A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

Meng, Xiangfu; Fang, Jinfeng

doi:10.1109/access.2020.3045442

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…However, it does not take into account the attractions' own characteristics and tourists' interests. Meng et al [10] used a spectral clustering method to cluster attractions and used a Bayesian matrix decomposition model to calculate their interaction probabilities with visitors to generate a recommendation list. This method does not take into account the attraction sequence characteristics, resulting in a single recommendation result.…”

Section: Introductionmentioning

confidence: 99%

Attraction Recommendation Based on Tourism Context Modeling and Multi‐neural Collaborative Filtering Algorithm

Zhang

Wang

Fei

et al. 2023

IEEJ Transactions Elec Engng

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The key to generating tailored suggestions in the scenic location recommendation scene is how to model the diverse tourism environments in order to correctly acquire visitor preferences and scenic spot tourism features. However, most existing recommendation algorithms focus on the spatiotemporal background modeling of historical tourism trajectories. Only tourists' preferences are understood, and the rich heterogeneous tourism information such as tourists' personal tourism constraints and scenic spots' tourism attributes are ignored. In this paper, we proposed a multiple neural collaborative filtering attraction recommendation architecture (MNCF-AR). To begin, we learn the tourism feature representation of tourists by modeling diverse tourism contexts, and then we create the tourism trajectory background of tourists using a large number of actual tourism logs to achieve the entire feature representation of tourists. Second, to learn the feature vector of scenic spots in the context, the tourism heterogeneous network map is used to build the scenic spot attribute background, the self-attention network to learn the scenic spot sequence learning, and a neural network to project each scenic spot into a unified potential feature space. Finally, the multi-neural collaborative filtering method is utilized to forecast the difference in scores between visitors and scenic sites so that tailored scenic spots may be recommended. Extensive experiments on mainstream datasets, such as MaFengwo, New York, Tokyo and Xi'an, show that the proposed method can effectively and accurately recommend attractions for users.

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Section: Introductionmentioning

confidence: 99%