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Journal articles on the topic 'Travel recommender system'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Jambusariya, Shlok, Pragati Yadav, Mit Virani, and Pranali Wagh. "Intelligent Travel Guide: A Travel Recommender System." Journal of Web Development and Web Designing 7, no. 1 (2022): 15–20. http://dx.doi.org/10.46610/jowdwd.2022.v07i01.003.

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The hassle of deciding on a travel destination is often overlooked by travel websites. Travellers, a lot of times, do not have a clear idea of where they want to travel to. We aim on solving this problem by introducing a chat bot system that can recommend travel destinations based on minimal information from the traveller. Another interesting feature of the project is its itinerary generator. The system aims on providing a human-like user experience through the use of a chatbot interface. The interface interacts with the user to retrieve information about the user’s details like travel date, n

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Jambusariya, Shlok, Pragati Yadav, Mit Virani, and Pranali Wagh. "Intelligent Travel Guide: A Travel Recommender System." Journal of Web Development and Web Designing 7, no. 1 (2022): 15–20. http://dx.doi.org/10.46610/jowdwd.2022.v07i01.003.

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The hassle of deciding on a travel destination is often overlooked by travel websites. Travellers, a lot of times, do not have a clear idea of where they want to travel to. We aim on solving this problem by introducing a chat bot system that can recommend travel destinations based on minimal information from the traveller. Another interesting feature of the project is its itinerary generator. The system aims on providing a human-like user experience through the use of a chatbot interface. The interface interacts with the user to retrieve information about the user’s details like travel date, n

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R, oopesh L., and Tulasi B. . "A Survey of Travel Recommender System." International Journal of Computer Sciences and Engineering 7, no. 3 (2019): 356–62. http://dx.doi.org/10.26438/ijcse/v7i3.356362.

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Supriyanto, Supriyanto, and Jefree Fahana. "Possible System Architecture for Travel Recommender." Jurnal Online Informatika 5, no. 1 (2020): 1–8. http://dx.doi.org/10.15575/join.v5i1.573.

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Travel recommender systems have been developed to meet the needs of users in the field of tourism. This system has several versions depending on the characteristics of the country, users and filtering techniques used. The development of recommendation filtering system techniques is very rapid so that the recommendation system has high enough complexity, but it also must have high usability. This paper discusses how the travel recommender system architecture is built by examining data structures, processing procedures and interaction design. The goal is to obtain the best usability in implement

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B, Tanuja Choudhary, and Tulasi B. "Recommender system for personalised travel itinerary." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4460. http://dx.doi.org/10.11591/ijece.v9i5.pp4460-4465.

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<p class="Body">A recommender system is an approach to give an appropriate solu-tion to a particular problem. This helps in recognising the pattern or behaviour of a user to suggest future possible likes of the user. Nowa-days people like to travel during their spare time, it has become a rigid task to decide where to go. This paper represents a customised recommender system to help users in destining their itinerary. A model is designed to suggest the best places to visit in Rome. A questionnaire was prepared to get information about users interest during their travel. The model generat

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Tanuja, Choudhary B., and B. Tulasi. "Recommender system for personalised travel itinerary." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4460–65. https://doi.org/10.11591/ijece.v9i5.pp4460-4465.

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A recommender system is an approach to give an appropriate solution to a particular problem. This helps in recognising the pattern or behaviour of a user to suggest future possible likes of the user. Nowadays people like to travel during their spare time, it has become a rigid task to decide where to go. This paper represents a customised recommender system to help users in destining their itinerary. A model is designed to suggest the best places to visit in Rome. A questionnaire was prepared to get information about user’s interest during their travel. The model generates the best five

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Chen, Meng-Kuan, Hsin-Wen Wei, and Wei-Tsong Lee. "Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment." International Journal for Applied Information Management 2, no. 2 (2021): 66–80. http://dx.doi.org/10.47738/ijaim.v2i2.28.

