10 ms·

Deep Neural Networks for YouTube Recommendations

150 points by os7borne 10y ago

Eliezer 10y ago
I noticed that YouTube's recommendations had suddenly gotten better! I wondered if they were using a new statistical approach, or had just started really optimizing at all because the old recommendations were extremely naive. I'm actually a little disappointed to find out that it might just be another deep learning thing. (Yes, it works, but I feel like you learn a little less about problem structure when what you read is mostly "we threw a generic function approximator and 30,000 hours of GPU time at it".)
- thomasahle 10y ago
  Mine seem like they have gotten a lot more click-baity. I wonder if that's an artifact of their optimization goal.
  - artifaxx 10y ago
    If people fall for the click bait(they do), then they get more views from optimizing for it. It would be more surprising if an optimization algorithm for views didn't favor this.
  - brokenmachine 10y ago
    Probably some part of it is that there's more clickbait videos being created now, which is making them get more views. It's a vicious clickbaity cycle.
- Rotonde 10y ago
  That's curious, because in my opinion YouTube recommendations haven't been any good since 2009. Instead of getting interesting, strange and niche content, I'm bombarded with videos that have >100k views, feature clickbait titles and thumbnails and are generally incredibly low effort content. Methods that work better for a population as a whole might not work better for a large subset of that population, and might even cause users to stop using features entirely. The lack of transparency in recommendation algorithms combined with the homogenizing effect of distributing low-quality content this way is something I find somewhat depressing.
  - robinduckett 10y ago
    Maybe those are just the videos that you're statistically more likely to watch through to the end based on your viewing history...
    - alphydan 10y ago
      Precisely. But that might be a local minimum. "Show him boobs and action trailers" is guaranteed to make him stay another 40min. But perhaps there is a more risky strategy that takes longer to craft and actually delivers hours and hours of content to the user (but needs to fail longer before getting there).
      - 5olidor 10y ago
        It seems like reinforcement learning would be useful, i.e. at a high level, forming a policy for recommendations would require balancing exploration (experimenting with more risky recommendations) vs. exploitation (showing you recommendations that it knows will likely lead to clicks) and using the click-throughs, time spent watching the video, etc. as reward signals. Does anyone know whether RL is used for recommendation in practical settings, and if so what is the current state of the art?
        pcovington 10y ago
        This is a very natural avenue and an active area of research at Google/Deep Mind. Stay tuned...
- AndrewKemendo 10y ago
  Throwing passive aggressive shade at what is current state of the art in machine learning is unwarranted.
  - blahi 10y ago
    Please. Deep learning is cool and all and has its applications but state if the art inplies that it,s the best for everything when in reality is that they are just very good for a very little subset of problems.
- pcovington 10y ago
  YouTube has used machine learning in recommendations for many years. We have struggled with interpretability, both while debugging mistakes made by the system and exposing plausible "reasons" to users. There was a fascinating discussion [1] about interpretability during a deep learning panel at KDD this year. [1] https://www.youtube.com/watch?v=furfdqtdAvc#t=54m25s https://www.youtube.com/watch?v=furfdqtdAvc#t=54m25s
erichocean 10y ago
Recommendation systems are a really interesting topic to study/engineer on. I think there's a lot of unexplored/undiscovered techniques still remaining.
- taeric 10y ago
  Oddly... I have grown rather weary of them. I have yet to get a surprise recommendation that I cared for. At best, I have seen people surprised that some good recommendations came out of a system. Which usually just leads to curation systems being key. And they work well, until they are gamed. And they will be gamed.
  - erichocean 10y ago
    > Oddly... I have grown rather weary of them. That's what makes the problem so interesting! Most recommendation systems are terrible, and those that aren't, are good only for the first 1-2 recommendations. And then there's Google Search, which so thoroughly demolished existing search result recommendation systems (remember Altavista?) that they now own the market and are one of the most valuable companies in history. When you finally solve a recommendation system problem in a way that actually works, it's a huge freaking deal!
  - brianberns 10y ago
    I felt the same way until I tried Spotify's Discover Weekly recommendation system. I don't know what they're doing, but I've found many songs I really enjoy that way.
    - iverjo 10y ago
      Spotify's Discover Weekly is based on collaborative filtering. Some of the techniques they use are described in these slides: http://www.slideshare.net/MrChrisJohnson/collaborative-filtering-with-spark/6 http://www.slideshare.net/MrChrisJohnson/collaborative-filte...
      - 10y ago
deleted 10y ago
[deleted]
deleted 10y ago
[deleted]
pcovington 10y ago
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.
- deleted 10y ago
  [deleted]
- AndrewKemendo 10y ago
