Current location-based next POI recommendation

Oppokhonov, Shokirkhon; Park, Seyoung; Ampomah, Isaac K. E.

doi:10.1145/3106426.3106528

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…(1) CLB: Current location-based recommendation proposes a model that captures sequential influence and geographical influence. This model finds the user's current location and then recommends new POIs based on collaborative filtering [18].…”

Section: Evaluated Methodsmentioning

confidence: 99%

“…Feng et al propose a POI2vec algorithm to jointly learn the latent representation for users and POIs, and then capture users' preference and POI sequence to improve the accuracy [17]. Oppokhonov et al find the user's current location and then recommends new POIs based on collaborative filtering [18]. Since users' preferences are changing with the time going on and the check-in data exhibits users' latent states, although the above studies have considered the sequential influence, these studies have not yet well studied the latent states and users' transition patterns hidden in the check-in data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Providing privacy preserving in next POI recommendation for Mobile edge computing

Kuang

Zhang

et al. 2020

J Cloud Comp

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Point of interest (POI) recommendation can benefit users and merchants. It is a very important and popular service in modern life. In this paper, we aim to study the next new POI recommendation problem with the consideration of privacy preserving in edge computing. The challenge lies in capturing the transition patterns between POIs precisely and meanwhile protecting users' location. In this paper, first, we propose to model users' check-in sequences with their latent states based on HMM, and EM algorithm is used to estimate the parameters of the model. Second, we propose to protect users' location information by a weighted noise injection method. Third, we predict users' next movement according to his current location based on Forward algorithm. Experimental results on two large-scale LBSNs datasets show that our proposed model without noise injection can achieve better recommendation accuracy than several state-of-the-art techniques, and the proposed weighted noise injection approach can achieve better performance on privacy preserving than traditional one with a little cost on accuracy.

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Section: Evaluated Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Providing privacy preserving in next POI recommendation for Mobile edge computing

Kuang

Zhang

et al. 2020

J Cloud Comp

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show abstract

“…He et al [28] propose a spatial-temporal topic model (STM), which embedded the temporal and spatial patterns in users chick-in activities. Oppokhonov et al [29] develop a recommendation system based on a directed graph. The algorithm of the system considers both the temporal factor and the distance for recommending a new POI for next hours.…”

Section: Related Workmentioning

confidence: 99%

“…In the real world, users' behaviors usually happen in succession, and the next action is often related to the previous one. In recent years, studies have focused on various sequential recommendation tasks, such as next POI recommendation [29,36]. Early studies were typically based on the Markov chain models for sequential recommendation [32,36].…”

Section: Sequential Poi Recommendationmentioning

confidence: 99%

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

2021

Wireless Communications and Mobile Computing

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The POI recommendation system has become an important means to help people discover attractive and interesting places. Based on our data analysis, we observe that users pay equal attention to conservatism and curiosity. In particular, adopting analysis corresponding to different time intervals, we find that users lean towards old POIs in the short term and look for new POIs with the increase of the time interval. However, existing approaches usually neglect users’ conservatism and curiosity preferences. Therefore, they are confronted with a bottleneck of depicting accurate user needs, making it difficult to improve the recommendation performance further. Besides, we further find that the number of user daily check-ins has uneven distribution, which is not conducive to capture the accurate transition patterns of user behaviors. In light of the above, we design a single POI sequential method. On this basis, we propose a recommendation method of the variable additive Markov chain. We consider the user sequential preferences, especially liking old and pursuing new features. In addition, our model exploits the geographical tendency of user behaviors. Finally, we conduct abundant experiments on four cities in the two real datasets, i.e., Foursquare and Jiepang. The experimental results show its superiority over other competitors.

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“…The recommendation methods based on collaborative filtering [10], [20], [26]- [28] are mainly divided into user-based collaborative filtering and location-based collaborative filtering. The POI recommendation system uses the collaborative filtering method mainly into the following three steps: generating recommendation candidate sets, calculating similarity and calculating recommendation scores of POIs.…”

Section: Related Workmentioning

confidence: 99%

Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation

Xiao

Zheng

et al. 2019

IEEE Access

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In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits the business in real life. Although a personalized POI recommendation system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model human mobility and the difficulty to address data sparsity. Moreover, geographical influence on users should be personalized, but current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we design a novel and effective personalized POI recommendation system. First, our system mines the target user's active area based on his or her check-in history, and designs a personalized user spatial similarity calculation method based on the target user's active area. Secondly, our system takes into account three features of the human mobility pattern: spatial, temporal, and sequential properties. Furthermore, our system designs a novel personalized user mobility pattern similarity calculation method based on the features of human mobility pattern. Finally, a recommendation list is generated based on the idea of collaborative filtering. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance. INDEX TERMS POI recommendation, user active area, user mobility pattern, the minimum enclosing circle. I. INTRODUCTION

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Current location-based next POI recommendation

Cited by 10 publications

References 21 publications

Providing privacy preserving in next POI recommendation for Mobile edge computing

Providing privacy preserving in next POI recommendation for Mobile edge computing

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation

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