Integrating spatial and temporal contexts into a factorization model for POI recommendation

Cai, Ling; Xu, Jing; Ju, Liu; Pei, Tao

doi:10.1080/13658816.2017.1400550

Cited by 46 publications

(20 citation statements)

References 59 publications

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“…some previous POI recommendation methods are user-based CF or item-based CF, which take advantage of check-ins of similar users or POIs [2,3,5,29]. A cutting-edge CF method is matrix factorization (MF) [4,6,12,30], which mining potential location preferences of a user by factorizing the observed user-POI matrix. To the best of our knowledge, many POI recommender systems integrate the geographical information [4][5][6]12,31], temporal information [2,[32][33][34][35], social information [1,4,36] or other POI characteristic information (reviews, categories, labels, etc.)…”

Section: Collaborative Filtering Based Methodsmentioning

confidence: 99%

“…A cutting-edge CF method is matrix factorization (MF) [4,6,12,30], which mining potential location preferences of a user by factorizing the observed user-POI matrix. To the best of our knowledge, many POI recommender systems integrate the geographical information [4][5][6]12,31], temporal information [2,[32][33][34][35], social information [1,4,36] or other POI characteristic information (reviews, categories, labels, etc.) [37][38][39][40] into traditional recommendation algorithms.…”

Section: Collaborative Filtering Based Methodsmentioning

confidence: 99%

“…Therefore, POI recommendation can not only meet user's personalized preferences for visiting new places but also help LBSNs service providers implement intelligent location-aware online advertising services [3,4]. The task of POI recommendations is investigated in different settings, such as general POI recommendation [5,6], out-of-town recommendation [7], next POI recommendation [3,8,9], tour recommendation for groups [10,11], and so on. In this work, we focus on next POI recommendation by modeling check-in sequences and incorporating spatiotemporal influence in a personalized way, this task is more significant since it can predict users' next movement behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, POI recommendation has been extensively studied from the academic and industrial fields [5][6][7][8][9][10][11][12][13]. POI recommendation is different from the general recommendation tasks (e.g., goods, movies, news recommendation) because there is highly spatiotemporal dependence.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Liu

Wang

et al. 2019

IJGI

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Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user's preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user's main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user's sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user's main behavioral intention in the user's check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

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Section: Collaborative Filtering Based Methodsmentioning

confidence: 99%

Section: Collaborative Filtering Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liu

Wang

et al. 2019

IJGI

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“…With the rapid development of location-based social networks (LBSN), location recommendation systems are widely applied [27,28]. Recent studies are focusing on exploring social influences and users' personal preferences on location through user social relationships and geospatial features [29,30]. To make large-scale online social networks accessible, researchers have conducted a comprehensive study of the spatial attributes of social networks [31].…”

Section: Introductionmentioning

confidence: 99%

Location Recommendation of Digital Signage Based on Multi-Source Information Fusion

Xie

Zhang

et al. 2018

Sustainability

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Abstract:With the increasing amount of digital signage and the complexity of digital signage services, the problem of introducing precise location recommendation methods for digital signage should be solved by digital signage enterprises. This research aims to provide a sustainable location recommendation model that integrates the spatial characteristics of geographic locations and multi-source feature data to recommend locations for digital signage. We used the outdoor commercial digital signage within the Sixth Ring Road area in Beijing as an example and combined it with economic census, population census, average house prices, social network check-in data, and the centrality of traffic networks that have an impact on the sustainable development of the regional economy as research data. The result shows that the proposed method has higher precision and recall in location recommendation, which indicates that this method has a better recommendation effect. It can further improve the recommendation quality and the deployment of digital signage. By this method, we can optimize resource allocation and make the economics and resources sustainable. The digital signage recommendation results of the Beijing City Sixth Ring Road indicated that the areas suitable for digital signage were primarily distributed in Wangfujing, Financial Street, Beijing West Railway Station, and tourist attractions in the northwest direction of the Fifth Ring Road. The research of this paper not only provides a reference for the integration of geographical features and their related elements data in a location recommendation algorithm but also effectively improves the science of digital signage layout, prompting advertising efforts to advance precision, personalization, low carbonization, and sustainable development.

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Adaptive group‐aware topic model for venue recommendation

Meng

Zhang

2022

Concurrency and Computation

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SummaryVenue recommendation plays a vital role in helping the user discovers interesting locations. However, most of the existing studies have never considered the case of multiple people visiting a venue together, such as inviting friends to dinner, so they ignore the influence of group preference on making decisions. Furthermore, the participants' influence on the inviter's decision is also affected by participation time, venue category, and visiting sequence. In this article, we propose an adaptive group‐aware topic model (AGATM) to provide TOP‐K venue recommendations. The model considers the individual's personalized preferences and group preferences at different times to provide venue recommendations by simulating the process of making decisions for venues. We conduct extensive experiments on two real datasets to evaluate the performance of our proposed method. The experimental results show that our model outperforms existing benchmark algorithms.

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Integrating spatial and temporal contexts into a factorization model for POI recommendation

Cited by 46 publications

References 59 publications

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

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

Location Recommendation of Digital Signage Based on Multi-Source Information Fusion

Adaptive group‐aware topic model for venue recommendation

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