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Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasona

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Fang, Hui, Chongcheng Chen, Yunfei Long, Ge Xu, and Yongqiang Xiao. "DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph." Mathematics 10, no. 9 (2022): 1402. http://dx.doi.org/10.3390/math10091402.

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In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namel

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Mohite, K. T. "Travel and Tourism Management System Using Chatbot Recommender." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2908–17. http://dx.doi.org/10.22214/ijraset.2024.60581.

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Abstract: The importance of chatbots has surged in both research and practical applications, exemplified by widely used platforms such as Amazon’s Alexa and Apple’s Siri. This paper introduces the methodologies and technologies underpinning a chatbot tailored for e-tourism, facilitating textual communication to aid in hotel bookings, trip planning, and recommendations for noteworthy sights. Specifically, we explore the integration of model-based reasoning to elevate user interaction, particularly in scenarios featuring an overwhelming array of choices or where user preferences are excessively

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Mohite, Prof K. T. "TRAVEL AND TOURISM MANAGEMENT SYSTEM USING CHATBOT RECOMMENDER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26416.

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Chatbots have gained increasing importance for research and practice with a lot of applications available today including Amazon’s Alexa or Apple’s Siri. In this paper, we present the underlying methods and technologies behind a Chatbot for e-tourism that allows people textually communicate with the purpose of booking hotels, planning trips, and asking for interesting sights worth being visit. In particular, we show how model- based reasoning can be used for enhancing user experience during a chat, e.g., in cases where too many possible selections are available or where user preferences are to

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Liviandra, Monica, Z. K. Abdurahman Baizal, and Ramanti Dharayani. "Conversational Recommender System for Impromptu Tourists to Recommend Tourist Routes Using Haversine Formula." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1224. http://dx.doi.org/10.30865/mib.v5i4.3229.

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In this paper, we use two terms to describe tourists, i.e. planned tourists and impromptu tourists. Planned tourists are tourists who intentionally travel. Meanwhile, impromptu tourists are those who accidentally become tourists because they are in a new area for an activity. Previously, tourists who were going to travel usually relied on the services of travel agents to get recommendations for tourist attractions, different from impromptu tourists this was not done before. Impromptu tourists sometimes do not have much time to carry out tourism activities so that impromptu tourists only visit

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Binucci, Carla, Felice De Luca, Emilio Di Giacomo, Giuseppe Liotta, and Fabrizio Montecchiani. "Designing the Content Analyzer of a Travel Recommender System." Expert Systems with Applications 87 (November 2017): 199–208. http://dx.doi.org/10.1016/j.eswa.2017.06.028.

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V.K, Muneer, and Dr Mohamed Basheer K.P. "A Comparative Study of Collaborative Filtering and Content-Based Approaches for Improving the Accuracy of Travel Recommender Systems for Malayalam Language." International Journal of Advanced Networking and Applications 14, no. 06 (2023): 5717–21. http://dx.doi.org/10.35444/ijana.2023.14608.

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In this paper, we present a personalized travel recommender system in the Malayalam language using artificial intelligence techniques. The study focuses on the use of travelogues and travel reviews written by travellers on social media as the primary source of data. A dataset of 11000 posts from 6444 travellers was collected from Facebook and other online platforms during 2020-2023. The data was pre-processed to extract relevant information such as travel mode, type of travel, location visited, and climate. Two approaches were used to build the recommender system: collaborative filtering-based

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Glaret Shirley, Sinnappan, Kodukula Subrahmanyam, Dusanapudi Susrija, and Palempati Akhila. "K-Means Algorithm and Clustering Technique for A Recommender System." International Journal of Application on Sciences, Technology and Engineering 1, no. 1 (2023): 302–12. http://dx.doi.org/10.24912/ijaste.v1.i1.302-312.

