{"id":38396,"date":"2020-02-06T00:00:24","date_gmt":"2020-02-05T16:00:24","guid":{"rendered":"https:\/\/case.ntu.edu.tw\/blog\/?p=38396"},"modified":"2022-01-03T21:41:15","modified_gmt":"2022-01-03T13:41:15","slug":"youtube%e6%bc%94%e7%ae%97%e6%b3%95%e6%9b%b4%e6%96%b0%ef%bc%8c%e4%b8%80%e5%a0%b4%e5%85%a8%e7%90%83%e7%b7%9a%e4%b8%8a%e5%af%a6%e9%a9%97","status":"publish","type":"post","link":"https:\/\/case.ntu.edu.tw\/blog\/?p=38396","title":{"rendered":"YouTube\u6f14\u7b97\u6cd5\u66f4\u65b0\uff0c\u4e00\u5834\u5168\u7403\u7dda\u4e0a\u5be6\u9a57"},"content":{"rendered":"
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 <\/p>\n

\u53c3\u8003\u8cc7\u6599<\/strong><\/p>\n

    \n
  1. K. Hao, \u201cYouTube is experimenting with ways to make its algorithm even more addictive<\/a>\u201c,\u00a0MIT Technology Review<\/em>, 2019.<\/li>\n
  2. Z. Zhao et al., \u201cRecommending what video to watch next<\/a>\u201c,\u00a0Proceedings of the 13th ACM Conference on Recommender Systems \u2013 RecSys \u201919<\/i>, 2019.<\/li>\n
  3. P. Covington, J. Adams, and E. Sargin, \u201cDeep Neural Networks for YouTube Recommendations<\/a>,\u201d\u00a0Proceedings of the 10th ACM Conference on Recommender Systems \u2013 RecSys 16<\/i>, 2016.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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