Designing a tourism recommendation system using a hybrid method (Collaborative Filtering and Content-Based Filtering)

Praditya, Ni Wayan Priscila Yuni; Permanasari, Adhistya Erna; Hidayah, Indriana

doi:10.1109/comnetsat53002.2021.9530823

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Some researchers have used a recommendation model built on Word2vec and LDA to solve the problem of recommending travel routes, incorporating users' reviews and scenic spot ratings into the model. By analyzing tourists' activities, such as consumption and social interactions, researchers have discovered that tourists' activities vary depending on their location scenery [18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Intelligent recommendation method of intelligent tourism scenic spot route based on collaborative filtering

2024

KSII TIIS

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Section: Related Workmentioning

confidence: 99%

Intelligent recommendation method of intelligent tourism scenic spot route based on collaborative filtering

2024

KSII TIIS

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“…Another popular approach is the top-k POI recommendation. Essentially it uses collaborative filtering with additional mechanisms to find a ranked list of top locations [46], [47], [48], [49], [50], [51].…”

Section: E Information Retrievalmentioning

confidence: 99%

Strategic and Crowd-Aware Itinerary Recommendation

Liu

Wood

Lim

2021

Lecture Notes in Computer Science

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Itinerary recommendation is a complex sequence prediction problem with numerous real-world applications. This task becomes even more challenging when considering the optimization of multiple user queuing times and crowd levels, as well as numerous involved parameters, such as attraction popularity, queuing time, walking time, and operating hours. Existing solutions typically focus on single-person perspectives and fail to address real-world issues resulting from natural crowd behavior, like the Selfish Routing problem. In this paper, we introduce the Strategic and Crowd-Aware Itinerary Recommendation (SCAIR) algorithm, which optimizes group utility in real-world settings. We model the route recommendation strategy as a Markov Decision Process and propose a State Encoding mechanism that enables real-time planning and allocation in linear time. We evaluate our algorithm against various competitive and realistic baselines using a theme park dataset, demonstrating that SCAIR outperforms these baselines in addressing the Selfish Routing problem across four theme parks.

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