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Most of netizens are fond of traveling. Though they are interested to travel, majority of them are confused about where to go. Whenever the netizens planed for a travel, they spent hours searching for an interesting place that matches their point of interest. Therefore, there is a need for a recommender system which can recommend several interesting travel venues based on their preferences. Hence, information regarding different users prefers different travel locations are required. In this paper, Google review is used as a reference to divide the users into clusters of similar interests. The

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Vijayakumar, V., Subramaniyaswamy Vairavasundaram, R. Logesh, and A. Sivapathi. "Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation." International Journal of Web Portals 11, no. 1 (2019): 1–18. http://dx.doi.org/10.4018/ijwp.2019010101.

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With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visit

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Zhang, Mu, Jie Miao, Jing Luo, and Jian Hua Lan. "Research on Personalized Recommendation Technology for Tourism Industry - A Perspective of a System Framework Design." Advanced Materials Research 219-220 (March 2011): 1276–80. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1276.

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When massive information brings people more channels to attain messages, there generates different types of new personalized recommendation systems. As the important sector for social development, tourism industry suffers the problem of information over-loading. In the construction of personalized recommender system mainly involves two problems: information acquisition and personalized recommender. This recommender system is able to form client’s databank after user’s login and assessment for various travel destination and products to support more accurate user’s information mining. The author

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Javadian Sabet, Alireza, Mahsa Shekari, Chaofeng Guan, Matteo Rossi, Fabio Schreiber, and Letizia Tanca. "THOR: A Hybrid Recommender System for the Personalized Travel Experience." Big Data and Cognitive Computing 6, no. 4 (2022): 131. http://dx.doi.org/10.3390/bdcc6040131.

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One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This

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Zhuang, Yuanyuan, and Jaekyeong Kim. "A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management." Sustainability 13, no. 14 (2021): 8039. http://dx.doi.org/10.3390/su13148039.

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Numerous reviews are posted every day on travel information sharing platforms and sites. Hotels want to develop a customer recommender system to quickly and effectively identify potential target customers. TripAdvisor, the travel website that provided the data used in this study, allows customers to rate the hotel based on six criteria: Value, Service, Location, Room, Cleanliness, and Sleep Quality. Existing studies classify reviews into positive, negative, and neutral by extracting sentiment terms through simple sentimental analysis. However, this method has limitations in that it does not co

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Ravi, Logesh, V. Subramaniyaswamy, V. Vijayakumar, Siguang Chen, A. Karmel, and Malathi Devarajan. "Hybrid Location-based Recommender System for Mobility and Travel Planning." Mobile Networks and Applications 24, no. 4 (2019): 1226–39. http://dx.doi.org/10.1007/s11036-019-01260-4.

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Sabri, Ily Amalina Ahmad, Noor Maizura Mohamad Noor, Noraida Haji Ali, and Fathilah Ismail. "A PERSONALIZED TRAVEL RECOMMENDER SYSTEM USING FUZZY ANALYTIC HIERARCHY PROCESS." BIMP-EAGA Journal for Sustainable Tourism Development 11, no. 1 (2022): 15–26. http://dx.doi.org/10.51200/bimpeagajtsd.v11i1.3914.

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Information and communication technologies have a deep implication for the industry. This combination is being used extensively in an excessive variety of functions and numerous applications. On the other hand, tourism has become an extremely dynamic system. The globalization enabled by technology development and budget travel cost has greatly increased competition. Decision support system (DSS) can play an important role to organizations and people who managing tourism destinations. The main intention of this research is to apply Decision Support System (DSS) in tourism. It aims to establish

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Tsai, Chieh-Yuan, and Jing-Hao Wang. "A Personalized Itinerary Recommender System: Considering Sequential Pattern Mining." Electronics 14, no. 10 (2025): 2077. https://doi.org/10.3390/electronics14102077.

