Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations

Aldayel, Mashael; Al-Nafjan, Abeer; Al-Nuwaiser, Waleed M.; Alrehaili, Ghadeer; Alyahya, Ghadi

doi:10.3390/electronics12194047

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Therefore, directly using high or low scoring as the criteria for recommending POIs is also a fuzzy recommendation with uncertainty. As to the POI tour route recommendation, the traditional methods directly recommend routes visited by historical tourists to current tourists, which is also a fuzzy recommendation method with uncertainty that cannot fully match tourists' interests [3,4].…”

Section: Introduction 1research Background and Problem Discussionmentioning

confidence: 99%

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Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

Zhou,

Peng,

Wen

et al. 2023

Symmetry

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In machine learning, classifiers have the feature of constant symmetry when performing the attribute transformation. In the research field of tourism recommendation, tourists’ interests should be mined and extracted by the symmetrical transformation in founding the training dataset and creating the classifier, so as to ensure that the recommendation results meet the individualized interests and needs. In this paper, by applying the feature of constant symmetry in the classifier and analyzing the research background and existing problems of POI tour routes, we propose and construct a tour route recommendation model using improved symmetry-based Naive Bayes mining and spatial decision forest search. First, the POI natural attribute classification model is constructed based on text mining to classify the natural attributes of the destination POIs. Second, the destination POI recommendation model based on the improved symmetry-based Naive Bayes mining and decision forest algorithm is constructed, outputting POIs that match tourists’ interests. On this basis, the POI tour route recommendation model based on a spatial decision tree algorithm is established, which outputs the optimal tour route with the lowest sub-interval cost and route interval cost. Finally, the validation and comparative experiments are designed to output the optimal POIs and tour routes by using the proposed algorithms, and then the proposed algorithm is compared with the commonly used route planning methods, GDM and 360M. Experimental results show that the proposed algorithm can reduce travel costs by 4.56% and 10.36%, respectively, on the optimal tour route compared to the GDM and 360M and by 2.94% and 8.01%, respectively, on the suboptimal tour route compared to the GDM and 360M, which verifies the advantages of the proposed algorithm over the traditional route planning methods.

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Section: Introduction 1research Background and Problem Discussionmentioning

confidence: 99%

“…Connect n ode (3) and n ode (1), there is a closed loop, delete n ode (1); connect n ode (5), there is no closed loop, the moving path distance is D (3,5) , as shown in Figure 5E 7:…”

mentioning

confidence: 99%

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

Zhou,

Peng,

Wen

et al. 2023

Symmetry

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“…[5]. Model konseptual dan mengeksplorasi berbagai algoritma CF dalam konteks rekomendasi destinasi wisata, menunjukkan bahwa algoritma Bilateral Variational Autoencoder (BiVAE) unggul dalam kinerja [6].…”

Section: Iunclassified

Pengelolaan Populasi Kucing Liar Melalui Collaborative Filtering: Dampak Terhadap Kesehatan Masyarakat Dan Ekosistem Lokal

Hidayat,

Supriyono

2024

INDEXIA

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Seiring berjalannya waktu, kebersamaan sahabat kucing telah menjadi aspek yang abadi dalam kehidupan kita. Namun, sebuah masalah signifikan telah muncul: reproduksi kucing yang cepat, mengakibatkan kelebihan populasi yang tidak terkendali. Kelebihan hewan peliharaan ini memiliki dampak yang besar pada masyarakat sekitar. Banyaknya kucing liar menimbulkan tantangan besar, termasuk meningkatnya risiko kesehatan masyarakat akibat penularan penyakit seperti rabies dan toksoplasmosis, gangguan pada ekosistem lokal akibat pemangsaan yang tidak terkendali terhadap burung dan satwa liar kecil, serta masalah kebersihan dan kenyamanan di masyarakat. Lebih jauh lagi, situasi ini merenggangkan hubungan sosial di antara para tetangga, sehingga menimbulkan konflik dan ketidaknyamanan. Untuk mengatasi masalah yang memiliki banyak sisi ini, penelitian ini memperkenalkan pendekatan inovatif kecerdasan buatan yang berpusat pada model Penyaringan Kolaboratif. Sistem ini menggunakan teknik pembelajaran mesin, termasuk Penyaringan Kolaboratif, untuk meningkatkan fungsinya. Menyerupai platform pencocokan yang populer, sistem yang dihasilkan berkolaborasi dengan para profesional kedokteran hewan lokal dan organisasi penyelamatan hewan. Tujuan utamanya adalah untuk memfasilitasi identifikasi pemilik yang bertanggung jawab atas kucing, memastikan kehidupan yang bermartabat bagi mereka. Pendekatan perintis ini menjanjikan dampak positif pada populasi kucing dan masyarakat, serta mendorong koeksistensi yang harmonis. Sifat kolaboratif dari sistem ini, yang melibatkan Rumah Steril dan Lets Adopt Indonesia, menggarisbawahi potensinya untuk mengurangi tantangan yang ditimbulkan oleh kelebihan populasi kucing liar dan mempromosikan hubungan yang seimbang dan saling menghormati antara manusia dan kucing. Hasil Penelitian ini menghasilkan rekomendasi id kucing paling baik: 63, 58, 56, 22, 64, 35, 60, 39, 21, dan 7.

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“…When obtaining representations for users and items, some researchers has used pretraining methods to acquire more accurate prior probabilities for items [9,10]. Pre-training involves constructing initial features of the users and the items based on existing datasets and utilizing these features directly in subsequent recommendations.…”

Section: Introductionmentioning

confidence: 99%

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Zhang,

Ma,

Zhang

et al. 2023

Electronics

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Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.

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Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations

Cited by 7 publications

References 23 publications

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

Pengelolaan Populasi Kucing Liar Melalui Collaborative Filtering: Dampak Terhadap Kesehatan Masyarakat Dan Ekosistem Lokal

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

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