Hybrid group recommendations for a travel service

Pessemier, Toon De; Dhondt, Jeroen; Martens, Luc

doi:10.1007/s11042-016-3265-x

Cited by 40 publications

(25 citation statements)

References 32 publications

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“…Several hybrid recommendation systems apply contentbased, collaborative, and other technique(s) into recommendation [3], [62], [63]. Pessemier et al [63] proposed a hybrid travel recommender system, which merged content-based, collaborative, knowledge-based solution for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination.…”

Section: ) Hybrid Methodsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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Section: ) Hybrid Methodsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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“…The most used technology is collaborative filtering, which can be applied on various occasions and can achieve excellent results. Fig 1 shows that the system is divided into three parts: user, object, and algorithm [ 18 ]. The first part identifies the user’s interests and hobbies from information such as consumption data, search data, and browse data.…”

Section: Methodsmentioning

confidence: 99%

Applying Internet information technology combined with deep learning to tourism collaborative recommendation system

Wang

2020

PLoS ONE

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Recently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate sparse tourism information on the Internet, thereby providing more convenient, faster, and more personalized tourism services. Based on the shortcomings of the traditional tourism recommendation system, a deep learning-based classification processing method of tourism product information is proposed. This method uses word embedding in the data preprocessing stage. The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. The Deep Neural Network (DNN) is used to process the necessary information of users and tourism service items. Also, factorization machine technology is used to learn the interaction between the extracted features to improve the prediction model. The results show that the proposed model can maintain an excellent precision of 64.2% when generating personalized recommendation lists for users. The sensitivity and accuracy of the recommendation list are better than other algorithms. By adding DNN, the word embedding method, and the factorization machine model, the precision is improved by 30%, 33.3%, and 40%, respectively. The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. The results have enriched and developed the theory of tourism service supply chain, providing a reference for constructing a personalized tourism service system.

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“…It has also been used to study specialized recommendation problems, including cold start [13] and reversible machine learning [3]. Its algorithms have been used to recommend books [21], tourist destinations [22], videos [24], and a number of other item types.…”

Section: Researchmentioning

confidence: 99%

The LKPY Package for Recommender Systems Experiments

Ekstrand¹

2018

Computer Science Faculty Publications and Presentations

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Since 2010, we have built and maintained LensKit, an open-source toolkit for building, researching, and learning about recommender systems. We have successfully used the software in a wide range of recommender systems experiments, to support education in traditional classroom and online settings, and as the algorithmic backend for user-facing recommendation services in movies and books. This experience, along with community feedback, has surfaced a number of challenges with LensKit's design and environmental choices. In response to these challenges, we are developing a new set of tools that leverage the PyData stack to enable the kinds of research experiments and educational experiences that we have been able to deliver with LensKit, along with new experimental structures that the existing code makes difficult. The result is a set of research tools that should significantly increase research velocity and provide much smoother integration with other software such as Keras while maintaining the same level of reproducibility as a LensKit experiment. In this paper, we reflect on the LensKit project, particularly on our experience using it for offline evaluation experiments, and describe the next-generation LKPY tools for enabling new offline evaluations and experiments with flexible, open-ended designs and well-tested evaluation primitives.

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Hybrid group recommendations for a travel service

Cited by 40 publications

References 32 publications

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Applying Internet information technology combined with deep learning to tourism collaborative recommendation system

The LKPY Package for Recommender Systems Experiments

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