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Personalized itinerary recommendations are essential as many people choose traveling as their primary leisure pursuit. Unlike model-based and optimization-based methods, sequential-pattern-mining-based methods, which are based on the users’ previous visiting experience, can generate more personalized itineraries and avoid the difficulties caused by the two methods. Although sequential-pattern-mining-based methods have shown promise in generating personalized itineraries, the following three challenges remain. First, they often overlook user diversity in time and category preferences, leading t

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Hang, Lei, Sang-Hun Kang, Wenquan Jin, and Do-Hyeun Kim. "Design and Implementation of an Optimal Travel Route Recommender System on Big Data for Tourists in Jeju." Processes 6, no. 8 (2018): 133. http://dx.doi.org/10.3390/pr6080133.

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A recommender system is currently applied in many different domains, seeking to provide users with recommendation services according to their personalized preferences to relieve rising online information congestion. As the number of mobile phone users is large and growing, mobile tourist guides have attracted considerable research interest in recent years. In this paper, we propose an optimal travel route recommender system by analyzing the data history of previous users. The open dataset used covers the travel data from thousands of mobile tourists who visited Jeju in a full year. Our approac

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Mohamed Basheer K. P., Rizwana Kallooravi Thandil, Muneer V. K. ,. "Utilizing BiLSTM For Fine-Grained Aspect-Based Travel Recommendations Using Travel Reviews In Low Resourced Language." Journal of Electrical Systems 20, no. 2s (2024): 233–40. http://dx.doi.org/10.52783/jes.1133.

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Recommender systems have become an essential tool for enhancing user experiences by providing personalized recommendations. In this study, we present a novel approach to constructing a recommender system specifically tailored for Malayalam travel reviews. Our objective was to extract relevant features from these reviews and employ a bidirectional Long Short-Term Memory (BiLSTM) architecture to construct a robust and accurate recommendation model. We focused on four key features extracted from the travel reviews: travel mode, travel type, location climate, and location type. The travel mode fea

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Selmi, Afef, Maryah Alawadh, Raghad Alotaibi, and Shrefah Alharbi. "A tag-based recommender system for tourism using collaborative filtering." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 960. https://doi.org/10.11591/ijeecs.v38.i2.pp960-974.

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<p>Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation

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Afef, Selmi Maryah Alawadh Raghad Alotaibi Shrefah Alharbi. "A tag-based recommender system for tourism using collaborative filtering." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 960–74. https://doi.org/10.11591/ijeecs.v38.i2.pp960-974.

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Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation technolo

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Mat Amin, Maizan, Jannifer Yep Ai Lan, Mokhairi Makhtar, and Abd Rasid Mamat. "A Decision Tree Based Recommender System for Backpackers Accommodations." International Journal of Engineering & Technology 7, no. 2.15 (2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.15.11210.

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Backpackers often travel for a longer period of time, have their own budgets and requirements on accommodations. The existing systems do not offer personalized recommendation criteria and some proposed inefficient recommender system (RS) for users. Moreover, other than information searching from websites and bloggers, only limited systems were specifically designed for backpackers’ accommodations recommender system. An observation and online survey was conducted to get the information from backpackers regarding their preferences while looking for the accommodations. Fifty (50) respondents were

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Fu, Zhe, Li Yu, and Xi Niu. "TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (2022): 1–22. http://dx.doi.org/10.1145/3487047.

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As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE ( T ravel R einforcement Recommendations Based on Location- A ware C ontext E xtraction) i

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Ravi, Logesh, and Subramaniyaswamy Vairavasundaram. "A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users." Computational Intelligence and Neuroscience 2016 (2016): 1–28. http://dx.doi.org/10.1155/2016/1291358.

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Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance tradit

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Fudholi, Dhomas Hatta, Septia Rani, Dimastyo Muhaimin Arifin, and Mochamad Rezky Satyatama. "Deep Learning-based Mobile Tourism Recommender System." Scientific Journal of Informatics 8, no. 1 (2021): 111–18. http://dx.doi.org/10.15294/sji.v8i1.29262.

