POI recommendation based on a multiple bipartite graph network model

Lang, Chen; Wang, Ze; He, Kai; Sun, Shimin

doi:10.1007/s11227-021-04279-1

Cited by 12 publications

(3 citation statements)

References 45 publications

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“…At the same time, in terms of environmental conditions, artists can learn the connotation of Hui-style printmaking during its formation, pay attention to its potential elements in the development process, and ensure that the formation of Hui-style printmaking is more meaningful through continuous innovation and development. If we cause the formation of Hui-style printmaking and the development of Huizhou to connect closely, a complete guarantee mechanism can be built, and corresponding assistance to the cultural development of Huizhou can be provided [ 7 ]. Therefore, the author thinks that we can combine the geographical factors and natural conditions of the Huizhou area to analyze and understand the historical process of this area and make the tourists who pass through the Huizhou area fall in love with the Huizhou culture so as to ensure that they can fall in love with the quiet landscape and have the idea of living in this area for a long time.…”

Section: Research Significancementioning

confidence: 99%

[Retracted] Analysis of the Natural Conditions and Geographical Factors of the Formation of Hui‐Style Prints

2022

Journal of Environmental and Public Health

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In order to analyze the natural conditions and geographical factors of the formation of Hui-style prints, big data analysis technology is brought in, through which more audiences can understand the true meaning of Hui-style printmaking, the internal system of Hui-style printmaking during its operation can be improved, and artists can complete the analysis of natural conditions and geographical factors for the formation of Hui-style prints by means of empirical analysis. In this way, during the implementation of artistic acts, the benefits of the city and even the society can be increased, and the social and economic benefits can be improved accordingly and involved in information management systems directionally, and the natural conditions and geographical factors related to the formation of Hui-style prints can be deeply analyzed, so as to ensure that the formation of Hui-style prints has a social and economic promotion.

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Section: Research Significancementioning

confidence: 99%

[Retracted] Analysis of the Natural Conditions and Geographical Factors of the Formation of Hui‐Style Prints

2022

Journal of Environmental and Public Health

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“…The exploration for depth reflects the effect of the number of different aggregation iterations on the model performance. Specifically, we vary the number of propagation layers L in [1][2][3][4][5], and the results are shown in Table 3; through analysis, we can conclude that:…”

Section: Effect Of Model Depthmentioning

confidence: 96%

“…(2) Not targeted for pose of aggregating neighborhood characteristics. This can lead to many feature Existing POI recommendation methods can be divided into four categories according to method distinctions: Collaborative Filtering recommendation (CF) [3,4], Content-Based recommendation (CB) [5,6], social relationship-based recommendation [7,8], and network structure-based recommendation [9,10]. Existing methods tend to concentrate on using spatio-temporal context for POI recommendations, which focus more on temporal information, geographic information, and user social relationships in social networks, and use shallow interaction information such as clicks or check-ins as the basis for recommendations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Graph Convolutional Network for Fine-Grained and Personalized POI Recommendation

Zhang

Bai

et al. 2022

Electronics

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With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Point-Of-Interest (POI) recommendation system has been applied to many real-life scenarios, such as life services and autonomous driving. Specifically, the goal of POI recommendation is to recommend locations that match their personalized preferences to users. In existing POI recommendation methods, people tend to pay more attention to the impact of temporal and spatial factors of POI on users, which will alleviate the problems of data sparsity and cold start in POI recommendation. However, this tends to ignore the differences among individual users, and considering only temporal and spatial attributes does not support fine-grained POI recommendations. To solve this problem, we propose a new Fine-grained POI Recommendation With Multi-Graph Convolutional Network (FP-MGCN). This model focuses on the content representation of POIs, captures users’ personalized preferences using semantic information from user comments, and learns fine-grained representations of users and POIs through the relationships between content–content, content–POI, and POI–user. FP-MGCN employs multiple embedded propagation layers and adopts information propagation mechanisms to model the higher-order connections of different POI-related relations for enhanced representation. Fine-grained POI is finally recommended to users through the three types of propagation we designed: content–content information propagation, content–POI information propagation, and POI–user information propagation. We have conducted detailed experiments on two datasets, and the results show that FP-MGCN has advanced performance and can alleviate the data sparsity problem in POI recommendation tasks.

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Using personalized next session to improve session-based recommender systems

Chen,

Wu,

Shih

2023

J Supercomput

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In e-commerce, session-based personalized recommendation is still a challenging task due to the limited information of users in a session. Therefore, it is not enough to include only the user's local information. Instead, we should consider global information, i.e., extract information from all users' sessions to explore more collaborative information. However, using all session information will waste computing resources, and there will be a lot of information in the global information that is not relevant to the current user, which will damage the correctness of the prediction. Therefore, instead of referencing all global information, we select only those sessions that are useful to users, the so-called Personalized Next Session (PNS), as global information to improve the lack of local information. This work is the rst to adopt a deep network architecture study incorporating the concept of PNS to recommend the next item of the current session to the user. We have conducted experiments on several real-world datasets. Extensive experimental results show that our proposed deep network architecture outperforms state-of-the-art recommendation methods.

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POI recommendation based on a multiple bipartite graph network model

Cited by 12 publications

References 45 publications

[Retracted] Analysis of the Natural Conditions and Geographical Factors of the Formation of Hui‐Style Prints

[Retracted] Analysis of the Natural Conditions and Geographical Factors of the Formation of Hui‐Style Prints

Multi-Graph Convolutional Network for Fine-Grained and Personalized POI Recommendation

Using personalized next session to improve session-based recommender systems

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