single-jc.php

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

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

Received:
July 28, 2022
Accepted:
December 20, 2022
Published:
March 20, 2023
Keywords:
fresh food, recommendation systems, text segmentation, term frequency, digital transformation
Abstract

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.
Data files:
References
  1. [1] R. Ceipek et al., “Digital transformation through exploratory and exploitative internet of things innovations: The impact of family management and technological diversification,” J. of Product Innovation Management, Vol.38, No.1, pp. 142-165, 2021. https://doi.org/10.1111/jpim.12551
  2. [2] C. Guthrie, S. Fosso-Wamba, and J. B. Arnaud, “Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown,” J. of Retailing and Consumer Services, Vol.61, Article No.102570, 2021. https://doi.org/10.1016/j.jretconser.2021.102570
  3. [3] H.-H. Chang and C. D. Meyerhoefer, “COVID-19 and the demand for online food shopping services: Empirical evidence from Taiwan,” American J. of Agricultural Economics, Vol.103, No.2, pp. 448-465, 2021. https://doi.org/10.1111/ajae.12170
  4. [4] C.-C. Chen and Y.-P. Chiu, “Advertising content and online engagement on social media during the COVID-19 epidemic in Taiwan,” J. of Marketing Communications (in press). https://doi.org/10.1080/13527266.2021.2012499
  5. [5] P. Lak et al., “A replication study on implicit feedback recommender systems with application to the data visualization recommendation,” Expert Systems, Vol.39, No.4, Article No.e12871, 2022. https://doi.org/10.1111/exsy.12871
  6. [6] M. K. Najafabadi and M. N. Mahrin, “A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback,” Artificial Intelligence Review, Vol.45, No.2, pp. 167-201, 2016. https://doi.org/10.1007/s10462-015-9443-9
  7. [7] D. Das, L. Sahoo, and S. Datta, “A survey on recommendation system,” Int. J. of Computer Applications, Vol.160, No.7, pp. 6-10, 2017. http://doi.org/10.5120/ijca2017913081
  8. [8] Q. Y. Shambour et al., “Effective hybrid content-based collaborative filtering approach for requirements engineering,” Computer Systems Science and Engineering, Vol.40, No.1, pp. 113-125, 2022. https://doi.org/10.32604/csse.2022.017221
  9. [9] N. Kumar et al., “Technical job recommendation system using APIs and web crawling,” Computational Intelligence and Neuroscience, Vol.2022, Article No.7797548, 2022. https://doi.org/10.1155/2022/7797548
  10. [10] M.-S. Jian et al., “Based on automatic correlation keyword grouping and combination based deep information search corresponding to specific language big data – Case of leisure recreation,” Proc. of 2020 22nd Int. Conf. on Advanced Communication Technology (ICACT), pp. 372-377, 2020. https://doi.org/10.23919/ICACT48636.2020.9061481
  11. [11] P. G. Chaitra et al., “A study on different types of web crawlers,” Intelligent Communication, Control and Devices (Proc. of ICICCD 2018), pp. 781-789, 2020. https://doi.org/10.1007/978-981-13-8618-3_80
  12. [12] N. S. M. Nafis and S. Awang, “The impact of pre-processing and feature selection on text classification,” Advances in Electronics Engineering (Proc. of the ICCEE 2019), pp. 269-280, 2020. https://doi.org/10.1007/978-981-15-1289-6_25
  13. [13] D. Peng et al., “Recognition of handwritten Chinese text by segmentation: A segment-annotation-free approach,” IEEE Trans. on Multimedia (in press). https://doi.org/10.1109/TMM.2022.3146771
  14. [14] X. Zhang et al., “A contrastive study of Chinese text segmentation tools in marketing notification texts,” J. of Physics: Conf. Series, Vol.1302, No.2, Article No.022010, 2019. https://doi.org/10.1088/1742-6596/1302/2/022010
  15. [15] P. Jain and P. Sharma, “Behind every good decision: How anyone can use business analytics to turn data into profitable insight,” AMACOM, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 19, 2024