An Automatic Scenario Control in Serious Game to Visualize Tourism Destinations Recommendation
22 Jun 2021-IEEE Access (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 9, pp 89941-89957
TL;DR: In this paper, an automatic scenario control system is proposed to visualize travel recommendation scenarios choice according to the player's expectations of potential tourism destinations criteria, and the test results show that Automatic Scenario Control generates a preference value for each alternative as a reference for choosing tourism destination scenarios for the player.
Abstract: In the field of tourism, serious games are a pedagogical media application that helps players develop travel knowledge and expertise based on game content. A tourism serious game requires a scenario control system to visualize an attractive travel scenario. This paper proposes an Automatic Scenario Control in the serious game to visualize travel recommendation scenarios choice according to the player’s expectations of potential tourism destinations criteria. There are two stages in system development, namely scenario design and scenario selection. In the scenario design stage, we use the Hierarchical Finite State Machine to translate challenge-based stories according to the type of attraction. While at the scenario selection stage, Dynamic Weight Topsis is a method for selecting one of the player’s recommended scenarios. This study uses tourism destinations recommendations as to alternative variables, characteristics of tourism destinations as criteria, and players’ expectations of tourism destinations’ characteristics as weight criteria. In the implementation phase, the tourism serious game uses the content of tourism destinations in Mojokerto Indonesia. The test results show that Automatic Scenario Control generates a preference value for each alternative as a reference for choosing tourism destination scenarios for the player. Three things affect the scenario choice results, including the choice of month of tourist visits, player expectations of tourist destinations, and alternative input from the recommender system.
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TL;DR: In this article , the authors proposed a recommendation system to support knowledge sources in the Indonesian halal tourism game, where they used destinations ratings-based multi-criteria recommender system (MCRS) to generate recommendation rankings as a reference for visualizing halal travel for players as potential tourists.
Abstract: : Halal tourism is one of the tourism products that have the prospect of contributing to economic growth in Indonesia. Therefore, the government needs to increase promotions to increase tourist interest in halal tourism destinations in Indonesia. Game is one of the alternative promotional media that can also function as an educational medium for choosing halal tourism that is fun for potential tourists. This study proposes a recommendation system to support knowledge sources in the Indonesian halal tourism game. We use destinations ratings-based multi-criteria recommender system (MCRS) to generate recommendation rankings as a reference for visualizing halal travel for players as potential tourists. This method improves the ability of the conventional tourism recommendation system, which is generally based on a single criterion. In this study, we use eight destinations rating criteria as a reference for calculating the recommender system in the halal tourism game. Each of these criteria is a reference for tourists' assessment of halal tourist destinations in Indonesia. Next, we integrate the cosine-based similarity technique in MCRS to measure the level of similarity between players and previous tourist data sets. This research's testing phase uses the theme of halal tourism destinations in the Batu City area. The test results show that the number and composition of the tourism destinations item rating as input of the recommender system affect the accuracy, precision, recall, and F1 scores. Based on 40 experiments with different tourism destination item rating input configurations, it shows that the average value of accuracy = 0.60, precision = 0.67, recall = 0.64 and F1 score = 0.65.
7 citations
TL;DR: In this paper , the authors used the Multi-Criteria Recommender System (MCRS) to produce recommendations for tourist destinations as a reference for selecting scenario visualizations and used the Ethereum blockchain platform to handle data circulation between parts of the system.
3 citations
TL;DR: In this article , the Multi-Criteria Recommender System (MCRS) was used to select halal tourism in Batu City, and the implementation of MCRS using cosine-based similarity succeeded in producing the five highest recommendations for halal tourist attractions.
Abstract: Tourism is an activity where people or groups travel voluntarily for relaxation, seeking entertainment, or enjoy cultural diversity both within the city, outside the city, or even abroad. For traveling, information about halal tourism is essential that tourists must know. Tourists can contact a tour guide to find information and recommendations for halal tourism. However, it will cost quite a bit and need for a recommendation system to obtain recommendations and make it easier for tourists to determine which halal tourism to visit. This study aims to obtain the Multi-Criteria Recommender System's (MCRS) performance using cosine-based similarity to select halal tourism in Batu City. MCRS extends the traditional approach by using more than one scoring criteria to generate recommendations. The implementation of MCRS using cosine-based similarity succeeded in producing the five highest recommendations for halal tourist attractions, which were implemented in a game-based system. Through recommendation accuracy testing on two items, three items, four items, and five tourist attractions items, we obtained an average accuracy is 77,95%.
2 citations
Journal Article•
TL;DR: In this paper , the authors present the preliminary results of a systematic literature review, following Kitchenham's guidelines, regarding the application of MCDA methods in recommender systems over the last two decades.
Abstract: Multiple Criteria Decision Making (MCDA) methods have been increasingly applied to improve recommendations when multiple criteria are considered in Recommender Systems (RSs). This study presents the preliminary results of a systematic literature review, following Kitchenham’s guidelines, regarding the application of MCDA methods in RSs over the last two decades. Based on our findings, MCDA methods can be applied in two RS phases: the preference elicitation and the recommendation phases. In the former, RSs usually have a strong interaction with the user, which results in more personalized recommendations, ensuring higher user satisfaction and contributing to address the cold-start challenge in RSs. Regarding the recommendation phase, while most RSs are based on ranking approaches, there is a trend to apply sorting methods in order to avoid an additional step involving a filtering application that selects a subset of alternatives. Future research could focus on applying preference learning combined with MCDA methods for exploring improvements in prediction and recommendation phases, and also in quality and processing time.
1 citations
TL;DR: In this paper , a game-based learning media supported by virtual reality and ambient intelligence technology is proposed to equip students with adaptive responses to subject matter scenarios, which can provide an adaptive response to the choice of geometry subject matter recommendations for students according to their pre-test results.
Abstract: —The challenge to increasing understanding of mathematics lessons for students in madrasah schools makes the learning process require the support of adaptive alternative learning media. In this study, we propose a serious game-based learning media supported by virtual reality and ambient intelligence technology to equip students with adaptive responses to subject matter scenarios. Ambient intelligence works based on recommendations generated by the Multi-Criteria Recommender System (MCRS). In calculating a similarity between users and reference data, MCRS uses cosine-based similarity calculations, and average similarity is used for ranking. We developed this learning media experiment called Math-VR using the Unity game engine. The experimental test results show that MCRS-based ambient intelligence technology can provide an adaptive response to the choice of geometry subject matter recommendations for students according to their pre-test results. The analysis results show that the recommendation system as part of ambient intelligence has the highest accuracy rate of 0.92 when using 80 reference data.
References
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