歡迎來到演算法、軟體和硬體三位一體的未來世界論文書籍站
YouTube algorithm的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理
YouTube algorithm的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦Eves, Derral寫的 The Youtube Formula: How Anyone Can Unlock the Algorithm to Drive Views, Build an Audience, and Grow Revenue 和Johnson, Brian G.的 Tube Ritual: Jumpstart Your Journey to 5,000 Youtube Subscribers都 可以從中找到所需的評價。
另外網站Everything that You Need To Know about YouTube Algorithm也說明：YouTube Algorithm - A Brief History. Here's how YouTube shaped their list of algorithms according to their audience over time: Till 2012: View Count.
這兩本書分別來自 和所出版 。
國立陽明交通大學 資訊科學與工程研究所 謝秉均所指導 謝秉瑾的 貝氏最佳化的小樣本採集函數學習 （2021），提出YouTube algorithm關鍵因素是什麼，來自於貝氏最佳化、強化學習、少樣本學習、機器學習、超參數最佳化。
而第二篇論文國立政治大學 資訊科學系 蔡銘峰所指導 陳先灝的 基於使用者表示法轉換之跨領域偏好排序於推薦系統 （2021），提出因為有 推薦系統、機器學習、跨領域推薦、冷啟動問題的重點而找出了 YouTube algorithm的解答。
最後網站YouTube's recommendations still push harmful videos ...則補充：YouTube's recommendation algorithm suggests videos with misinformation, violence, hate speech and other content that violates its own ...
The Youtube Formula: How Anyone Can Unlock the Algorithm to Drive Views, Build an Audience, and Grow Revenue
為了解決YouTube algorithm 的問題，作者Eves, Derral 這樣論述：
Derral Eves is a video marketing expert, coach, and speaker. He consults some of the biggest YouTubers in the world on audience development and data-driven strategies. He is the founder of VidSummit, an exclusive annual conference for video creators and marketers. He is executive producer of the hig
hest grossing crowdfunded movie or TV project of all time.
YouTube algorithm進入發燒排行的影片
【400,000 Subscribes Special】I played soundtracks from Studio Ghibli written by Joe Hisaihi in a music store located in Taipei. Thank you all for making this happened!
↓ More info down below ↓
💬SLSTalk
Finally, 400K Subscribers. It's been a long way! We're really sorry we couldn't celebrate by doing recital, concert or fans event due to the coronavirus issues. Still, we prepared this special video for you, hope you like it.
I've played them a lot at live streaming, but I rarely do videos for Studio Ghibli Animations. Not because I don't like it, it's just because there were too many covers already on YouTube. So I think this time it's a good opportunity to make it a medley with a different style of video, adding some words I'd like to say to you, hope you like this video.
As many of you might know, maybe it's the change of time, or change of the algorithm, change of YouTube, musicians on YouTube lives much harder than 4 or 5 years ago. We all have to find another way out to keep living. So in the past few month we've been working on streaming platforms, digital albums, and now is the Patreon. It would be really helpful that fans support us with those platforms IF you're able and willing to.
And just to be clear, I won't quit doing music videos even if it keep going worse, because doing these things including performing, recording and editing is one of my personal interest, that's why I started all this in the first place, and that's something never change.
Once again, thanks for all the supporters around the world. We couldn't have done it without you. I don't know how far we can go, but as long as there's audience waiting for me, the music would never stop. No matter what way you choose to support us, we truly appreciate it from the bottom of our hearts.
