A Learning-Based POI Recommendation With Spatiotemporal Context Awareness

Chen, Yi Cheng; Thaipisutikul, Tipajin; Shih, Timothy K.

doi:10.1109/tcyb.2020.3000733

Cited by 38 publications

(15 citation statements)

References 50 publications

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“…The second limitation is that the limited experimental results in various related fields. In the future, we will design parallel methods to deal with massive data and apply our findings to various related fields to observe their robustness [34][35][36][37][38][39][40][41].…”

Section: Discussionmentioning

confidence: 99%

An Improved Evaluation Methodology for Mining Association Rules

Bao

Mao

Zhu

et al. 2021

Axioms

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At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application.

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Section: Discussionmentioning

confidence: 99%

An Improved Evaluation Methodology for Mining Association Rules

Bao

Mao

Zhu

et al. 2021

Axioms

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“…Moreover, several studies (Chen et al, 2022;Wang et al, 2018b;Zhang and Chow, 2016;Zhao et al, 2016) incorporate the spatial factor into matrix factorization. Wang et al (2018b) presented a novel clustered adversarial matrix factorization to exploit the underlying cluster structure of the spatial data in order to facilitate effective imputation.…”

Section: Temporal and Spatial Recommendationmentioning

confidence: 99%

“…Wang et al (2018b) presented a novel clustered adversarial matrix factorization to exploit the underlying cluster structure of the spatial data in order to facilitate effective imputation. Chen et al (2022) proposed a novel POI recommendation system to capture and learn the complicated sequential transitions by incorporating time and distance irregularity with dynamic decay values into the model learning process. Zhao et al (2016) proposed a spatial-temporal method to explicitly model the interactions using a pairwise tensor factorization framework.…”

Section: Temporal and Spatial Recommendationmentioning

confidence: 99%

A novel virtual-communicated evolution learning recommendation

Chen,

Chen

2023

IMDS

Self Cite

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PurposeIn this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.Design/methodology/approachA novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.FindingsA novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.Originality/valueBased on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

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“…The correlation between two consecutive POIs has rarely been used in previous studies. As reported in [18], Chen et al established a POI model to capture the sequence conversion relationship by the check-in time and distance information between two consecutive POIs. In the prediction process, the importance of each context was expressed by learning weights.…”

Section: General Poi Recommendationmentioning

confidence: 99%

Attention mechanism and adaptive convolution actuated fusion network for next POI recommendation

Qing

Zhang

et al. 2022

Int J of Intelligent Sys

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Nextpoint-of-interest (POI) recommendation has received widespread attention in recent years due to its superiority of recommending where users will go to next. However, there exist two limitations in many recommendation methods: (1) the hardness of modeling users' short-term preferences adaptively based on input sequence; (2) the efficient learning of the joint information between users' long-and short-term preferences. To this end, we propose an attention mechanism and adaptive convolution actuated fusion network (AMACF) innovatively, which optimizes forecast effectiveness of user preference. To better model some contextual information such as category, temporal, we utilize long-and short-term memory network to learn contextual features of POIs in historical check-ins and embed self-attention mechanism to capture users' preference in the long-term module. In the short-term module, for capturing the complicated interest, the adaptive convolution network (Ada-CN) is novelly proposed, which applies the attention mechanism to aggregate multiple parallel convolution kernels selectively and adjust to shortterm preference according to users' continuously updated check-ins. Furthermore, an attention-based fusion mechanism is designed to combine long-and

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A Learning-Based POI Recommendation With Spatiotemporal Context Awareness

Cited by 38 publications

References 50 publications

An Improved Evaluation Methodology for Mining Association Rules

An Improved Evaluation Methodology for Mining Association Rules

A novel virtual-communicated evolution learning recommendation

Attention mechanism and adaptive convolution actuated fusion network for next POI recommendation

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