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A tourism recommendation system is a crucial solution to help tourists discover more diverse tourism destinations. A content-based approach in a recommender system can be an effective way of recommending items because it looks at the user's preference histories. For a cold-start problem in the tourism domain, where rating data or past access may not be found, we can treat the user's past-travel-photos as the histories data. Besides, the use of photos as an input makes the user experience seamless and more effortless. The current development in Artificial Intelligence-based services enable the

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Bahramian, Z., and R. Ali Abbaspour. "AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 83–90. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-83-2015.

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A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s pr

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J. Karthiyayini. "Personalized Travel Recommendations System Using Hybrid Filtering and Deep Learning." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 118–29. https://doi.org/10.52783/jisem.v10i13s.2011.

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Personalized recommendations are provided by recommender systems, which help users cope with the issue of information overload. The primary objective of this research paper is to address the issue of information overload by offering personalized tourism recommendations to users. The study proposes using multi-criteria recommendation algorithms that consider many attributes rather than depending exclusively on overall user ratings, as is common in traditional recommender systems. The fundamental concept of this strategy relies on a hybrid filtering technique. In addition, the system employs a b

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Mule, Rishi N., and Shubham S. Mulik. "TRS – A Rule Based Personalized Tourism Recommender System." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1280–85. http://dx.doi.org/10.22214/ijraset.2022.47172.

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Abstract: In today’s world many people rely on online services. They even plan for their trips by searching places online. However, they usually face problems of being supplied with lots of information. In consequence, they end up wasting a great amount of time in searching and decision making. Getting the expert advice to specify a reliable tourist attraction quickly and consistent to the requirements of each tourist was difficult, and the amount of experienced experts who can advise for tourism issues was insufficient. Providing an effective service in the tourism sector, such as using the t

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Jeswani, Ashish, Radhika Shinde, Roma Panaskar, Varad Kanade, and Vrushali Kolapkar. "Intelligent Destination Recommender and Community Builder." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 665–71. http://dx.doi.org/10.17762/ijritcc.v11i11s.8302.

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Recommendation engines make use of machine learning techniques and generally deal with ranking and rating of products/users. With the help of this system we aim to suggest different destinations to users based on their interest and previous visits. Along with recommendations we also aspire to enable users to build travel communities for people sharing similar interests .This shall help travelers with planning ,meeting like-minded people,safety and enthralling experience. As per the analysis done on pre-existing systems we discerned that enabling users to build a community of travelers vi

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Logesh, R., V. Subramaniyaswamy, and V. Vijayakumar. "A personalised travel recommender system utilising social network profile and accurate GPS data." Electronic Government, an International Journal 14, no. 1 (2018): 90. http://dx.doi.org/10.1504/eg.2018.089538.

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Subramaniyaswamy, V., V. Vijayakumar, and R. Logesh. "A personalized travel recommender system utilizing social network profile and accurate GPS data." Electronic Government, an International Journal 14, no. 1 (2018): 1. http://dx.doi.org/10.1504/eg.2018.10008842.

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Bangoria, Bhoomi Mansukhlal, Sweta S. Panchal, Sandipkumar R. Panchal, Janvi M. Maheta, and Sweety R. Dhabaliya. "Multidimensional Dynamic Destination Recommender Search System Employing Clustering: A Machine Learning Approach." Indian Journal Of Science And Technology 17, no. 40 (2024): 4187–97. http://dx.doi.org/10.17485/ijst/v17i40.2266.

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Objectives: Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and significance. According to the city first find latitude and l

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Logesh, R., V. Subramaniyaswamy, V. Vijayakumar, and Xiong Li. "Efficient User Profiling Based Intelligent Travel Recommender System for Individual and Group of Users." Mobile Networks and Applications 24, no. 3 (2018): 1018–33. http://dx.doi.org/10.1007/s11036-018-1059-2.

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Deshmukh, Miss Samradni, and Prof K. R. Ingole. "Implementation Paper on Personalized Travel Recommendation by Mining People Attributes." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1441–45. http://dx.doi.org/10.22214/ijraset.2022.41415.

