A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

Liu, Chunyang; Liu, Jiping; Xu, Shengyuan; Wang, Jian; Liu, Chao; Chen, Tianyang; Jiang, Tao

doi:10.3390/ijgi9020113

Cited by 11 publications

(6 citation statements)

References 42 publications

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“…Zhang et al [75] developed a recommendation system based on the embedding of users and POIs in conjunction with a long short-term memory network to derive user preferences based on their check-in sequence, before using a fully-connected neural network to evaluate candidate recommendations. Liu et al [76] developed the spatiotemporal dilated convolutional generative network for POI recommendation. Cao et al [30] developed a recommendation system based on edge computing using LBSN data.…”

Section: Recommendation Systems Using Data Acquired From Lbsnsmentioning

confidence: 99%

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Chen

Huang

Chiu

et al. 2021

IJGI

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Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city. This article proposes a framework to recommend Joint Promotion Partners using location-based social networks (LBSN) data. We considered six factors in determining the suitability of a partner (customer base, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. The effectiveness and efficiency of our algorithms were verified using the Foursquare dataset and real-life case studies.

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Section: Recommendation Systems Using Data Acquired From Lbsnsmentioning

confidence: 99%

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Chen

Huang

Chiu

et al. 2021

IJGI

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“…Jiang et al [18] proposed obtaining travel sequences from CCGPs by taking multiple attributes (e.g., time, cost, and tags) into account. Liu et al [19] used a generative method and convolutions network to model users' check-in sequences. Other typical methods have been used to recommend locations and sequences in a given geospatial area [20,21].…”

Section: Ccgp-based Travel Location Recommendationmentioning

confidence: 99%

A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

Ameen

Chen

et al. 2020

IJGI

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Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

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“…In addition to the above three types of methods, it is worth mentioning that neural networks have also been gradually applied in POI recommendations in recent years. For example, typical methods in existing research are based on Word2Vec [42,43], multilayer perceptron (MLP) [44,45], deep neural networks (DNNs) [46], recurrent neural networks (RNNs) [47,48], convolutional neural networks (CNNs) [49], and attention model [50,51].…”

Section: Related Workmentioning

confidence: 99%

“…We choose three widely used evaluation metrics, namely, Recall@k [6,16], F1-score@k [47,50], and NDCG@k [49], where k is the length of the recommendation list. Formally, the three metrics are formulated and defined as 8 Mathematical Problems in Engineering…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Other baseline parameters are determined with grid search and random search to ensure all baselines achieve their best performance [54]. In order to facilitate the comparison of all algorithms, the training set and the test set of the experimental data are divided in the same way as that in [12,49], i.e., leave-one-out evaluation. To be specific, for each user's check-in sequence, the last POI is the test data, and the other data are for training.…”

Section: Experiments Setupmentioning

confidence: 99%

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Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

Liu

Xin²,

Jian

et al. 2020

Mathematical Problems in Engineering

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Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

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A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

Cited by 11 publications

References 42 publications

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

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