  I'm most curious about this part: n conjugation with other product areas across Google, YouTube has undergone a fundamental paradigm shift to- wards using deep learning as a general-purpose solution for nearly all learning problems. Can you talk about how this works in practice? Is the deep learning group separate from other teams and then tackles problems from different areas as needed, or are there deep learning engineers in each project area that are building nets for each different area? Is the ML team also redesigning product architecture by building products around reinforcement learning?
  - pcovington 10y ago
    There are many close collaborations between product and research, as well as direct exchanges between different product areas. Close collaboration is key because those working directly on the product understand best the data, serving system and fundamental constraints. A recent article [1] revealed how engineers are trained in ML across Google. [1] https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70 https://backchannel.com/how-google-is-remaking-itself-as-a-m...
  - 2bitencryption 10y ago
    What I've heard from Google employees is that if you work there, you are getting training on Deep Learning for sure. It doesn't matter what team you're on, Google is now essentially a deep learning company (which sells ads).
- boulos 10y ago
  Perhaps I missed it, but there doesn't seem to be a comparison to the previous system. Is Hidden Layers "None" a good proxy?
dsco 10y ago
One of the very best recommendation engines I've encountered is the "Discover Weekly" playlist from Spotify. It's helped me reconsider my relationship to music which I basically thought was dead since I had hit a rut on exploring new artists. There's an interesting presentation of how it's created on SlideShare http://www.slideshare.net/MrChrisJohnson/from-idea-to-execution-spotifys-discover-weekly/8-Discover_Weekly_Started_in_2006 http://www.slideshare.net/MrChrisJohnson/from-idea-to-execut...
- seanwilson 10y ago
  > One of the very best recommendation engines I've encountered is the "Discover Weekly" playlist from Spotify. The addition of Discover Weekly really confused me. Shouldn't the features that create a radio station from an artist or a playlist fill this need already? Why is it only updated weekly? I haven't tried other services much but it feels like Spotify isn't doing as much as they can with recommendations.
  - makeee 10y ago
    I think it's because people are used to listening to their Playlists. It feels more natural to check your "Discover Weekly" playlist then a whole new section within Spotify. And I think the decision to only update it ever Monday was pretty genius. Most people aren't particularly excited when Monday rolls around.. but when they think about the fact that it's Monday they are likely to remember they have a brand new Discover Weekly playlist to listen to. It's one of the good things about their Monday and becomes a habit over time.
    - seanwilson 10y ago
      > I think it's because people are used to listening to their Playlists. I wonder if there's any data on how common this is. I listen to large shared playlists or the radio feature the vast majority of the time to try to find new music.
    - 2bitencryption 10y ago
      I have Android Auto in my car, and my favorite part of the terrible, traffic-ridden commute from Redmond to Bellevue is listening to my Discover Weekly playlist. Even though I only really like about 10% of what it picks, usually in that 10% I find an artist I really enjoy and dig into that.
rainy-day 10y ago
I don't feel like anyone has gotten recommendations right, even though one seemingly obvious approach has not been tried by anyone: allow ratings of favorite works across all media: movies, tv shows, books, music, radio programs, youtube videos. Make a very easy, efficient UI to add ratings. This way you will avoid superficial matches: if I just watched an excellent steampunk cartoon, let's offer a zillion of throwaway crap steampunk. It's not the steampunk part that I liked, it's that it was amazingly done. If I was a huge fan of books, movies, music, youtube picks of another user, it may be there is a deeper connection of the kind of quality we are both looking for, and so his or her recommendations would be highly relevant.
- firasd 10y ago
  You're kind of suggesting people go in reverse. Ratings were the initial way these things worked but then they moved to more implicit signals. Netflix used to be all about star ratings back in the day; now they want to measure what you're actually watching. I think the issue of a system determining whether you like the steampunk genre vs the quality of only that particular steampunk video is separate from the issue of ratings.
  - pastullo 10y ago
    But also view-time or view-count don't tell the full story of how much you liked that video. I am not happy with YT recommendations because they suggest crap videos to me and not the finest one available for that topic, just as he said. The system should rather suggest me a different topic but with the best quality/content available, rather than a super similar video with crappier quality/content.
- neeraj1987 10y ago
  Recommender systems moved from explicit feedback (like ratings) to implicit feedback precisely because users are less likely to actually rate stuff and also because ratings are subjective; by which I mean your interpretation of 3 stars(good) may not agree with mine(average). I have watched tons of movies/shows on netflix or videos on YT for that matter but have not rated a single video. To address the other part of your suggestion i.e collapse ratings/feedback across media like movies,books,etc. usually it is very difficult to have a dataset that spans multiple media across the same set of users. Even if it is present it would be too sparse (more sparse than usual for a site like YT with a continuously changing content library) to actually help. Though I agree that if anybody can get the recommender right, it is Google with the sheer amount of info it has on each user.
