Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photos

Lyu, Dandan; Chen, Ling; Xu, Zhenxing; Yu, Shujuan

doi:10.1007/s10489-019-01566-6

Cited by 27 publications

(21 citation statements)

References 43 publications

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“…Hence, the performance of the recommendation model was poor. Reference [14] proposed a personalized travel recommendation model based on weighted multi-information constraint matrix decomposition scheme, which comprehensively describes users and travel locations using photos, user access sequences, and text tags, and it allocates different weights in combination with the common access probability based on geographical distance, which can achieve better travel route recommendation. However, because of the traditional method, the recommendation performance is lower than that of the proposed model using deep learning.…”

Section: Influence Analysis Of Model Characteristicsmentioning

confidence: 99%

“…To demonstrate the recommended performance of the proposed model, it is compared with reference [9,11,14]. e results are shown in Table 2.…”

Section: Mse Analysis Of Different Modelsmentioning

confidence: 99%

“…from the actual route. Reference [14] uses the weighted multi-information constraint matrix decomposition method to realize personalized travel recommendation, which considers many factors, however, it lacks a powerful learning algorithm. erefore, the accuracy of the recommendation result is not high, and the MSE is 1.705.…”

Section: Proposed Modelmentioning

confidence: 99%

“…In this way, the best route recommendation under complex conditions is realized. Reference [14] proposed a personalized travel recommendation scheme based on a weighted multi-information constraint matrix factorization scheme.…”

Section: Introductionmentioning

confidence: 99%

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Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Huang¹

2022

Journal of Robotics

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Aiming at the problems that the traditional model is difficult to extract information features, difficult to learn deep knowledge, and cannot automatically and effectively obtain features, which leads to the problem of low recommendation accuracy, this paper proposes a personalized tourism route recommendation model of intelligent service robot using deep learning in a big data environment. Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. Finally, the processed data is input into the trained recommendation model to generate a personalized tourism route recommendation scheme. The experimental analysis of the proposed model based on Pytorch deep learning framework shows that its Pre@10, Rec@10 values are 88% and 83%, respectively, and the mean square error is 1.537, which are better than other comparison models and closer to the real tourist route of the tourists.

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Section: Influence Analysis Of Model Characteristicsmentioning

confidence: 99%

“…To demonstrate the recommended performance of the proposed model, it is compared with reference [9,11,14]. e results are shown in Table 2.…”

Section: Mse Analysis Of Different Modelsmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Huang¹

2022

Journal of Robotics

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“…At the same time, the shortest expected cruise distance was introduced into the model to improve the recommendation efficiency of the scheme [11]. Lyu et al believe that the existing travel recommendation system cannot deal with different information specifically, so they propose a weighted multi-information constraint matrix decomposition scheme for personalized travel location recommendation based on geographical tag photos, to achieve a comprehensive description of users' travel needs from the perspective of photos, user access sequences, text tags, and so on [12].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Modeling and Analysis of Multidimensional Hybrid Recommendation Algorithm in Tourism Itinerary Planning under the Background of Big Data

Hao¹,

Song

2021

Discrete Dynamics in Nature and Society

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Smart tourism can provide high-quality and convenient services for different tourists, and tourism itinerary planning system can simplify tourists’ tourism preparation. In order to improve the limitation of the recommendation dimension of traditional travel planning system, this paper designs a mixed user interest model on the premise of traditional user interest modeling and combines various attributes of scenic spots to form personalized recommendation of scenic spots. Then, it uses heuristic travel planning cost-effective method to construct the corresponding travel planning system for travel planning. In terms of the accuracy rate of travel planning recommendation, the accuracy rate of multidimensional hybrid travel recommendation algorithm is 0.984, and the missing rate is 0. When the travel cost and travel time are the same and the number of scenic spots is 20–30, the memory occupation of MH algorithm is only 1/2 of that of TM algorithm. The results show that the multidimensional hybrid travel recommendation algorithm can improve the personalized travel planning of users and the travel time efficiency ratio. The results of this study have a certain reference value in improving user satisfaction with the travel planning system and reducing user interaction.

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Context-and category-aware double self-attention model for next POI recommendation

Wang

Wan

et al. 2023

Appl Intell

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Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photos

Cited by 27 publications

References 43 publications

Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Dynamic Modeling and Analysis of Multidimensional Hybrid Recommendation Algorithm in Tourism Itinerary Planning under the Background of Big Data

Context-and category-aware double self-attention model for next POI recommendation

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