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A new approach to image-based recommender systems with the application of heatmaps maps

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Języki publikacji
EN
Abstrakty
EN
One of the fundamental issues of modern society is access to interesting and useful content. As the amount of available content increases, this task becomes more and more challenging. Our needs are not always formulated in words; sometimes we have to use complex data types like images. In this paper, we consider the three approaches to creating recommender systems based on image data. The proposed systems are evaluated on a real-world dataset. Two case studies are presented. The first one presents the case of an item with many similar objects in a database, and the second one with only a few similar items
Słowa kluczowe
Rocznik
Strony
63--72
Opis fizyczny
Bibliogr. 18 poz., rys.
Twórcy
autor
  • Department of Computer Engineering, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland Evorain Ltd. (www.evorain.com)
autor
  • Department of Computer Engineering, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland
  • Information Technology Institute University of Social Sciences, 90 - 113 Łodź, Poland
  • Department of Software of Computer Systems Dnipro University of Technology, 49005 Dnipro, Ukraine
Bibliografia
  • [1] Charu C Aggarwal. Content-based recommender systems. In Recommender systems, pages 139–166. Springer, 2016.
  • [2] Aasia Ali and Sanjay Sharma. Content based image retrieval using feature extraction with machine learning. In 2017 international conference on intelligent computing and control systems (ICICCS), pages 1048–1053. IEEE, 2017.
  • [3] Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems, pages 7–10, 2016.
  • [4] Fuhu Deng, Panlong Ren, Zhen Qin, Gu Huang, and Zhiguang Qin. Leveraging image visual features in content-based recommender system. Scientific Programming, 2018, 2018.
  • [5] Narges Yarahmadi Gharaei, Chitra Dadkhah, and Lorence Daryoush. Content-based clothing recommender system using deep neural network. In 2021 26th International Computer Conference, Computer Society of Iran (CSICC), pages 1–6. IEEE, 2021.
  • [6] Rafał Grycuk, Rafał Scherer, Alina Marchlewska, and Christian Napoli. Semantic hashing for fast solar magnetogram retrieval. Journal of Artificial Intelligence and Soft Computing Research, 12(4):299–306, 2022.
  • [7] Rafał Grycuk, Adam Wojciechowski, Wei Wei, and Agnieszka Siwocha. Detecting visual objects by edge crawling. Journal of Artificial Intelligence and Soft Computing Research, 10, 2020.
  • [8] Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 2020.
  • [9] Yun-Rou Lin, Wei-Hsiang Su, Chub-Hsien Lin, Bing-Fei Wu, Chang-Hong Lin, Hsin-Yeh Yang, and Ming-Yen Chen. Clothing recommendation system based on visual information analytics. In 2019 International Automatic Control Conference (CACS), pages 1–6. IEEE, 2019.
  • [10] Hung-Cuong Nguyen, Thi-Hao Nguyen, Jakub Nowak, Aleksander Byrski, Agnieszka Siwocha, and Van-Hung Le. Combined yolov5 and hrnet for high accuracy 2d keypoint and human pose estimation. Journal of Artificial Intelligence and Soft Computing Research, 12(4):281–298, 2022.
  • [11] Mirosław Pawlak, Gurmukh Singh Panesar, and Marcin Korytkowski. A novel method for invariant image reconstruction. Journal of Artificial Intelligence and Soft Computing Research, 11:69–80, 2021.
  • [12] Tomasz Rutkowski, Krystian Łapa, Maciej Jaworski, Radosław Nielek, and Danuta Rutkowska. On explainable flexible fuzzy recommender and its performance evaluation using the akaike information criterion. In International Conference on Neural Information Processing, pages 717–724. Springer, 2019.
  • [13] Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving 72 Piotr Woldan, Piotr Duda, Andrzej Cader, Ivan Laktionov for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806, 2014.
  • [14] Marko Tkalcic, Matevz Kunaver, Jurij Tasic, and Andrej Kosir. Personality based user similarity measure for a collaborative recommender system. In Proceedings of the 5th Workshop on Emotion in Human-Computer Interaction-Real world challenges, pages 30–37, 2009.
  • [15] Shoujin Wang, Longbing Cao, Yan Wang, Quan Z Sheng, Mehmet A Orgun, and Defu Lian. A survey on session-based recommender systems. ACM Computing Surveys (CSUR), 54(7):1–38, 2021.
  • [16] Michał Wrobel, Janusz T. Starczewski, Justyna Fijałkowska, Agnieszka Siwocha, and Christian Napoli. Handwritten word recognition using fuzzy matching degrees. Journal of Artificial Intelligence and Soft Computing Research, 11(3):229–242, 2021.
  • [17] Shiwen Wu, Fei Sun, Wentao Zhang, and Bin Cui. Graph neural networks in recommender systems: a survey. arXiv preprint arXiv:2011.02260, 2020.
  • [18] Zeyad Safaa Younus, Dzulkifli Mohamad, Tanzila Saba, Mohammed Hazim Alkawaz, Amjad Rehman, Mznah Al-Rodhaan, and Abdullah Al-Dhelaan. Content-based image retrieval using pso and k-means clustering algorithm. Arabian Journal of Geosciences, 8(8):6211–6224, 2015.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0de5c6d5-d707-4b94-a339-5026d127efa7
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