VCG: Exploiting visual contents and geographical influence for Point-of-Interest recommendation

Zhang, Zhibin; Zou, Cong; Ding, Ruifeng; Chen, Zhenzhong

doi:10.1016/j.neucom.2019.04.079

Cited by 20 publications

(15 citation statements)

References 9 publications

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“…There were two major ways to model geographical distance constraints in previous studies. One is to establish a simple inverse relationship between user's preference and geographical distance among locations, for instance, the power-law function [20,21], the Gaussian Model [22,23], and other reverse functions [24]. The other is to set a cutoff distance, and those locations whose distance from the current visiting location is larger than the cutoff distance would be filtered [15,19].…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers leveraged Scale-Invariant Feature Transform (SIFT) or color histograms to extract visual information [33,34], but these hand-crafted features limit the accuracy of visual information extraction to a great extent. The rise of the Convolutional neural network greatly improves visual information representation and has been applied in recommendation methods with visual content [21,35]. However, the imbalance of the number of photos in each tourist attraction and the noise and redundancy in photos still affect visual information's representativeness.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

Han

Liu

Chen

et al. 2021

IJGI

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The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

Han

Liu

Chen

et al. 2021

IJGI

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“…However, the ability to express local features will be slightly worse [17]. Therefore, the reference [18] proposed a unified framework called VCG to enhance POI recommendation, incorporating visual content and geographic influence into LBSN. The test results show that the proposed method has strong effectiveness.…”

Section: Related Researchmentioning

confidence: 99%

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Wang

Lü

2021

Complex Intell. Syst.

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Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

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“…For selecting better representative images, some filtering preprocess also attempted in previous studies. Most of them target to filter images with humans, by either applying a sophisticated library (such as OpenCV) [8] or training a deep learning model for image classification [5]. However, all of the previous studies conducted the undifferentiated filter process, which may fail to find the representative image for some tourist attractions.…”

Section: Representative Image Selectionmentioning

confidence: 99%

“…It can provide informative descriptions about the tourist attractions [2]. Furthermore, it can be applied in building touristic information systems [3] and generating tourist maps [4], as well as providing image content to some content-based tourist recommendations [5]. With the prevalence of image-based content sharing platforms, more and more researchers are inclined to extract tourist attractions from such platforms.…”

Section: Introductionmentioning

confidence: 99%

Extracting Representative Images of Tourist Attractions from Flickr by Combining an Improved Cluster Method and Multiple Deep Learning Models

Han

Ren

et al. 2020

IJGI

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Extracting representative images of tourist attractions from geotagged photos is beneficial to many fields in tourist management, such as applications in touristic information systems. This task usually begins with clustering to extract tourist attractions from raw coordinates in geotagged photos. However, most existing cluster methods are limited in the accuracy and granularity of the places of interest, as well as in detecting distinct tags, due to its primary consideration of spatial relationships. After clustering, the challenge still exists for the task of extracting representative images within the geotagged base image data, because of the existence of noisy photos occupied by a large area proportion of humans and unrelated objects. In this paper, we propose a framework containing an improved cluster method and multiple neural network models to extract representative images of tourist attractions. We first propose a novel time- and user-constrained density-joinable cluster method (TU-DJ-Cluster), specific to photos with similar geotags to detect place-relevant tags. Then we merge and extend the clusters according to the similarity between pairs of tag embeddings, as trained from Word2Vec. Based on the clustering result, we filter noise images with Multilayer Perceptron and a single-shot multibox detector model, and further select representative images with the deep ranking model. We select Beijing as the study area. The quantitative and qualitative analysis, as well as the questionnaire results obtained from real-life tourists, demonstrate the effectiveness of this framework.

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VCG: Exploiting visual contents and geographical influence for Point-of-Interest recommendation

Cited by 20 publications

References 9 publications

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Extracting Representative Images of Tourist Attractions from Flickr by Combining an Improved Cluster Method and Multiple Deep Learning Models

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