiacheng, Pengpeng Zhao, Jiajie Xu, Junhua Fang, Zhixu Li, Xuefeng Xian, Zhiming Cui, and Victor S. Sheng. "Spatio-Temporal Self-Attention Network for Next POI Recommendation." In Web and Big Data, 409–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_30.

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Chen, Ming, Wenzhong Li, Lin Qian, Sanglu Lu, and Daoxu Chen. "Interest-Aware Next POI Recommendation for Mobile Social Networks." In Wireless Algorithms, Systems, and Applications, 27–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94268-1_3.

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Li, Yepeng, Xuefeng Xian, Pengpeng Zhao, Yanchi Liu, and Victor S. Sheng. "MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendation." In Web Information Systems Engineering – WISE 2021, 193–208. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91560-5_14.

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Li, Changheng, Yongjing Hao, Pengpeng Zhao, Fuzhen Zhuang, Yanchi Liu, and Victor S. Sheng. "Tell Me Where to Go Next: Improving POI Recommendation via Conversation." In Database Systems for Advanced Applications, 211–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73200-4_14.

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Halder, Sajal, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang. "Transformer-Based Multi-task Learning for Queuing Time Aware Next POI Recommendation." In Advances in Knowledge Discovery and Data Mining, 510–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_41.

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Wang, Yu, An Liu, Junhua Fang, Jianfeng Qu, and Lei Zhao. "ADQ-GNN: Next POI Recommendation by Fusing GNN and Area Division with Quadtree." In Web Information Systems Engineering – WISE 2021, 177–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91560-5_13.

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Conference papers on the topic "Next POI Recommendation"

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Oppokhonov, Shokirkhon, Seyoung Park, and Isaac K. E. Ampomah. "Current location-based next POI recommendation." In WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3106528.

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Chen, Yudong, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. "Curriculum Meta-Learning for Next POI Recommendation." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467132.

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Zhao, Kangzhi, Yong Zhang, Hongzhi Yin, Jin Wang, Kai Zheng, Xiaofang Zhou, and Chunxiao Xing. "Discovering Subsequence Patterns for Next POI Recommendation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/445.

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Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.
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Huang, Zheng, Jing Ma, Yushun Dong, Natasha Zhang Foutz, and Jundong Li. "Empowering Next POI Recommendation with Multi-Relational Modeling." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531801.

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Kim, Junbeom, Sihyun Jeong, Goeon Park, Kihoon Cha, Ilhyun Suh, and Byungkook Oh. "DynaPosGNN: Dynamic-Positional GNN for Next POI Recommendation." In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00012.

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Wang, Zhaobo, Yanmin Zhu, Haobing Liu, and Chunyang Wang. "Learning Graph-based Disentangled Representations for Next POI Recommendation." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3532012.

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Li, Yang, Tong Chen, Yadan Luo, Hongzhi Yin, and Zi Huang. "Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/206.

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Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.
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Massimo, David, and Francesco Ricci. "Harnessing a generalised user behaviour model for next-POI recommendation." In RecSys '18: Twelfth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240323.3240392.

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Wu, Yuxia, Ke Li, Guoshuai Zhao, and Xueming Qian. "Long- and Short-term Preference Learning for Next POI Recommendation." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3358171.

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Tan, Haining, Di Yao, Tao Huang, Baoli Wang, Quanliang Jing, and Jingping Bi. "Meta-Learning Enhanced Neural ODE for Citywide Next POI Recommendation." In 2021 22nd IEEE International Conference on Mobile Data Management (MDM). IEEE, 2021. http://dx.doi.org/10.1109/mdm52706.2021.00023.

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