Recurrent Spatio-Temporal Point Process for Check-in Time Prediction

Yang, Guolei; Cai, Ying; Reddy, Chandan K.

doi:10.1145/3269206.3272003

Cited by 20 publications

(19 citation statements)

References 19 publications

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“…Cai et al [37] present a long-and short-term Hawkes process (LSHP) model, which combines two Hawkes processes to capture "mutual-influence" of different behaviors as well as "self-influence" of behaviors of the same type. Yang et al [36] design a novel Recurrent Spatio-Temporal Point Process (RSTPP) to learn the latent dependencies of event times over behavior sequences. Compared with other methods, RSTPP can utilize abundant spatio-temporal information of precedent records for predicting the time of users' next checkin behaviors.…”

Section: B Temporal Point Processmentioning

confidence: 99%

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Wang

Zhang

Xiang

et al. 2022

IEEE Trans. Cybern.

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Recommender systems are important approaches for dealing with the information overload problem in big data era, and various kinds of auxiliary information, including time and sequential information, can help to improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model named Multivariate Hawkes Process Embedding with attention (MHPE-a), which combines a temporal point process with attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform accurate sequential recommendation to satisfy target users' realtime requirement based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

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Section: B Temporal Point Processmentioning

confidence: 99%

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Wang

Zhang

Xiang

et al. 2022

IEEE Trans. Cybern.

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“…Another approach [13] is a generic model for predicting user trajectories as well as next product recommendation. Recent approaches for checkin time prediction are limited to a single dataset [5,19,37]. They also model event-times as random variables rather than sequential flows and thus cannot be used for transfer across regions.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, Marked Temporal Point Processes(MTPP) have outperformed other neural architectures for characterizing asynchronous events localized in continuous time and are even used in a wide range of applications, including healthcare [29], finance [1,39], and social networks [15,23,40]. Recent works that deploy MTPP for predicting user mobility patterns are either: (i) limited to predicting the time of user-location interactions rather than actual locations [37], (ii) restricted to one dataset without a foreseeable way to easily utilize external information [19], or (iii) disregard the opportunity to reuse trained parameters from external datasets by jointly embedding the checkin and time distributions [5]. Thus, none of these approaches can be used for designing mobility prediction models for limited data regions.…”

mentioning

confidence: 99%

Region Invariant Normalizing Flows for Mobility Transfer

Gupta

Bedathur

2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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There exists a high variability in mobility data volumes across different regions, which deteriorates the performance of spatial recommender systems that rely on region-specific data. In this paper, we propose a novel transfer learning framework called Reformd, for continuous-time location prediction for regions with sparse checkin data. Specifically, we model user-specific checkin-sequences in a region using a marked temporal point process (MTPP) with normalizing flows to learn the inter-checkin time and geo-distributions. Later, we transfer the model parameters of spatial and temporal flows trained on a data-rich origin region for the next check-in and time prediction in a target region with scarce checkin data. We capture the evolving region-specific checkin dynamics for MTPP and spatial-temporal flows by maximizing the joint likelihood of next checkin with three channels (1) checkin-category prediction, (2) checkin-time prediction, and (3) travel distance prediction. Extensive experiments on different user mobility datasets across the U.S. and Japan show that our model significantly outperforms stateof-the-art methods for modeling continuous-time sequences. Moreover, we also show that Reformd can be easily adapted for product recommendations i.e., sequences without any spatial component. CCS CONCEPTS• Information systems → Location based services.

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“…Precisely estimating the locations and times of the future earthquake events and criminal activities could save many lives and predicting malfunctions in an electrical grid could reduce the maintenance times and costs [6], [3], [4]. Despite these critical applications, certain difficulties such as the nonstationarity in both time and space in the data restrict the application of standard approaches [7], [8]. Therefore, we introduce a novel prediction algorithm that models point generations in adaptive subregions with interacting point processes.…”

Section: A Preliminariesmentioning

confidence: 99%

Spatio-temporal Sequence Prediction with Point Processes and Self-organizing Decision Trees

Karaahmetoğlu¹,

Kozat²

2020

Preprint

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We investigate spatio-temporal prediction and introduce a novel prediction algorithm. Our approach is based on the point processes, which we use to model the event arrivals in both space and time. Although we specifically use the Hawkes process, other processes can be readily used as provided remarks in the paper. Moreover, we partition the given spatial region into subregions by an adaptive decision tree and model each subregion with individual and interacting point processes. With individual point processes for each subregion, we estimate the time and location of the events using the past event times and locations. Furthermore, thanks to the nonstationary and self-exciting point generation mechanism in the Hawkes process and the adaptive partitioning of the space, we model the data as nonstationary in both time and space. Finally, we provide a gradient based joint optimization algorithm for the adaptive tree parameter and the point process parameters. With the joint optimization, our algorithm can infer the source statistics and adaptive partitioning of the region. We also provide a training algorithm for the online setup, where we update the model parameters with newly arrived points. We provide experimental results on both simulated data and real-life data where we compare our approach with the standard approaches and demonstrate significant performance improvements thanks to the adaptive spatial partitioning mechanism and the joint optimization procedure.

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Recurrent Spatio-Temporal Point Process for Check-in Time Prediction

Cited by 20 publications

References 19 publications

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Region Invariant Normalizing Flows for Mobility Transfer

Spatio-temporal Sequence Prediction with Point Processes and Self-organizing Decision Trees

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