Design and Implementation of the Culinary Recommendation System Using Sentiment Analysis and Simple Adaptive Weighting in Bengkulu, Indonesia

In 2017, the minister of Indonesia tourism stated that everyone who travels spends his time for culinary about 30-40%. The key point in increasing the tourism income; especially from the culinary sector is about how to inform and promote the wealth of Indonesia culinary to all travellers. The information system of Bengkulu tourism has been developed in the previous study. However, that system has not been able to provide the best culinary recommendations to the travellers. This study focuses on designing and implementing the recommendation system of Bengkulu culinary by using sentiment analysis and simple adaptive weighting (SAW). The recommendation offered is based on the user review and criteria as well. The user review will be classified into positive, negativeand neutral reviews by the sentiment analysis method. If the user needs culinary information based on criteria, the system will provide a recommendation and rank of culinary by using simple adaptive weighting. These criteria used are the average price,opening hours, facilities, distance from a central city, and transportation as well. Sentiment analysis method obtains the accuracy of recomendation classification at 79% while the recommendation rank obtained by the SAW method is 90.83%. These results show that the proposed method has a potential for assisting the travellers to gain the best culinary recommendation, especially in the Bengkulu area.

IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Weather and Climate Information for Tourism

The tourism sector is one of the largest and fastest growing global industries and is a significant contributor to national and local economies around the world. The interface between climate and tourism is multifaceted and complex, as climate represents both a vital resource to be exploited and an important limiting factor that poses risks to be managed by the tourism industry and tourists alike. All tourism destinations and operators are climate-sensitive to a degree and climate is a key influence on travel planning and the travel experience. This chapter provides a synopsis of the capacities and needs for climate services in the tourism sector, including current and emerging applications of climate services by diverse tourism end-users, and a discussion of key knowledge gaps, research and capacity-building needs and partnerships that are required to accelerate the application of climate information to manage risks to climate variability and facilitate successful adaptation to climate change

Weather and Climate Information for Tourism

The tourism sector is one of the largest and fastest growing global industries and is a significant contributor to national and local economies around the world. The interface between climate and tourism is multifaceted and complex, as climate represents both a vital resource to be exploited and an important limiting factor that poses risks to be managed by the tourism industry and tourists alike. All tourism destinations and operators are climate-sensitive to a degree and climate is a key influence on travel planning and the travel experience. This chapter provides a synopsis of the capacities and needs for climate services in the tourism sector, including current and emerging applications of climate services by diverse tourism end-users, and a discussion of key knowledge gaps, research and capacity-building needs and partnerships that are required to accelerate the application of climate information to manage risks to climate variability and facilitate successful adaptation to climate change

Weather and Climate Information for Tourism

The tourism sector is one of the largest and fastest growing global industries and is a significant contributor to national and local economies around the world. The interface between climate and tourism is multifaceted and complex, as climate represents both a vital resource to be exploited and an important limiting factor that poses risks to be managed by the tourism industry and tourists alike. All tourism destinations and operators are climate-sensitive to a degree and climate is a key influence on travel planning and the travel experience. This chapter provides a synopsis of the capacities and needs for climate services in the tourism sector, including current and emerging applications of climate services by diverse tourism end-users, and a discussion of key knowledge gaps, research and capacity-building needs and partnerships that are required to accelerate the application of climate information to manage risks to climate variability and facilitate successful adaptation to climate change

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations 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. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

Focussed palmtop information access combining starfield displays and profile-based recommendations

This paper presents two palmtop applications: Taeneb CityGuide and Taeneb ConferenceGuide. Both applications are centred around Starfield displays on palmtop computers - this provides fast, dynamic access to information on a small platform. The paper describes the applications focussing on this novel palmtop information access method and on the user-profiling aspect of the CityGuide, where restaurants are recommended to users based on both the match of restaurant type to the users' observed previous interactions and the rating given by reviewers with similar observed preferences