A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning
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Summary
The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user’s feedbacks and additional contextual information and outperforms current artificial neural network methods and combinations of case based reasoning with -nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.
- Type
- article
- Published
- 2017-09-10
- Cited by
- 27
- References
- 57
- Access
- Open access
- OpenAlex
- https://openalex.org/W2751087670
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:27266361
Keywords
Computer science, Recommender system, Context (archaeology), Artificial neural network, Cold start (automotive)
References
- ExpertClerk: Navigating Shoppers Buying Process with the Combination of Asking and Proposing
- Hybrid Recommender Systems: Survey and Experiments
- Travel recommendations in a mobile tourist information system
- Topic based context-aware travel recommendation method exploiting geotagged photos
- Understanding and Using Context
- Mobile recommender systems in tourism
- A weight-aware recommendation algorithm for mobile multimedia systems
- SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities
- Intelligent tourism recommender systems: A survey
- Context relevance assessment and exploitation in mobile recommender systems
- Recommender systems survey
- A recommender mechanism based on case-based reasoning
- Short term stock selection with case-based reasoning technique
- On planning sightseeing tours with TripBuilder
- Recommender systems based on user reviews: the state of the art
- Research of new strategies for improving CBR system
- A survey on mobile tourism Recommender Systems
- The search for knowledge, contexts, and Case-Based Reasoning
- Building and evaluating a location-based service recommendation system with a preference adjustment mechanism
- iTravel: A recommender system in mobile peer-to-peer environment
Cited by
- Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction
- Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems
- Context-aware tourism technologies
- The MOM of context-aware systems: A survey
- Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism
- A MULTI CRITERIA RECOMMENDATION MODEL FOR JAUNT
- A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system
- Generating Training Dataset of Machine Learning Model for Context-Awareness in a Health Status Notification Service
- Context Aware Recommendation Systems: A review of the state of the art techniques
- A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph
- Blockchain-Based Data Sharing for Decentralized Tourism Destinations Recommendation System
- A store location-based recommender system using user’s position and web searches
- A Two-Stage Neural Network-Based Cold Start Item Recommender
- A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions
- A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces
- Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts
- Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview
- Destinations Ratings Based Multi-Criteria Recommender System for Indonesian Halal Tourism Game
- NPR-LBN: next point of interest recommendation using large bipartite networks with edge and cloud computing
- Automatic Real-Time Adaptation of TrainingSession Difficulty Using Rules andReinforcement Learning in the AI-VT ITS
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