Theoretical Article
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Year 2024, Volume: 3 Issue: 2, 36 - 44
https://doi.org/10.70395/cunas.1515178

Abstract

References

  • [1] Aravindhan, R., Shanmugalakshmi, R. (2014, October 28). Comparative analysis of Web 3.0 search engines: A survey report. ICACCS 2013 - Proceedings of the 2013 International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Around the Globe. https://doi.org/10.1109/ICACCS.2013.6938715
  • [2] López-Ortiz, A. (2005). Search engines and web information retrieval. Lecture Notes in Computer Science, 3405, 183–191. https://doi.org/10.1007/11527954_18
  • [3] Sánchez, D., Martínez-Sanahuja, L., Batet, M. (2018). Survey and evaluation of web search engine hit counts as research tools in computational linguistics. Information Systems, 73, 50–60. https://doi.org/10.1016/j.is.2017.12.007
  • [4] Harris, Z. S. (1954). Distributional Structure. WORD, 10(2–3), 146–162. https://doi.org/10.1080/00437956.1954.11659520
  • [5] Salton, G., Wong, A., Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11), 613–620. https://doi.org/10.1145/361219.361220
  • [6] Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. In undefined. http://ronan.collobert.com/senna/
  • [7] Pennington, J., Socher, R., Manning, C. D. (2014). GloVe: Global vectors for word representation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1532–1543. https://doi.org/10.3115/v1/d14-1162
  • [8] Fan, J., Gao, X., Wang, T., Liu, R., Yang, Y. (2021). Research and Application of Automated Search Engine Based on Machine Learning. International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 69–73. https://doi.org/10.1109/HPBDIS53214.2021.9658474
  • [9] Tautkute, I., Trzcinski, T., Skorupa, A. P., Lukasz, K., Marasek, K. (2019). DeepStyle: Multimodal Search Engine for Fashion and Interior Design. IEEE Access, 7, 84613–84628. https://doi.org/10.1109/ACCESS.2019.2923552
  • [10] Karwa, R., Honmane, V. (2019). Building search engine using machine learning technique. International Conference on Intelligent Computing and Control Systems, 1061–1064. https://doi.org/10.1109/ICCS45141.2019.9065846
  • [11] Yoganarasimhan, H. (2019). Search Personalization Using Machine Learning. Management Science, 66(3), 1045–1070. https://doi.org/10.1287/MNSC.2018.3255
  • [12] Li, Y., Jiang, Y., Yang, C., Yu, M., Kamal, L., Armstrong, E. M., Huang, T., Moroni, D., McGibbney, L. J. (2020). Improving search ranking of geospatial data based on deep learning using user behavior data. Computers & Geosciences, 142, 104520. https://doi.org/10.1016/J.CAGEO.2020.104520
  • [13] Gupta, S., Tiwari, K. K. (2024). Search Query Refinement Using Context, Knowledge and Long-term Memorization. Asian Journal of Research in Computer Science, 17(2), 27-36.
  • [14] Murshleen, M., Prabhu, A., Jain, G. (2024). Personalized Collaborative Search Engine for Decision Making. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 202-206). IEEE.
  • [15] Raghuvanshi, M. K., Bele, M. S. (2024). Novel Design and Implementation of the Personalized Search Engine in Context with User Keywords Profile and Keywords Optimization. Recent Advancements in Science and Technology, 306.
  • [16] Zhou, Y., Zhu, Q., Jin, J., Dou, Z. (2024). Cognitive personalized search integrating large language models with an efficient memory mechanism. In Proceedings of the ACM on Web Conference 2024 (pp. 1464-1473).

Design and Development of a Fashion Oriented Personalized Search Engine

Year 2024, Volume: 3 Issue: 2, 36 - 44
https://doi.org/10.70395/cunas.1515178

Abstract

Finding the desired product in the e-commerce sector in the fastest and easiest way plays a vital role in customer satisfaction and revenue growth. Some interactive search engines have already been proposed in the literature and allow for text or visual queries. Nevertheless, these studies focus on finding items aesthetically similar to the query without considering the query personalization aspects. Query personalization allows that user preferences are provided as a user profile separately from the query and dynamically decide how this profile will affect the query results. In this study, a personalized search engine is proposed, which is fed with the product data of the e-commerce site trendyol.com operating in the fashion-oriented retailing sector. More specifically, a search engine has been developed to recognize and help online shoppers find what they are looking for and discover a broader and more relevant range of products in the trendyol.com catalog. The index, search, and data collection infrastructures and a brand-based user-segmented product listing algorithm have been designed and implemented to realize the search engine. As the outcome of the study, a fashion-oriented and personalized site search has been enabled thar successfully reveals products that have never been thought of before by directly associating the products the customers want. The results show that personalizing the search queries increase the odds of success. With the development of the personalized search engine, it is expected that Trendyol’s revenues will grow in a short time through users visiting the site.

