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[Μάθε πως Μαθαίνουν] Προτεινόμενα Βίντεο στο Youtube

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Εσυ είσαι περήφανος/η για το feed σου από προτεινόμενα βίντεο στο Υoutube;
Πως δουλεύουν οι προτάσεις του Youtube και πως μαθαίνει τα γούστα σου για να σου προσφέρει προσωποποιημένες προτάσεις;
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Background Music: Lurking Sloth . Written by Alexander Nakarada
Creative Commons by 4.0 - Attribution licensed music
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Πηγές:
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1. Davidson, James, et al. "The YouTube video recommendation system." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.
2. Zhou, Renjie, Samamon Khemmarat, and Lixin Gao. "The impact of YouTube recommendation system on video views." Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, 2010.
3. Deshpande, Mukund, and George Karypis. "Item-based top-n recommendation algorithms." ACM Transactions on Information Systems (TOIS) 22.1 (2004): 143-177.
4. Isinkaye, F. O., Y. O. Folajimi, and B. A. Ojokoh. "Recommendation systems: Principles, methods and evaluation." Egyptian Informatics Journal 16.3 (2015): 261-273.
5. Sinha, Rashmi R., and Kirsten Swearingen. "Comparing recommendations made by online systems and friends." DELOS. 2001.
6. Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 1 (2003): 76-80.
7. Brusilovski, Peter, Alfred Kobsa, and Wolfgang Nejdl, eds. The adaptive web: methods and strategies of web personalization. Vol. 4321. Springer Science & Business Media, 2007.
#YoutubeRecommendations #topN #TechToMeAboutIt #mathepwsmathainoyn
Πως δουλεύουν οι προτάσεις του Youtube και πως μαθαίνει τα γούστα σου για να σου προσφέρει προσωποποιημένες προτάσεις;
====================================
====================================
Background Music: Lurking Sloth . Written by Alexander Nakarada
Creative Commons by 4.0 - Attribution licensed music
====================================
Πηγές:
------------
1. Davidson, James, et al. "The YouTube video recommendation system." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.
2. Zhou, Renjie, Samamon Khemmarat, and Lixin Gao. "The impact of YouTube recommendation system on video views." Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, 2010.
3. Deshpande, Mukund, and George Karypis. "Item-based top-n recommendation algorithms." ACM Transactions on Information Systems (TOIS) 22.1 (2004): 143-177.
4. Isinkaye, F. O., Y. O. Folajimi, and B. A. Ojokoh. "Recommendation systems: Principles, methods and evaluation." Egyptian Informatics Journal 16.3 (2015): 261-273.
5. Sinha, Rashmi R., and Kirsten Swearingen. "Comparing recommendations made by online systems and friends." DELOS. 2001.
6. Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 1 (2003): 76-80.
7. Brusilovski, Peter, Alfred Kobsa, and Wolfgang Nejdl, eds. The adaptive web: methods and strategies of web personalization. Vol. 4321. Springer Science & Business Media, 2007.
#YoutubeRecommendations #topN #TechToMeAboutIt #mathepwsmathainoyn
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