Hybrid Recommendation System for Tourism

Chen, Jen‐Hsiang; Chao, Kuo-Ming; Shah, Nazaraf

doi:10.1109/icebe.2013.24

Cited by 31 publications

(13 citation statements)

References 19 publications

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“…The advantage of the method is that it manages sparse data better than memory-based methods and is scalable in large data, as well as improving its performance in recommendation algorithms. Hybrid methods consisting of model-based and memory-based methods are used to overcome the disadvantages of the method and improve the suggested results [ 47 ]. A new algorithm based on Memetic algorithm and metaheuristic methods has been introduces, although it does not consider the time parameter but the simulated annealing algorithm improves performance [ 3 , 48 ].…”

Section: Literature Reviewmentioning

confidence: 99%

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nia

Jalali

2022

Computational Intelligence and Neuroscience

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Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.

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Section: Literature Reviewmentioning

confidence: 99%

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nia

Jalali

2022

Computational Intelligence and Neuroscience

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“…Other ML methodologies applied to tourism recommender systems include genetic algorithms [176] and [179], fuzzy logic and association rule mining [174], data clustering [168,169], latent dirichlet allocation + natural language processing [97] and decision trees + KNN [79].…”

Section: Tourism Recommender Systemsmentioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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“…Chen et al [8] propose a solution that considers several traveling factors such as the budget and available time. Thus, their recommendation system refines an exact set of tourist locations by applying a GA based on minimum cost.…”

Section: Related Workmentioning

confidence: 99%

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

et al. 2020

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In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD)-Orienteering Problem (OP)-Time Windows (TW), which analyzes in real time the user's constraints and the points of interest's constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users' interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users' perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries.

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Hybrid Recommendation System for Tourism

Cited by 31 publications

References 19 publications

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

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