References

  • [1] Aravindhan, R., Shanmugalakshmi, R. (2014, October 28). Comparative analysis of Web 3.0 search engines: A survey report. ICACCS 2013 - Proceedings of the 2013 International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Around the Globe. https://doi.org/10.1109/ICACCS.2013.6938715
  • [2] López-Ortiz, A. (2005). Search engines and web information retrieval. Lecture Notes in Computer Science, 3405, 183–191. https://doi.org/10.1007/11527954_18
  • [3] Sánchez, D., Martínez-Sanahuja, L., Batet, M. (2018). Survey and evaluation of web search engine hit counts as research tools in computational linguistics. Information Systems, 73, 50–60. https://doi.org/10.1016/j.is.2017.12.007
  • [4] Harris, Z. S. (1954). Distributional Structure. WORD, 10(2–3), 146–162. https://doi.org/10.1080/00437956.1954.11659520
  • [5] Salton, G., Wong, A., Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11), 613–620. https://doi.org/10.1145/361219.361220
  • [6] Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. In undefined. http://ronan.collobert.com/senna/
  • [7] Pennington, J., Socher, R., Manning, C. D. (2014). GloVe: Global vectors for word representation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1532–1543. https://doi.org/10.3115/v1/d14-1162
  • [8] Fan, J., Gao, X., Wang, T., Liu, R., Yang, Y. (2021). Research and Application of Automated Search Engine Based on Machine Learning. International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 69–73. https://doi.org/10.1109/HPBDIS53214.2021.9658474
  • [9] Tautkute, I., Trzcinski, T., Skorupa, A. P., Lukasz, K., Marasek, K. (2019). DeepStyle: Multimodal Search Engine for Fashion and Interior Design. IEEE Access, 7, 84613–84628. https://doi.org/10.1109/ACCESS.2019.2923552
  • [10] Karwa, R., Honmane, V. (2019). Building search engine using machine learning technique. International Conference on Intelligent Computing and Control Systems, 1061–1064. https://doi.org/10.1109/ICCS45141.2019.9065846
  • [11] Yoganarasimhan, H. (2019). Search Personalization Using Machine Learning. Management Science, 66(3), 1045–1070. https://doi.org/10.1287/MNSC.2018.3255
  • [12] Li, Y., Jiang, Y., Yang, C., Yu, M., Kamal, L., Armstrong, E. M., Huang, T., Moroni, D., McGibbney, L. J. (2020). Improving search ranking of geospatial data based on deep learning using user behavior data. Computers & Geosciences, 142, 104520. https://doi.org/10.1016/J.CAGEO.2020.104520
  • [13] Gupta, S., Tiwari, K. K. (2024). Search Query Refinement Using Context, Knowledge and Long-term Memorization. Asian Journal of Research in Computer Science, 17(2), 27-36.
  • [14] Murshleen, M., Prabhu, A., Jain, G. (2024). Personalized Collaborative Search Engine for Decision Making. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 202-206). IEEE.
  • [15] Raghuvanshi, M. K., Bele, M. S. (2024). Novel Design and Implementation of the Personalized Search Engine in Context with User Keywords Profile and Keywords Optimization. Recent Advancements in Science and Technology, 306.
  • [16] Zhou, Y., Zhu, Q., Jin, J., Dou, Z. (2024). Cognitive personalized search integrating large language models with an efficient memory mechanism. In Proceedings of the ACM on Web Conference 2024 (pp. 1464-1473).
There are 16 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Bahadır Yalın 0009-0003-9005-7405

Akasya Akyuz 0000-0003-3456-2936

Fatih Abut 0000-0001-5876-4116

Mehmet Fatih Akay 0000-0003-0780-0679

Ceren Ulus 0000-0003-2086-6381

Early Pub Date December 11, 2024
Publication Date
Submission Date July 12, 2024
Acceptance Date August 1, 2024
Published in Issue Year 2024 Volume: 3 Issue: 2

Cite

APA Yalın, B., Akyuz, A., Abut, F., Akay, M. F., et al. (2024). Design and Development of a Fashion Oriented Personalized Search Engine. Cukurova University Journal of Natural and Applied Sciences, 3(2), 36-44. https://doi.org/10.70395/cunas.1515178
AMA Yalın B, Akyuz A, Abut F, Akay MF, Ulus C. Design and Development of a Fashion Oriented Personalized Search Engine. Cukurova University Journal of Natural and Applied Sciences. December 2024;3(2):36-44. doi:10.70395/cunas.1515178
Chicago Yalın, Bahadır, Akasya Akyuz, Fatih Abut, Mehmet Fatih Akay, and Ceren Ulus. “Design and Development of a Fashion Oriented Personalized Search Engine”. Cukurova University Journal of Natural and Applied Sciences 3, no. 2 (December 2024): 36-44. https://doi.org/10.70395/cunas.1515178.
EndNote Yalın B, Akyuz A, Abut F, Akay MF, Ulus C (December 1, 2024) Design and Development of a Fashion Oriented Personalized Search Engine. Cukurova University Journal of Natural and Applied Sciences 3 2 36–44.
IEEE B. Yalın, A. Akyuz, F. Abut, M. F. Akay, and C. Ulus, “Design and Development of a Fashion Oriented Personalized Search Engine”, Cukurova University Journal of Natural and Applied Sciences, vol. 3, no. 2, pp. 36–44, 2024, doi: 10.70395/cunas.1515178.
ISNAD Yalın, Bahadır et al. “Design and Development of a Fashion Oriented Personalized Search Engine”. Cukurova University Journal of Natural and Applied Sciences 3/2 (December 2024), 36-44. https://doi.org/10.70395/cunas.1515178.
JAMA Yalın B, Akyuz A, Abut F, Akay MF, Ulus C. Design and Development of a Fashion Oriented Personalized Search Engine. Cukurova University Journal of Natural and Applied Sciences. 2024;3:36–44.
MLA Yalın, Bahadır et al. “Design and Development of a Fashion Oriented Personalized Search Engine”. Cukurova University Journal of Natural and Applied Sciences, vol. 3, no. 2, 2024, pp. 36-44, doi:10.70395/cunas.1515178.
Vancouver Yalın B, Akyuz A, Abut F, Akay MF, Ulus C. Design and Development of a Fashion Oriented Personalized Search Engine. Cukurova University Journal of Natural and Applied Sciences. 2024;3(2):36-44.