Fast Recommendation Method of Personalized Tourism Big Data Information Based on Improved Clustering Algorithm

Feng, Yi-lin; Zhang, Heqing; Peng, Cai-ting

doi:10.1007/978-3-030-94554-1_23

Cited by 2 publications

(1 citation statement)

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“…There has been research in this area (Navarro et al, 2000;Abdulaziz et al, 2015;Ramos et al, 2017), but there is generally little research in this field and there is a lack of centralized data repositories in the tourism sector, especially data lake repositories • Analytical techniques: The most commonly used analytical techniques in tourism research are identified. We obtain a wide range of analytical techniques, among which we can highlight: clustering (Feng et al, 2022), personalization (Gupta et al, 2022), collaborative-filtering (He, 2022), machine learning (Kayakus, 2022), recommender-systems (Julashokri et al, 2022), data-mining (Ma, 2022), text mining (Loureiro et al, 2022), natural language processing (Ray & Bala, 2021), or deep learning (law et al, 2019). some of the techniques identified like: text mining, sentimental analysis or natural language processing are applied to unstructured text-type information, which is the information typically collected in the sources like UGC or online reviews identified in the data sources area.…”

Section: A Conceptual Model Of Data Architecture and Processes Of A D...mentioning

confidence: 99%

Data Sources as a Driver for Market-Oriented Tourism Organizations: a Bibliometric Perspective

et al. 2023

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This paper presents a conceptual framework that accurately represents the current and future perspectives of data-driven companies in tourism by means of an analysis of the data sources used in the data-driven tourism research literature, as well as the research topics to which they are applied. For this purpose, a bibliometric analysis of data-driven tourism research is carried out. The framework of the study is all tourism-related publications whose research was based on data sources during the period 1982–2020. The results show some of the basic bibliometric performance indicators and the maps of science. The main themes of research interest are identified, and the conceptual evolution is obtained based on these maps. Three major thematic areas are identified: tourism research topics, information sources, and data analysis techniques. Based on these three thematic areas, the conceptual model of data architecture and processes of a data-driven organization in the tourism sector are obtained. An additional qualitative analysis of the three thematic areas is performed.

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