2021.07.23
SLS
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🔵Patreon "Grand Piano"
Kater of Glencoe、Lyra Mao、かず K・、阿夜、清艵宮、Mikael Mazareth
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⏰Song List:
0:16 Merry-Go-Round of Life／人生のメリーゴーランド
1:36 Reprise／ふたたび
2:23 One Summer Day／あの夏へ
3:41 Carrying You／君をのせて
5:06 The Legend of Wind / 風の伝説
7:12 A Town with an Ocean View／海の見える街
■──────────────────■
#ghibli #piano #joehisaishi
貝氏最佳化的小樣本採集函數學習
為了解決YouTube algorithm 的問題，作者謝秉瑾 這樣論述：
貝氏最佳化 (Bayesian optimization, BO) 通常依賴於手工製作的採集函數 (acqui- sition function, AF) 來決定採集樣本點順序。然而已經廣泛觀察到，在不同類型的黑 盒函數 (black-box function) 下，在後悔 (regret) 方面表現最好的採集函數可能會有很 大差異。 設計一種能夠在各種黑盒函數中獲得最佳性能的採集函數仍然是一個挑戰。 本文目標在通過強化學習與少樣本學習來製作採集函數(few-shot acquisition function, FSAF)來應對這一挑戰。 具體來說，我們首先將採集函數的概念與 Q 函數 (Q
-function) 聯繫起來，並將深度 Q 網路 (DQN) 視為採集函數。 雖然將 DQN 和現有的小樣本 學習方法相結合是一個自然的想法，但我們發現這種直接組合由於嚴重的過度擬合(overfitting) 而表現不佳，這在 BO 中尤其重要，因為我們需要一個通用的採樣策略。 為了解決這個問題，我們提出了一個 DQN 的貝氏變體，它具有以下三個特徵: (i) 它 基於 Kullback-Leibler 正則化 (Kullback-Leibler regularization) 框架學習 Q 網絡的分佈(distribution) 作為採集函數這本質上提供了 BO 採樣所需的不確定性並減輕了
過度擬 合。 (ii) 對於貝氏 DQN 的先驗 (prior)，我們使用由現有被廣泛使用的採集函數誘導 學習的演示策略 (demonstration policy)，以獲得更好的訓練穩定性。 (iii) 在元 (meta) 級別，我們利用貝氏模型不可知元學習 (Bayesian model-agnostic meta-learning) 的元 損失 (meta loss) 作為 FSAF 的損失函數 (loss function)。 此外，通過適當設計 Q 網 路，FSAF 是通用的，因為它與輸入域的維度 (input dimension) 和基數 (cardinality) 無 關。通過廣
泛的實驗，我們驗證 FSAF 在各種合成和現實世界的測試函數上實現了與 最先進的基準相當或更好的表現。
Tube Ritual: Jumpstart Your Journey to 5,000 Youtube Subscribers
為了解決YouTube algorithm 的問題，作者Johnson, Brian G. 這樣論述：
Everybody begins their YouTube journey from zero.You have to start with no videos, views, or subscribers. Furthermore, more than 400 minutes of content is uploaded to YouTube each minute. To say that it’s challenging to grow a channel is an understatement! In fact, less than 3% of YouTube channel
s ever gain more than 10,000 subscribers. Yet, in a one-year period, Brian G Johnson gained 10,623 subscribers and drove over half a million video views. Truly beginning from zero. Brian had no previous YouTube success to draw from and had to learn the myriad of camera settings, editing options, and
technical details that often become a roadblock. Furthermore, he did it in a small and competitive niche, the YouTube video marketing niche. How, you ask?By researching, testing, and tweaking various video growth methods over a one-year period in order to identify why the YouTube algorithm promotes
one video over another. Ultimately, this led to the creation of a video ritual based on his findings--a series of actions according to a prescribed order. More than a mere guide, Tube Ritual is a one-year case study with the goal being to drive more views and convert more viewers into subscribers.
For those already creating videos or who want to in the future, Tube Ritual contains detailed, step-by-step information that plain works. From Branding to thumbnails, video structure, YouTube SEO, video calls to action, playlist strategies, channel strategies and more, Tube Ritual leaves no stone un
turned.
基於使用者表示法轉換之跨領域偏好排序於推薦系統
為了解決YouTube algorithm 的問題，作者陳先灝 這樣論述：
隨著電子商務、影像串流服務等線上服務平台的發展，各大服務供應商對於「精準掌握用戶喜好」等相關技術的需求也逐季提升。其中，推薦系統作為這類方法的核心技術，如何在多變的現實問題中，提出符合特定需求的解決方式，也成為近年來相關研究的主要方向。在本研究中，我們特別關心的是推薦系統中的冷啟動 (Cold Start) 問題。 冷啟動問題發生的主要原因，是因為特定情況造成的資料稀缺，比如推薦系統中的新用戶/物品等等。由於其困難性和實際應用中的無可避免，一直是推薦系統研究中，的一個具有挑戰性的問題。其中，緩解此問題的一種有效方法，是利用相關領域的知識來彌補目標領域的數據缺失問題，即所謂跨領域推薦 (Cro
ss-Domain Recommendation)。跨領域推薦的主要目的在於，在多個不同的領域中實行推薦演算法，從中描繪出用戶的個人偏好 (Personal Preference)，再利用這些資訊來補充目標領域缺少的數據，從而在某種程度上解決冷啟動問題。在本文中，我們提出了一個基於用戶轉換的的跨領域偏好排序方法（CPR），它讓用戶從源域 (Source Domain) 和目標域 (Target Domain)的物品中同時擷取資訊，並據此進行表示法學習，將其轉化為自身偏好的表示向量。通過這樣的轉換形式，CPR 將除了能有效地利用源域的資訊之外，也能直接地以此更新目標域中用戶和物品的相關表示，從而
有效地改善目標域的推薦成果。在數據實驗中，為了能有效證明 CPR 方法的能力，我們將 CPR 方法實驗在六個不同的工業級資料上，並在差異化的條件設定 (目標域全體、冷啟動用戶、共同用戶) 中進行測試，也以先進的跨領域和單領域推薦演算法做為比較基準，進行比較。最後發現，CPR 不僅成功提高目標域整體的推薦效能，針對特定的冷啟動用戶也達到相當好的成果。