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Abstract: The recommendation system has growth choices in recent years. The recommendation system is existing in many applications which gives online travel information for individual travel package. A new model named travel recommendation using data mining techniques which extracts the features like locations, travel seasons of various landscapes. Thus, it possesses the material of the travel packages and interests of tourists. Further extending E-TRAST model with the tourist-relation-area season topic model includes relationship with tourists. It includes mining significant tourist locations

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Deshmukh, Miss Samradni, and Prof K. R. Ingole. "Graph Based Personalized Travel Recommendation Using Data Mining Technique Collaborative Filtering Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 828–30. http://dx.doi.org/10.22214/ijraset.2022.40739.

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Abstract: The recommendation system has growth choices in recent years. The recommendation system is exist in many applications which gives online travel information for individual travel package. A new model named travel recommendation using data mining techniques which extracts the features like locations, travel seasons of various landscapes. Thus it possesses the material of the travel packages and interests of tourists. Further extending E-TRAST model with the tourist-relation-area season topic model includes relationship with tourists. It includes mining significant tourist locations bas

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Swapnali, Ravindra Teli. "User Preference Perspective for Implementing Personalized Travel Package Recommendation System." International Journal of Innovative Science and Research Technology 7, no. 7 (2022): 171–73. https://doi.org/10.5281/zenodo.6902166.

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In today’s market, recommender techniques are showing a considerable growth. Many travel packages are present on different websites and also you get these packages from different tourist companies to all the places over the world. You just need to decide a package which is best suits for you. Many of customers are facing problem of deciding the best package as she/he has to browse multiple websites, contact many travel agents and etc. which is a lengthy process and it is very hectic. There should be a system where the user should find the best package of his/her choice on single click. T

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Gamidullaeva, Leyla, Alexey Finogeev, Mikhail Kataev, and Larisa Bulysheva. "A Design Concept for a Tourism Recommender System for Regional Development." Algorithms 16, no. 1 (2023): 58. http://dx.doi.org/10.3390/a16010058.

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Despite of tourism infrastructure and software, the development of tourism is hampered due to the lack of information support, which encapsulates various aspects of travel implementation. This paper highlights a demand for integrating various approaches and methods to develop a universal tourism information recommender system when building individual tourist routes. The study objective is proposing a concept of a universal information recommender system for building a personalized tourist route. The developed design concept for such a system involves a procedure for data collection and prepara

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Patole, Prof Uttam. "Hybrid Recommender System for Tourism Based on Big Data and AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30708.

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With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To a

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Indriana, Marcelli, and Chein-Shung Hwang. "Applying Neural Network Model to Hybrid Tourist Attraction Recommendations." Jurnal ULTIMATICS 6, no. 2 (2014): 63–69. http://dx.doi.org/10.31937/ti.v6i2.339.

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Recently, recommender systems have been developed for a variety of domains. Recommender systems also can be applied in tourism industry to help tourists organizing their travel plans. Recommender systems can be developed by a variety of different techniques such as Content-Based filtering (CB), Collaborative filtering (CF), and Demographic filtering (DF). However, the uses of these techniques individually will have some disadvantages. In this research, we propose a hybrid recommender system to combine the predictions from CB, CF and DF approaches using neural network model. Neural network mode

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Badouch, Mohamed, and Mehdi Boutaounte. "Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism." April-May 2023, no. 33 (April 26, 2023): 35–45. http://dx.doi.org/10.55529/jaimlnn.33.35.45.

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Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recomme

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Anshari, Muhammad Ridha, and Z. K. A. Baizal. "N-Days Tourist Route Recommender System in Yogyakarta Using Genetic Algorithm Method." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 8, no. 3 (2023): 736–43. http://dx.doi.org/10.29100/jipi.v8i3.3893.

