Deep Neural Networks
1 2023-01-04T08:02:47+00:00 Jotsna Iyer 4f2bfb514a09301de0e5275ee45bf5db41479839 7 2 What are these? How are they different from other algorithms? plain 2023-01-19T11:49:36+00:00 Jotsna Iyer 4f2bfb514a09301de0e5275ee45bf5db41479839This page is referenced by:
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AI Speak : How Youtube Learns You Part 2
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The Process
Across Google, deep neural networks are now being used for machine learning.2 Based on the video model, Youtube's neural network takes videos similar to the ones already watched by the user. Then it tries to predict the watch time of each new video for a given user model and ranks them based on the prediction. The idea is then to show the 10 to 20 videos (depending on the device) with top rankings.
The process is similar to the Machine Learning model we studied earlier. First, the machine takes features from user and video models given by the programmer. It learns from training data what weight to give each feature to predict watch time correctly. And then, once tested and found to be ok, it can start predicting and recommending.Training
During training, millions of both positive and negative examples are given to the system. A positive example is when a user clicked on a video and watched for a certain time. A negative example is when the user did not click on the video or did not watch for long.2
The network takes in the user features and video features discussed under the models section of How Youtube Learns You Part 1.It adjusts the importance given to each input feature by checking whether it predicted correctly the watch time for a given video and user.
There are approximately one billion parameters(weight of each feature) to be learned on hundreds of billions of examples.2The network might also learn to disregard certain features - give it zero importance. Thus the embedding or the model the algorithm creates can be very different from what the developers envisioned.Testing
Once the network has been trained, it is tested on already available data and adjusted. Apart from accuracy of prediction, the output of the system has to be tuned by the programmer based on several value judgements. Showing videos that are too similar to already watched videos will not be very engaging. What does it really mean for a recommendation to be good? How many similar videos to show and how much diversity to introduce - both with respect to the other videos and with respect to the user history. How many of the user's interests to cover? What type of recommendations lead to immediate satisfaction and which lead to long-term use?1,3 These are all important questions to consider.
After this testing, realtime evaluation of the recommendations is done. The total watch time per set of predicted videos is measured.2 Longer a user watches the recommended set of videos, the model is considered to be more successful. Note that just looking at how many videos were clicked is not good for evaluation. Youtube evaluates its recommenders based on how many recommended videos were watched for a substantial fraction of the video, session length, time until first long watch and the fraction of logged in users with recommendations.1Interface
Finally, on to how the recommendations are presented to the viewer : How many videos to show? Should the best recommendations be presented all at once, or some should be saved for later? 3 How to display thumbnails and video titles? What other information to show? What settings can the user control? 1 Answers to these determine how Youtube keeps two billion users hooked round the clock.
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1 Davidson, J., Liebald, B., Liu, J., Nandy, P., Vleet, T., The Youtube Video Recommendation System, Proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, 2010
2 Covington, P., Adams, J., Sargin, E., Deep neural networks for Youtube Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, ACM, New York, 2016
3 Konstan, J., Terveen, L., Human-centered recommender systems: Origins, advances, challenges, and opportunities, AI Magazine, 42(3), 31-42, 2021
4 Spinelli, L., and Crovella, M., How YouTube Leads Privacy-Seeking Users Away from Reliable Information, In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '20 Adjunct), Association for Computing Machinery, New York, 244–251, 2020
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