A point of interest recommendation method using user similarity

Zeng, Jun; He, Xin; Li, Yinghua; Wen, Junhao; Zhou, Wei

doi:10.3233/web-180376

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The preference similarity that shows users' similarity in terms of their preference in visited POIs is calculated based on the cosine similarity of user-location check-in data. Zeng et al [79] consider creating vectors representing user check-ins in 24-hour time-slots. They consecutively calculate the user similarities by measuring the cosine similarity of these vectors.…”

Section: Social Information (S)mentioning

confidence: 99%

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

Rahmani

Aliannejadi

Baratchi

et al. 2022

ACM Trans. Inf. Syst.

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As the popularity of Location-based Social Networks (LBSNs) increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user’s main activity location, and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this paper, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.

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Section: Social Information (S)mentioning

confidence: 99%

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

Rahmani

Aliannejadi

Baratchi

et al. 2022

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

show abstract

Section: Social Information (S)mentioning

confidence: 99%

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

Rahmani¹,

Aliannejadi²,

Baratchi³

et al. 2022

Preprint

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As the popularity of Location-based Social Networks (LBSNs) increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user's main activity location, and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation.However, previous research does not consider the impact of the combination of these different factors. In this paper, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.

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“…With the rapid development of electronic maps and mobile communication technologies, the demands for location-based services have progressively increased [1]. Geographic spatial data represented by points of interest (POIs) has received increasing attention [2][3][4][5]. At present, improving data richness and quality with complementary attributes through the fusion of POIs from different sources has become an effective way to rapidly update POI data [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Different Sourcing Point of Interest Matching Method Considering Multiple Constraints

Liu

Dai

et al. 2020

IJGI

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Point of interest (POI) matching is critical but is the most technically difficult part of multi-source POI fusion. The accurate matching of POIs from different sources is important for the effective reuse of POI data. However, the existing research on POI matching usually adopts weak constraints, which leads to a low POI matching accuracy. To address the shortcomings of previous studies, this paper proposes a POI matching method with multiple determination constraints. First, according to various attributes (name, class, and spatial location), a new calculation model considering spatial topology, name role labeling, and bottom-up class constraints is established. In addition, the optimal threshold values corresponding to the different attribute constraints are determined. Second, according to the multiattribute constraint values and optimal thresholds, a constraint model with multiple strict determination constraints is proposed. Finally, actual POI data from Baidu Map and Gaode Map in Dongying city is used to validate the method. Comparing to the existing method, the accuracy and recall of the proposed method increase 0.3% and 7.1%, respectively. The experimental results demonstrate that the proposed POI matching method attains a high matching accuracy and high feasibility.

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A point of interest recommendation method using user similarity

Cited by 3 publications

References 12 publications

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

Different Sourcing Point of Interest Matching Method Considering Multiple Constraints

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