JACIII Vol.27 No.2 pp. 271-280
doi: 10.20965/jaciii.2023.p0271

Research Paper:

Design and Implementation of a Recommendation System for Buying Fresh Foods Online Based on Web Crawling

Tsung-Yin Ou*1 ORCID Icon, Yi-Chen Lee*2 ORCID Icon, Tien-Hsiang Chang*3 ORCID Icon, Shih-Hsiung Lee*3 ORCID Icon, and Wen-Lung Tsai*4,† ORCID Icon

*1Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology
No.1 University Road, Yanchao District, Kaohsiung 824005, Taiwan

*2Department of Seafood Science, National Kaohsiung University of Science and Technology
No.142 Haijhuan Road, Nanzih District, Kaohsiung 81157, Taiwan

*3Department of Intelligent Commerce, National Kaohsiung University of Science and Technology
No.58 Shenzhong Road, Yanchao District, Kaohsiung 824004, Taiwan

*4Department of Information Management, Asia Eastern University of Science and Technology
No.58, Section 2, Sihchuan Road, Banqiao District, New Taipei 220303, Taiwan

Corresponding author

July 28, 2022
December 20, 2022
March 20, 2023
fresh food, recommendation systems, text segmentation, term frequency, digital transformation

As shopping patterns have gradually shifted from offline to online mode, and with recent lockdowns during the coronavirus disease 2019 (COVID-19) pandemic restricting foreign trade and accelerating the growth of the domestic economy, digital transformation has become a major strategy for many retailers to support and expand their businesses. With the pandemic becoming a turning point, the business of major e-commerce companies in Taiwan in the retail of dry goods has grown significantly, and it has driven the online sales of fresh products as well. In this era of fierce competition, it is especially important to find a way that enables consumers to quickly find ideal fresh products on multiple platforms, shortens the time for price comparison, and improves the efficiency of online shopping. This study uses the Python programming language to write a web crawler program that captures product information from fresh food e-commerce platforms, including product introduction, price, origin, and sales volume, and then defines the relevant status of the product, such as product popularity. Accordingly, through Chinese text segmentation and term-frequency calculation, it aims to classify the product names and introductions into frequently occurring words and use them as product-related labels. Finally, the program combines the product information processing results and product-related labels to construct an online fresh food recommendation system. The results of the proposed system show that it reduces the time and energy spent comparing prices. It can also guide consumers to browse products that may be of interest using relevant tags and increase consumption efficiency by helping them find the ideal item when shopping.

Cite this article as:
T. Ou, Y. Lee, T. Chang, S. Lee, and W. Tsai, “Design and Implementation of a Recommendation System for Buying Fresh Foods Online Based on Web Crawling,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.2, pp. 271-280, 2023.
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Last updated on Jun. 19, 2024