Mou et al., 2022 - Google Patents
Personalized tourist route recommendation model with a trajectory understanding via neural networksMou et al., 2022
View PDF- Document ID
- 5571924140847001788
- Author
- Mou N
- Jiang Q
- Zhang L
- Niu J
- Zheng Y
- Wang Y
- Yang T
- Publication year
- Publication venue
- International Journal of Digital Earth
External Links
Snippet
Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features, for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by …
- 230000001537 neural 0 title abstract description 52
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30241—Information retrieval; Database structures therefor; File system structures therefor in geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Islam et al. | A survey on deep learning based Point-of-Interest (POI) recommendations | |
Liu et al. | An attention‐based category‐aware GRU model for the next POI recommendation | |
Zheng et al. | A survey of location prediction on twitter | |
Mou et al. | Personalized tourist route recommendation model with a trajectory understanding via neural networks | |
Bao et al. | A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media | |
Liu et al. | Exploiting geographical-temporal awareness attention for next point-of-interest recommendation | |
Li et al. | Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks | |
Wan et al. | A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks | |
Palumbo et al. | Predicting Your Next Stop-over from Location-based Social Network Data with Recurrent Neural Networks. | |
Robertson et al. | Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts | |
Chen et al. | Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility | |
Wang et al. | Urban traffic flow prediction: A dynamic temporal graph network considering missing values | |
Zhou et al. | Improving human mobility identification with trajectory augmentation | |
Liu et al. | POI Recommendation Method Using Deep Learning in Location‐Based Social Networks | |
Zeng et al. | A next location predicting approach based on a recurrent neural network and self-attention | |
Chen et al. | KE-CNN: A new social sensing method for extracting geographical attributes from text semantic features and its application in Wuhan, China | |
Timokhin et al. | Predicting venue popularity using crowd-sourced and passive sensor data | |
Yang et al. | ST-FVGAN: filling series traffic missing values with generative adversarial network | |
Liu et al. | Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data | |
Gan et al. | Mining multiple sequential patterns through multi-graph representation for next point-of-interest recommendation | |
Yu et al. | Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes | |
Qian et al. | Vehicle trajectory modelling with consideration of distant neighbouring dependencies for destination prediction | |
Shen et al. | Novel model for predicting individuals’ movements in dynamic regions of interest | |
Liang | Intelligent Tourism Personalized Recommendation Based on Multi‐Fusion of Clustering Algorithms | |
Xu et al. | Hierarchical temporal–spatial preference modeling for user consumption location prediction in Geo-Social Networks |