Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

Zeng, Jun; Feng, Li; He, Xin; Wen, Junhao

doi:10.4018/ijwsr.2019100103

Cited by 14 publications

(5 citation statements)

References 18 publications

(4 reference statements)

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“…Many scholars have studied the influence of geographic factors on POI recommendations [24][25][26][27]. Ye et al [28] analyzed the spatial aggregation of user check-in behaviors and proposed a power-law relationship between the probability of user access and the distance.…”

Section: The Influence Of Geographic Factorsmentioning

confidence: 99%

Relational POI recommendation model combined with geographic information

Wei

et al. 2022

PLoS ONE

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Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.

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Section: The Influence Of Geographic Factorsmentioning

confidence: 99%

Relational POI recommendation model combined with geographic information

Wei

et al. 2022

PLoS ONE

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“…Collaborating Filtering is a technique that has often been used for recommender systems [93][94][95]. The essential procedure is that if users have similar preferences about items, they probably display similar preferences about other items.…”

Section: Poi Recommendation Systems Based On a Collaborative Filterin...mentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.

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“…Traditional personalized recommendation methods are widely migrated and applied to POI recommendations. Scholars have proposed POI recommendation methods based on collaborative filtering technology [9][10][11][12], content-based POI recommendation methods [13][14][15], location-based social network POI recommendation methods [16][17][18][19], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

Mei

2021

Mathematics

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The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.

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Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

Cited by 14 publications

References 18 publications

Relational POI recommendation model combined with geographic information

Relational POI recommendation model combined with geographic information

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

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