noiv 10y ago
I use YT mostly for new music, interesting documentaries and the occasional fun. Beside that there are one-off searches for random topics. Regarding the latter recommendation won't help, because it's not fast enough to tell me which aspect I'm missing. Regarding the former three I'd love to know whether there are users who liked the same videos. So please, recommend users not videos and let me do the rest.
qd6pwu4 10y ago
I hate that one day I happened to click one video and watched it, then youtube starts to recommend videos on the same topic day after day, even if I marked them as not interested, they still show up time to time...
- aedron 10y ago
  Or you watch something in privacy and now you can't open up Youtube in front of anyone anymore.
fatman13gg 10y ago
Go through the comments of this video https://www.youtube.com/watch?v=tGe4uWEvwe8 https://www.youtube.com/watch?v=tGe4uWEvwe8 People were literally bizarred by youtube, saying they were there by recommendation. (I have this video in the recommendations also...)
- eva1984 10y ago
  Ahh...This happens to me also, there is one day, my recommendations is flooded with this CandianMum, and I didn't really watch that much, if any, video of relationship on Youtube.
spynxic 10y ago
would it be legal to re-use this technique on another commercial project without getting consent?
catnaroek 10y ago
During the last few months, YouTube has consistently recommended me videos I wasn't interested in (to put it in polite terms), in spite of the fact by now Google knows enough about me to answer quite reliably what I'm likely to be interested in. The only explanation that I can find is that their need to show me specific videos (what do they call it nowadays? “sponsored content”?) prevails over other considerations.
kbart 10y ago
I'm skeptic about recommendation feature. My reaction to them, in these rare occasions when not get ignored all together, in all sites ranges from 'huh?' to 'wtf?'. Specifically Youtube gives geographic location too much weight -- just because I live in a certain country, it doesn't I'm interesting in all these trending local shit-pop music or stupid 'fun' videos.
- thr0waway1239 10y ago
  I can confirm this. I saw a lot of region-based, really stereotyping videos when I got a new computer and visited YouTube. While I understand they need to fill up the home page with something, I just prefer they can be a little more creative about the whole thing. After glancing through the top few, I quickly went for the search box. To their credit, once I do a few searches, the recommendations drastically improved when visiting next time. A side effect of this, of course, is that you can study all kinds of stereotyping and biases by repeating my experiment in various regions I suppose.
  - genofon 10y ago
    if they don't have any good information about you other than your location what else would you recommend rather than the most popular in that location?
    - Ntrails 10y ago
      When you lack the information to make a good recommendation, "the best we can guess from really generic or sparse information" tends to be annoying. That's really bad. Yes, I watched a bunch of Dota replays during a recent tournament. No, I don't normally go on youtube. So I watched a daily show video clip that was linked. All my "watch next" and "recommended" are Dota. That's not smart, that's aggravating. I would have been ok watching a couple more ds clips, but instead exclusively bad recommendations were made based on poor data. I dislike the idea that my world gets filtered by algorithms, but I really hate when they're obviously bad at it. Although I suppose I should be grateful that it's easily spotted when it's bad?
intoverflow2 10y ago
I'd be more interested if YouTube recommendations took into account users I've blocked. Sometimes YouTube recommends me videos with clickbaity gross thumbnails or from YouTubers I dislike or have no desire to watch but there is nothing I can do to to stop it recommending these to me, why can't I just go to these users profiles and block them and have them removed from my YT experience? Block just seems to stop people from messaging you, not from you being shown their videos by an algorithm.
supergirl 10y ago
Like others here I am also disappointed by the youtube recommended videos. So I was investigating building a better recommender myself. I was actually searching for how the youtube recommender works yesterday but could only find the 2010 paper. Now I am starting to believe that it is not the recommender that is the problem. It is that youtube consists of 99% low quality videos.
bryanrasmussen 10y ago
Is this the source of all those Recommendations that I look at Ben and Holly cartoons in the middle of the day when my daughter is at school? Or more Italian daytime television when I've just watched a video my wife never would? In short, is this the reason why there's never anything interesting for me when I go to the frontpage of youtube?!?!
londons_explore 10y ago
Your recommender seems to be trying to predict (from logs) which video the user will watch next/soon, and how much watch time it will lead to. If you used to have a bad recommendation system, and then you switch over to this system, then it will still be trained with data generated by users who saw the old recommendations, leading it to have a bias towards the same bad predictions. Is there any way around that?
londons_explore 10y ago
You seem to be doing all the right things in this paper, yet user sentiment seems to still be negative. Do you think that's because maximizing watch time and impressing users are conflicting goals, or are there perfect recommendations out there which both impress users and maximize watch time, yet they haven't yet been found?

