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An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Chunyang Liu, Jiping Liu, Jian Wang, Shenghua Xu, Houzeng Han, Yang Chen
2019 ISPRS International Journal of Geo-Information  
To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper.  ...  Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs).  ...  for groups [10, 11] , and so on.  ... 
doi:10.3390/ijgi8080355 fatcat:bwj5scixkjenhkbm6sparmgnjq

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

Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali
2020 arXiv   pre-print
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  ...  This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.  ...  RNN based models Recurrent Neural Networks (RNN) are renowned for their high effectiveness in NLP problems.  ... 
arXiv:2011.10187v1 fatcat:3uampnqerfdvnpuzrxcrsjviwq

Personalized POI Recommendation Based on Subway Network Features and Users' Historical Behaviors

Danfeng Yan, Xuan Zhao, Zhengkai Guo
2018 Wireless Communications and Mobile Computing  
Specifically, the subway network features such as the number of passing stations, waiting time, and transfer times are extracted and a recurrent neural network model is employed to model user behaviors  ...  Current recommender systems often take fusion factors into consideration to realize personalize point-of-interest (POI) recommendation.  ...  It contains a large number of users, restaurants, and ratings for restaurant.  ... 
doi:10.1155/2018/3698198 fatcat:lpptebh4c5ey3hkloawtnajzd4

DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation [article]

Liwei Huang, Yutao Ma, Yanbo Liu, Keqing He
2020 arXiv   pre-print
In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR.  ...  Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is of high efficiency among six neural-network- and attention-based  ...  Because the next POI recommendation problem is, in essence, a sequence prediction problem, recurrent neural networks (RNNs) have been recently applied to modeling sequential influence for next POI recommendation  ... 
arXiv:2004.12161v1 fatcat:7ymnb4z4kndbrfkjwy35sy67aq

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks [chapter]

Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin
2021 Applications and Practices in Ontology Design, Extraction, and Reasoning  
Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture  ...  In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks.  ...  We attempt that by a hypergraph-and a taxonomy embedding technique and recurrent neural networks.  ... 
doi:10.3233/ssw210046 fatcat:rfsad4zo7zhybdjloyor4zjczu

Personalized Tour Recommender through Geotagged Photo Mining and LSTM Neural Networks

Chieh-Yuan Tsai, Gerardo Paniagua, Yu-Jen Chen, Chih-Chung Lo, Liguo Yao, N. Mastorakis, V. Mladenov, A. Bulucea
2019 MATEC Web of Conferences  
In this study, a tour recommendation system based on social media photos is proposed.  ...  The last step is the generation of tours using a long-short term memory neural network (LSTM).  ...  There are many variations of RNN models such as bidirectional RNN, gated recurrent units, recursive neural networks, long-short term memory neural network (LSTM).  ... 
doi:10.1051/matecconf/201929201003 fatcat:o4t77h3h3bdrlikabrbemxqyua

Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study

Sergei Mikhailov, Alexey Kashevnik
2020 Future Internet  
An ontological approach and artificial neural networks are used during behaviour model construction, training and evaluation.  ...  The case studies of behaviour analysis based on classification, clustering and time series events behaviour models are shown.  ...  The authors of Reference [25] use the SOM approach to clustering in the recommendation systems. The users' ratings clustering is achieved by using this model.  ... 
doi:10.3390/fi12100165 fatcat:qxdhaphc2jfipjvp5vw2us42vm

MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

Bin Xia, Yuxuan Bai, Junjie Yin, Qi Li, Lijie Xu
2020 Applied Sciences  
In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and  ...  The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems.  ...  In other words, the context of visiting a specific POI is also significant for demonstrating the user preference which can be captured using the recurrent neural network based model.  ... 
doi:10.3390/app10196664 fatcat:7inzkhfcr5afppwphjcvkup5vu

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task.  ...  To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation.  ...  More recently, researchers adopt recurrent neural networks (RNNs) and other variants like Gated Recurrent Unit (GRU) or Long Short-Term Memory (LSTM) to characterize users' dynamic short-term preferences  ... 
doi:10.1609/aaai.v34i01.5353 fatcat:b5gf4cw26vauhcripftn4gwaj4

LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising [article]

Jinsung Jeon, Soyoung Kang, Minju Jo, Seunghyeon Cho, Noseong Park, Seonghoon Kim, Chiyoung Song
2021 arXiv   pre-print
Considering the fact that next POI recommendation datasets are frequently sparse, we design our presented model based on neural ordinary differential equations (NODEs), which are known to be robust to  ...  In this work, we present a lightweight yet accurate deep learning-based method to predict taxicabs' next locations to better prepare for targeted advertising based on demographic information of locations  ...  Feng et al. designed a special embedding-based mechanism for POI recommendation [11] . Liu et al. used a recurrent neural network (RNN) to capture spatial and temporal information [20] .  ... 
arXiv:2108.04993v3 fatcat:ackfd4e3bfg2feotsxo4urqu5u

A Survey on Session-based Recommender Systems [article]

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Defu Lian
2021 arXiv   pre-print
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.  ...  Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy.  ...  Yan Zhao for their constructive suggestions on this work. This work was supported by Australian Research Council Discovery Grants (DP180102378, DP190101079 and FT190100734).  ... 
arXiv:1902.04864v3 fatcat:oka5bvibzzbk5oreltrupehaey

DeepStore: An Interaction-aware Wide&Deep Model for Store Site Recommendation with Attentional Spatial Embeddings

Yan Liu, Bin Guo, Nuo Li, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Zhiwen Yu, Sizhe Zhang, Lina Yao
2019 IEEE Internet of Things Journal  
[25] proposed an recurrent neural network (RNN)-based neural network solution by modeling the user's historical POI visits in a sequential manner. Feng et al.  ...  Third, the model for store site recommendation is different, we propose a unified interaction-aware model based on the neural network which can learn complex relations from multisource data.  ...  Her current research interests include data mining and machine learning, recommender systems, and human activity recognition.  ... 
doi:10.1109/jiot.2019.2916143 fatcat:jvvpggy245amfnqd4xalebqxle

Research directions in session-based and sequential recommendation

Dietmar Jannach, Bamshad Mobasher, Shlomo Berkovsky
2020 User modeling and user-adapted interaction  
Soon after, however, Hidasi et al. (2016) proposed the landmark GRU4REC method based on recurrent neural networks.  ...  Sequential recommendations for groups One more open challenge relevant to sequential recommendations is group-based sequential recommendation.  ...  His research areas include Web mining, Web personalization, and recommender systems.  ... 
doi:10.1007/s11257-020-09274-4 fatcat:tihp3ud43jfptothm47u5xehua

DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms

Yihan Ma, Hua Sun, Yang Chen, Jiayun Zhang, Yang Xu, Xin Wang, Pan Hui
2021 Journal of Social Computing  
To tackle the first challenge, DeepPredict involves users' historical records in all the cities and uses a deep learning based method to process them.  ...  In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel.  ...  [48] proposed Neural Attentive Recommendation Machine (NARM). NARM used attention-based neural network to process the sequential data. Chen et al.  ... 
doi:10.23919/jsc.2021.0004 fatcat:wvaq522hljaxtcij752lj3xcza

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

2020 KSII Transactions on Internet and Information Systems  
and prediction techniques like Recurrent Neural Network in Deep learning.  ...  Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects.  ...  Acknowledgement This research work is performed as part of the Ph.D work in the area of Context-Aware Recommender System. There is no funding source(s) involved in this research.  ... 
doi:10.3837/tiis.2020.02.004 fatcat:qeyhjdp57be6hgpoxbdxmuozji
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