51–60 of 102 posts
Author here - happy to answer questions about the techniques in the paper. We're super excited to finally share this work externally. Feedback about YouTube recommendations in general also welcome.
Earlier quoted context omitted.
Do you study the phenomenon of information bubbles at Google? Let's say, a German user just happens to watch some right-wing populist video claiming that we need to stop Merkel's refugee politics. The next day the user might receive plenty of recommendations in their feed that confirm the message in the first video. They happen to stumble upon a video of some party convention by an uprising German populist party, and…
What about the reverse scenario, though? Should someone who watches videos about refugee suffering be given anti-refugee video recommendations, lest they be dragged into a 'left wing ideology'? I don't see how that would be acceptable. Would Holocaust documentaries be 'diversified' with Holocaust denial videos? 'Information bubbles' have existed as long as people have had a choice of newspapers to buy and TV channels…
Earlier quoted context omitted.
What about the reverse scenario, though? Should someone who watches videos about refugee suffering be given anti-refugee video recommendations, lest they be dragged into a 'left wing ideology'? I don't see how that would be acceptable. Would Holocaust documentaries be 'diversified' with Holocaust denial videos? 'Information bubbles' have existed as long as people have had a choice of newspapers to buy and TV channels…
There is no such thing as unbiased. So they have top pick how they are going to be biased. My guess is the way that makes the most ad revenue irrespective of ideology.
Earlier quoted context omitted.
Do you study the phenomenon of information bubbles at Google? Let's say, a German user just happens to watch some right-wing populist video claiming that we need to stop Merkel's refugee politics. The next day the user might receive plenty of recommendations in their feed that confirm the message in the first video. They happen to stumble upon a video of some party convention by an uprising German populist party, and…
What about the reverse scenario, though? Should someone who watches videos about refugee suffering be given anti-refugee video recommendations, lest they be dragged into a 'left wing ideology'? I don't see how that would be acceptable. Would Holocaust documentaries be 'diversified' with Holocaust denial videos? 'Information bubbles' have existed as long as people have had a choice of newspapers to buy and TV channels…
Earlier quoted context omitted.
What about the reverse scenario, though? Should someone who watches videos about refugee suffering be given anti-refugee video recommendations, lest they be dragged into a 'left wing ideology'? I don't see how that would be acceptable. Would Holocaust documentaries be 'diversified' with Holocaust denial videos? 'Information bubbles' have existed as long as people have had a choice of newspapers to buy and TV channels…
Ah good old moral relativism.
Author here - happy to answer questions about the techniques in the paper. We're super excited to finally share this work externally. Feedback about YouTube recommendations in general also welcome.
I wish the recommendation engine had a better idea of what I liked based on the fact that I've been using Youtube for years, and I've thumbs-upped a lot of videos, and told it a lot of channels and videos that I don't like. But maybe that's just asking too much?
Author here - happy to answer questions about the techniques in the paper. We're super excited to finally share this work externally. Feedback about YouTube recommendations in general also welcome.
So, each video is mapped to fixed size vector of floats? A user's history is now a matrix of size [number of videos, embedding size]? What are the other parameters in this sentence "Importantly, the embeddings are learned jointly with all other model parameters through normal gradient descent back propagation updates."? And how do you concatenate all these into a "wide layer" when users would have histories of different length?
Author here - happy to answer questions about the techniques in the paper. We're super excited to finally share this work externally. Feedback about YouTube recommendations in general also welcome.
The recommendation system can't seem to handle outliers but maybe that's asking too much of current technology.
Author here - happy to answer questions about the techniques in the paper. We're super excited to finally share this work externally. Feedback about YouTube recommendations in general also welcome.
Could you elaborate on "we learn high dimensional embeddings for each video in a fixed vocabulary and feed these embeddings into a feedforward neural network." So, each video is mapped to fixed size vector of floats? A user's history is now a matrix of size [number of videos, embedding size]? What are the other parameters in this sentence "Importantly, the embeddings are learned jointly with all other model parameter…
This is of course not optimal, as the network should be able to learn how best to summarize the sequence. In the paper, however, we emphasize the importance of withholding certain sequential information from the classifier.
Author here - happy to answer questions about the techniques in the paper. We're super excited to finally share this work externally. Feedback about YouTube recommendations in general also welcome.
- the recommendations are very often not interesting to me because
+ they cater to the lowest common denominator (you won't believe these 10 hilarious fails, PewDiePie picks his nose, etc.)
+ I have already watched the video
+ a video has been in the Recommended section for weeks and I haven't clicked on it. What makes you think I'll change my mind after several weeks? If I don't click a video within a couple of days of it appearing in the section, it's a dud. Don't keep showing it
+ the video is from a channel I am already subscribed to. That's not a recommendation, it's trivial and not helpful
+ most or all of the videos in the section are sometimes matching the same key word. I once clicked on an Amy Schumer video, and for many days every video in the Recommended section was a Schumer video. This is terrible. The same thing happened after I clicked a Craig Ferguson video.
- the feedback UI is not streamlined. I have to click through multiple menus to be able to say: not interested in this channel
- there should be list of key words that I can specify where if the video matches one of them, don't add it to the section. Conversely, there should be a list of key words that when I specify them, the recommendation engine goes out and looks for videos matching them, and then adds some of them to the section
Is the deep learning approach already deployed for regular users? I have not seen a change in the quality of the recommendations.
Sorry to sound so negative, but I think this is a huge wasted opportunity. There is tons of amazing content on youtube, and it's often very hard to find.