Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!

Yang, Dingqi; Fankhauser, Benjamin; Rosso, Paolo; Cudré-Mauroux, Philippe

doi:10.24963/ijcai.2020/302

Cited by 98 publications

(56 citation statements)

References 16 publications

(1 reference statement)

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“…ST-RNN proposed by Liu et al [21] and DeepMove [8] are two popular methods that apply attention mechanisms upon RNN to forecast human mobility. Following this trend, various methods [3,9,22,33] incorporating different context information have been proposed to predict human mobility. Since the introduction of the self-attention-based Transformer architecture [27], it has been applied and achieved great success in many fields such as computer vision [1,6], audio processing [31], and natural language processing [5,15].…”

Section: Related Workmentioning

confidence: 99%

Translating Human Mobility Forecasting through Natural Language Generation

Xue

Salim

Ren

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical value as a prediction. Although treating this as a regression problem seems straightforward, incorporating various contextual information such as the semantic category information of each Place-of-Interest (POI) is a necessary step, and often the bottleneck, in designing an effective mobility prediction model. As opposed to the typical approach, we treat forecasting as a translation problem and propose a novel forecasting through a language generation pipeline. The paper aims to address the human mobility forecasting problem as a language translation task in a sequence-to-sequence manner. A mobilityto-language template is first introduced to describe the numerical mobility data as natural language sentences. The core intuition of the human mobility forecasting translation task is to convert the input mobility description sentences into a future mobility description from which the prediction target can be obtained. Under this pipeline, a two-branch network, SHIFT (Translating Human Mobility Forecasting), is designed. Specifically, it consists of one main branch for language generation and one auxiliary branch to directly learn mobility patterns. During the training, we develop a momentum mode for better connecting and training the two branches. Extensive experiments on three real-world datasets demonstrate that the proposed SHIFT is effective and presents a new revolutionary approach to forecasting human mobility. CCS CONCEPTS• Computing methodologies → Natural language generation; • Information systems → Data mining.

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Section: Related Workmentioning

confidence: 99%

Translating Human Mobility Forecasting through Natural Language Generation

Xue

Salim

Ren

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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“…Tips are shorter than reviews and tend to convey quick suggestions. photo id, business id, caption, label Contains photo data including the caption and classification In another recent work, Yang et al [38] proposed a model called Flashback in which they use Basic RNN. The model uses sparse user mobility data by focusing on rich spatio-temporal contexts and doing flashbacks on hidden states in RNNs.…”

Section: Business Id Datementioning

confidence: 99%

“…In the last few years, we have seen an unprecedented rise in the number of works leveraging deep learning in POI recommendation in all major venues (e.g., AAAI, IJCAI, SIGIR, CIKM, WWW, etc.). The use of different deep learning paradigms such as CNN [35,36], RNN [37,38,39,40], Long Short Term Memory (LSTM) [41,42,43,44], Gated Recurrent Unit (GRU) [45,46,47], and self-attention [48,49] have greatly boosted the performance of POI recommendation models. On top of that, state-of-the-art techniques from Natural Language Processing (NLP) have also been employed for complex modeling of human mobility in POI recommendation.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Islam¹,

Mohammad²,

Das³

et al. 2020

Preprint

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Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.

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“…Modelling the joint interactions between user mobility and social relationships can greatly enhance the performance of these downstream tasks [7]. Traditional approaches tailor hand-crafted features extracted from either user mobility data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Huynh

Tong

Nguyên

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Location-based social networks (LBSNs) have emerged over the past few years. Their exponential network effects depend on the fact that each user can share her daily digital footprints with different communities, in different places, and at different times (for example in the form of check-in activities). Unlike other types of social networks, activities in an LBSN can potentially be performed by several users in a collaborative way. Existing studies of representation learning for LBSNs often consider them as regular graphs and ignore these high-order, dynamic, and multi-role contexts, since their holistic interactions are quite difficult to capture. In this paper, we propose a model in which these holistic interactions can be learned and transferred into node embeddings derived from a hypergraph representation and a persona decomposition process. More specifically, the model learns from friendship edges, check-in hyperedges, and node personas at the same time, and devises multiple presentations for each user that reflects their multiple roles in a social context. The embedding learning process also exploits useful patterns such as user colocation and sequential effects through a carefully designed point-of-interest splitting step. Extensive experiments on real and synthetic datasets show that our model outperforms alternative state-of-the-art embedding methods on friendship and location prediction tasks by an average margin of 45.7% and 29.46%, respectively. We also demonstrate the robustness of our model against adversarial conditions such as structural noise, attribute noise, and hyperparameter sensitivity.

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Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!

Cited by 98 publications

References 16 publications

Translating Human Mobility Forecasting through Natural Language Generation

Translating Human Mobility Forecasting through Natural Language Generation

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

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