UFC: A Unified POI Recommendation Framework

Zhou, Jiajun; Bo, Liu; Chen, Yaofeng; Lin, Fuqiang

doi:10.1007/s13369-019-04011-5

Cited by 8 publications

(11 citation statements)

References 24 publications

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“…UFC [18]: UFC is a combination algorithm that integrates user preference, check-in correlation, and friend importance. UFC incorporates three essential items, and with the collaboration filtering technique, user preference is personalized.…”

Section: Resultsmentioning

confidence: 99%

“…The considerable accuracy from RecPOID was assessed using Precision, and Recall that metrics are outlined as follows [18]:…”

Section: Performance Metricsmentioning

confidence: 99%

“…Moreover, to reduce data sparsity within POI recommendation systems, categorical, geographical, and co-visiting influences have been exploited. The purpose of the study by Zhou et al [18] is to obtain a predictive score for each user of any POI. This overall score is the individual prediction score by factors such as user preference modeling, importance of friend modeling, and check-in correlation modeling.…”

Section: Introductionmentioning

confidence: 99%

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RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Safavi

Jalali

2021

Future Internet

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In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.

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Section: Resultsmentioning

confidence: 99%

“…The considerable accuracy from RecPOID was assessed using Precision, and Recall that metrics are outlined as follows [18]:…”

Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Safavi

Jalali

2021

Future Internet

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“…The remarkable correctness of the DeePOF technique was estimated using Precision and Recall. These criteria are outlined as follows 46 :

\left\{\begin{array}{c}\mathrm{Precision}@k=\frac{1}{\mid P\mid}\times \sum \limits_{i=j}^U\frac{\left| TopK\left({P}_j\right)\cap {L}_j\right|}{\left| TopK\left({U}_j\right)\right|}\\ {}\mathrm{Recall}@k=\frac{1}{\mid P\mid}\times \sum \limits_{j=1}^U\frac{\mid TopK\left({U}_j\right)\cap {L}_j\mid }{\mid {L}_j\mid}\end{array}\right.

where

TopK\left({U}_j\right)

depicts the highest K suggested POIs in the test samples pursuant to distinct techniques,

{L}_j

determines the POIs that the j th client has seen in the training samples and K indicates the various range (5–50) of suggested POIs to investigate the potency of models.…”

Section: Methodsmentioning

confidence: 99%

“…The remarkable correctness of the DeePOF technique was estimated using Precision and Recall. These criteria are outlined as follows 46 :…”

Section: Performance Metricsmentioning

confidence: 99%

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Safavi

Jalali

2022

Concurrency and Computation

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Today, millions of active users spend a percentage of their time on location-based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends' behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location-based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean-shift clustering technique is used to detect similarity. The most similar friends' spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top-k POIs outperforms state-of-the-art approaches.

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An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

Mei

et al. 2022

Complex Intell. Syst.

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With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.

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UFC: A Unified POI Recommendation Framework

Cited by 8 publications

References 24 publications

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

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