A genetic-based pairwise trip planner recommender system

Qomariyah, Nunung Nurul; Kazakov, Dimitar

doi:10.1186/s40537-021-00470-6

Cited by 4 publications

(5 citation statements)

References 20 publications

(5 reference statements)

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“…219,222,[250][251][252] • Involvement of big data & social networking is lacking in the taxi recommender systems; as information overload, some systems should be proposed to handle real-time trajectory complex data. 23,217,253,254 • The majority of taxi recommender systems are particular users. Still, more focus is needed on the group of users, such as: carpooling and taxi sharing frameworks, so that fuel, time, and traffic jams can be reduced for our green environment.…”

Section: Discussionmentioning

confidence: 99%

“…Significant concentration should be on optimized approaches: Managing data quality, spatial crowdsourcing, semantic data administration, and various types of sensors to get efficient taxi recommender systems 219,222,250–252 Involvement of big data & social networking is lacking in the taxi recommender systems; as information overload, some systems should be proposed to handle real‐time trajectory complex data 23,217,253,254 The majority of taxi recommender systems are particular users.…”

Section: Discussionmentioning

confidence: 99%

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Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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“…Definisi taksonomi didasarkan pada pendekatan penelitian yang dilakukan, jumlah fungsi objektif yang digunakan, faktor konteks yang mempengaruhi dan teknik yang digunakan untuk mendapatkan solusi. Penulis membagi menjadi 2 kelompok pendekatan dalam penelitian rekomendasi rute yaitu (i) penelitian yang berfokus pada proses optimasi rute perjalanan seperti pada penelitian [25], [39], [41] dan (ii) penelitian yang fokus pada optimasi mendapatkan nilai pada tempat wisata yang sesuai dengan personalisasi atau minat pengguna [16], [18], [40]. Selain itu, berdasarkan jumlah fungsi objektif yang digunakan mayoritas penelitian terbagi menjadi 2, yaitu penelitian yang menggunakan satu fungsi objektif dan multi fungsi objektif.…”

Section: Hasil Dan Analisisunclassified

Optimalisasi Rekomendasi Rute Pada Perencanaan Perjalanan Wisata: Studi Pustaka

Ramdani,

Widyantoro,

Munir

2024

MALCOM

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Tourist trip design problems (TTDP) merupakan permasalahan yang berkaitan dengan bidang pariwisata. TTDP berkaitan dengan perencanaan pengguna dalam melakukan perjalanan wisata berdasarkan pada tempat wisata yang menarik. Dalam sistem rekomendasi, TTDP merupakan permasalahan yang menarik. Hal ini karena tidak hanya digunakan untuk menemukan tempat wisata yang sesuai dengan pengguna, tetapi juga untuk menggabungkan tempat wisata ke dalam rute perjalanan yang praktis dengan mempertimbangkan batasan. Pada artikel ini bertujuan menyajikan penelitian sebelumnya yang berkaitan dengan proses optimasi rekomendasi perjalanan dan bagaimana permasalahan tersebut dimodelkan menggunakan pendekatan yang berbeda untuk mencari solusi yang optimal. Selain itu peluang penelitian yang dapat dilakukan untuk meningkatkan performa rekomendasi. Berdasarkan synthetic literatur review (SLR) dalam penelitian ini, didapatkan peluang penelitian yang dapat dilakukan untuk mendapatkan rekomendasi rute perjalanan yang optimal seperti kombinasi algoritma metaheuristic atau algoritma bio-inspired. Selain itu pada personalisasi pengguna terkait tempat wisata, terdapat peluang mengimplementasikan algorime deep learning seperti LTSM, Transformer, Bert sebagai nilai tempat wisata dari sisi pengguna

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“…In smart tourism, it is crucial to consider the dual importance of relevance and fairness in recommendations [11,12]. Relevance focuses on the user's perspective; it considers user-oriented factors shaping the overall travel experience that influence the possibility of repeat visits and contribute significantly to user satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism

Qassimi,

Rakrak

2024

Preprint

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In the context of smart tourism, the utilization of recommender systems is becoming increasingly critical for enhancing the personalization and quality of travel experiences. Tourists often encounter complex decision-making due to information overload, context-aware recommender systems have emerged as a promising solution, leveraging contextual data such as time, weather, and location. However, these systems face the challenge of the complexity of handling dynamic context. Thus, the static nature of these systems can result in a degradation of performance, as they fail to capture the dynamic nature of user behavior and context. Addressing these issues, this paper presents a novel multi-objective contextual multi-armed bandit-based recommender system. This proposal integrates the strengths of contextual bandit algorithms with multi-objective optimization, offering personalized recommendations and learning from user feedback. The multi-objective optimization includes the dual necessities of relevance and fairness in recommendations, ensuring the promotion of a balanced tourism ecosystem. Extensive experiments were carried out on public datasets to evaluate the performance of our proposed approach. Its effectiveness was compared with baseline methods to establish its performance, demonstrating the significance of multi-objective optimization in enhancing personalized recommendations in smart tourism. To evaluate the performance of our proposed algorithm, we conducted experiments using two datasets, a designed dataset that simulates real-world scenarios and TripAdvisor dataset. The study provides a case scenario of implementing this proposed approach in the smart tourism context of Marrakesh, demonstrating its potential to revolutionize the tourist experience in smart cities.

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A genetic-based pairwise trip planner recommender system

Cited by 4 publications

References 20 publications

Towards the significance of taxi recommender systems in smart cities

Towards the significance of taxi recommender systems in smart cities

Optimalisasi Rekomendasi Rute Pada Perencanaan Perjalanan Wisata: Studi Pustaka

Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism

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