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

Xu, Jing; Xiao, Yingyuan; Zheng, Wenguang; Xu, Lei; Wu, Hui

doi:10.1109/access.2019.2950927

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…These techniques tried to represent every user and POI into latent vector space which was learned from observed user-item matrix. Then the recommendation is provided based on the similarity between users and POIs [31], [32]. For calculating similarity, various methods like Euclidean Distance, Cosine similarity, and Pearson similarity were used.…”

Section: A Collaborative Filtering Based Methodsmentioning

confidence: 99%

CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation

et al. 2022

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Point-of-interest (POI) recommendation system that tries to anticipate user's next visiting location has attracted a plentiful research interest due to its ability in generating personalized suggestions. Since user's historical check-ins are sequential in nature, Recurrent neural network (RNN) based models with context embedding shows promising result for modeling user's mobility. However, such models can not provide correlation between non-consecutive and non-adjacent visits for understanding user's behavior. To mitigate data sparsity problem, many models use hierarchical gridding of the map which cannot represent spatial distance smoothly. Another important factor while providing POI recommendation is the impact of weather condition which has rarely been considered in the literature. To address the above shortcomings, we propose a Context-Aware Recency based Attention Network (CARAN) that incorporates weather condition with spatiotemporal context and gives focus on recently visited locations using the attention mechanism. It allows interaction between non-adjacent check-ins by using spatiotemporal matrices and uses linear interpolation for smooth representation of spatial distance. Moreover, we use positional encoding of the check-in sequence in order to maintain relative position of the visited locations. We evaluate our proposed model on three real world datasets and the result shows that CARAN surpasses the existing state-of-the art models by 7-14%.

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Section: A Collaborative Filtering Based Methodsmentioning

confidence: 99%

CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation

et al. 2022

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“…In order to evaluate the application effect of the scheme proposed in this paper in the location recommendation service, the evaluation indexes commonly used in the recommendation system were selected in the experiment: Precision , Recall and F-Score [ 34 ]. Then, in order to analyze the efficiency, the variation of the algorithm operation time is shown when the number of location recommendations is different.…”

Section: Performance Evaluationsmentioning

confidence: 99%

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Zhang

Ding

et al. 2022

Entropy

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The publication of trajectory data provides critical information for various location-based services, and it is critical to publish trajectory data safely while ensuring its availability. Differential privacy is a promising privacy protection technology for publishing trajectory data securely. Most of the existing trajectory privacy protection schemes do not take into account the user’s preference for location and the influence of semantic location. Besides, differential privacy for trajectory protection still has the problem of balance between the privacy budget and service quality. In this paper, a semantics- and prediction-based differential privacy protection scheme for trajectory data is proposed. Firstly, trajectory data are transformed into a prefix tree structure to ensure that they satisfy differential privacy. Secondly, considering the influence of semantic location on trajectory, semantic sensitivity combined with location check-in frequency is used to calculate the sensitivity of each position in the trajectory. The privacy level of the position is classified by setting thresholds. Moreover, the corresponding privacy budget is allocated according to the location privacy level. Finally, a Markov chain is used to predict the attack probability of each position in the trajectory. On this basis, the allocation of the privacy budget is further adjusted and its utilization rate is improved. Thus, the problem of the balance between the privacy budget and service quality is solved. Experimental results show that the proposed scheme is able to ensure data availability while protecting data privacy.

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“…▪ ESMP [11] : this method also explores users' activity regions, and the Manshift algorithm is employed to determine the regions. The final recommendation results are achieved also by a CF-based method.…”

Section: Datasetsmentioning

confidence: 99%

An Adaptive Collaborative Filtering Based Approach for Point-of-Interest Recommendations

Wang¹,

Chen²,

Chen³

2022

Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2

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LBSNs (Location-based Social Networks) provide abundant information for users to browse and explore the places where they are interested in, named POI (Point-of-Interest). However, such large amount of check-in records in LBSNs cause information overload problem and increase difficulty for users to find the really desire POIs. POI recommendation systems can be employed to solve this problem. Most traditional POI recommendation methods are CF (Collaborative Filtering) based and achieve recommendations for a particular user according to check-in records of his similar users. In this paper, we propose an adaptive CF based algorithm to achieve POI recommendations for users, considering their personalized activity regions. Compare with existing algorithm, our algorithm does not construct user-POI matrix by using the entire historical records. Instead, we first explore users' activity regions and construct more personalized user-POI matrix for each particular user according to corresponding activity regions. Besides, we propose a method to dynamically determine the number of similar users for a certain user, instead of using a fix number for all users, leading to more personalized recommendations. We have implemented our POI recommendation system and compared with state-of-theart methods by using Foursquare dataset. The experimental results show that our POI recommendation system achieves better performance than all these compared approaches.

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

Cited by 16 publications

References 37 publications

CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation

CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

An Adaptive Collaborative Filtering Based Approach for Point-of-Interest Recommendations

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