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Tourism is one of the proven solutions for the Indonesian economy. Tourism in certain regions, such as Yogyakarta, can significantly affect the region's economic development, including creating new jobs, creating new business opportunities, and increasing regional income. However, for tourists from outside Yogyakarta, it requires planning a tour before traveling in Yogyakarta, especially if he wants to spend several days on a tour. Many previous studies have developed systems that can recommend tourist routes, but not within a few days of tourist visits. In this study, we propose the use of Ge

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K. G. Saranya, Aditya Sharma, Dharma Dhurai V, and Harish J. "A Critical Review on Location Based Hybrid Filtering Recommender Systems." March 2023 5, no. 1 (2023): 1–10. http://dx.doi.org/10.36548/jscp.2023.1.001.

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A recommender system is basically a type of information filtering system that suggests/recommends items based on the factors that constitute what the user is most interested in. The recommendations are typically provided in relation to different decision-making processes. Tourism is a social phenomenon where people deliberately travel in search of recreation, well-being, cultural exploration or get themselves softened up. But, the amount of information available online keeps expanding at exponential rates and thus the users have expressed their feeling of frustration at how challenging it is t

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Mostafa. M.khater. "Mobile Tourism Recommender System for Users to Get a Better Choice of Tour." Wasit Journal of Computer and Mathematics Science 2, no. 3 (2023): 79–83. http://dx.doi.org/10.31185/wjcms.186.

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The system might include a turn-by-turn route highlight to prevent fake preferences that check if the user has taken the course. A larger customer overview with more participants is required to acquire more insightful client feedback. Our ex-amination was designed as a lab experiment to gather initial data straight absent. While making fun of other clients and their system comments, we looked at a few initial objective mixtures. Doing field research with actual clients using our suggested model in real-world situations (such as when looking for a course online to work from home) is crucial. Th

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Arif, Yunifa Miftachul, Dyah Wardani, Hani Nurhayati, and Norizan Mat Diah. "Non-Rating Recommender System for Choosing Tourist Destinations Using Artificial Neural Network." Applied Information System and Management (AISM) 6, no. 2 (2023): 61–68. http://dx.doi.org/10.15408/aism.v6i2.26741.

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The development of tourist destinations in Batu City makes it hard for the tourists to decide their destinations. The recommender system is a solution that provides a lot of information or tourist attraction data. Collaborative filtering is often used in recommender systems. However, it has drawbacks; one of which is the cold-start problem, where the system cannot recommend items to new users. It was caused by the new user who had no history of rating on any item, or the system had no information. This study aims to apply a non-rated travel destination recommendation system to address the cold

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Z., K. Abdurahman Baizal, A. Rahmawati Aniq, M. Lhaksmana Kemas, Z. Mubarok Moh, and Qadrian M. "Generating Travel Itinerary Using Ant Collony Optimization." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1208–16. https://doi.org/10.12928/TELKOMNIKA.v16i3.7268.

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Travelling is one of the activities needed by everyone to overcome weariness. The number of information about the tourism destination on the internet sometimes does not provide easiness for oncoming tourists. This paper proposes a system capable of making travel itinerary, for tourists who want to visit an area within a few days. For generating itinerary, the system considers several criterias (Multicriteria-based), which include the popularity level of tourist attractions to visit, tourist visits that minimize budgets or tourist visits with as many destinations as possible. To handle multi cr

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Yanti, Putri Previa. "A Survey : Application of Big Data in the Travel and Tourism Industry." ITEJ (Information Technology Engineering Journals) 5, no. 1 (2020): 1–13. http://dx.doi.org/10.24235/itej.v5i1.38.

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The development of information technology has increased the travel and tourism industry. The travel and tourism data are available in many sources such as telephone, social media, sensor system on the internet of things, and others. The application of big data has great potential in the development of the travel and tourism industry. Big data can take advantage of new things in making the right decisions and seeing opportunities in doing better business. This paper provides a survey that discusses big data in the travel and tourism industry. Big data is used to ticket price and demand predicti

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