150 points by os7borne 10y ago

I noticed that YouTube's recommendations had suddenly gotten better! I wondered if they were using a new statistical approach, or had just started really optimizing at all because the old recommendations were extremely naive. I'm actually a little disappointed to find out that it might just be another deep learning thing. (Yes, it works, but I feel like you learn a little less about problem structure when what you read is mostly "we threw a generic function approximator and 30,000 hours of GPU time at it".)

thomasahle 10y ago
Mine seem like they have gotten a lot more click-baity. I wonder if that's an artifact of their optimization goal.
- artifaxx 10y ago
  If people fall for the click bait(they do), then they get more views from optimizing for it. It would be more surprising if an optimization algorithm for views didn't favor this.
- brokenmachine 10y ago
  Probably some part of it is that there's more clickbait videos being created now, which is making them get more views. It's a vicious clickbaity cycle.
Rotonde 10y ago
That's curious, because in my opinion YouTube recommendations haven't been any good since 2009. Instead of getting interesting, strange and niche content, I'm bombarded with videos that have >100k views, feature clickbait titles and thumbnails and are generally incredibly low effort content. Methods that work better for a population as a whole might not work better for a large subset of that population, and might even cause users to stop using features entirely. The lack of transparency in recommendation algorithms combined with the homogenizing effect of distributing low-quality content this way is something I find somewhat depressing.
- robinduckett 10y ago
  Maybe those are just the videos that you're statistically more likely to watch through to the end based on your viewing history...
  - alphydan 10y ago
    Precisely. But that might be a local minimum. "Show him boobs and action trailers" is guaranteed to make him stay another 40min. But perhaps there is a more risky strategy that takes longer to craft and actually delivers hours and hours of content to the user (but needs to fail longer before getting there).
    - 5olidor 10y ago
      It seems like reinforcement learning would be useful, i.e. at a high level, forming a policy for recommendations would require balancing exploration (experimenting with more risky recommendations) vs. exploitation (showing you recommendations that it knows will likely lead to clicks) and using the click-throughs, time spent watching the video, etc. as reward signals. Does anyone know whether RL is used for recommendation in practical settings, and if so what is the current state of the art?
      - pcovington 10y ago
        This is a very natural avenue and an active area of research at Google/Deep Mind. Stay tuned...
AndrewKemendo 10y ago
Throwing passive aggressive shade at what is current state of the art in machine learning is unwarranted.
- blahi 10y ago
  Please. Deep learning is cool and all and has its applications but state if the art inplies that it,s the best for everything when in reality is that they are just very good for a very little subset of problems.
pcovington 10y ago
YouTube has used machine learning in recommendations for many years. We have struggled with interpretability, both while debugging mistakes made by the system and exposing plausible "reasons" to users. There was a fascinating discussion [1] about interpretability during a deep learning panel at KDD this year. [1] https://www.youtube.com/watch?v=furfdqtdAvc#t=54m25s https://www.youtube.com/watch?v=furfdqtdAvc#t=54m25s

erichocean 10y ago

Recommendation systems are a really interesting topic to study/engineer on. I think there's a lot of unexplored/undiscovered techniques still remaining.

taeric 10y ago
Oddly... I have grown rather weary of them. I have yet to get a surprise recommendation that I cared for. At best, I have seen people surprised that some good recommendations came out of a system. Which usually just leads to curation systems being key. And they work well, until they are gamed. And they will be gamed.
- erichocean 10y ago
  > Oddly... I have grown rather weary of them. That's what makes the problem so interesting! Most recommendation systems are terrible, and those that aren't, are good only for the first 1-2 recommendations. And then there's Google Search, which so thoroughly demolished existing search result recommendation systems (remember Altavista?) that they now own the market and are one of the most valuable companies in history. When you finally solve a recommendation system problem in a way that actually works, it's a huge freaking deal!
- brianberns 10y ago
  I felt the same way until I tried Spotify's Discover Weekly recommendation system. I don't know what they're doing, but I've found many songs I really enjoy that way.
  - iverjo 10y ago
    Spotify's Discover Weekly is based on collaborative filtering. Some of the techniques they use are described in these slides: http://www.slideshare.net/MrChrisJohnson/collaborative-filtering-with-spark/6 http://www.slideshare.net/MrChrisJohnson/collaborative-filte...
    - 10y ago

deleted 10y ago

[deleted]

deleted 10y ago

[deleted]

pcovington 10y ago

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.

deleted 10y ago
[deleted]
AndrewKemendo 10y ago
I'm most curious about this part: n conjugation with other product areas across Google, YouTube has undergone a fundamental paradigm shift to- wards using deep learning as a general-purpose solution for nearly all learning problems. Can you talk about how this works in practice? Is the deep learning group separate from other teams and then tackles problems from different areas as needed, or are there deep learning engineers in each project area that are building nets for each different area? Is the ML team also redesigning product architecture by building products around reinforcement learning?
- pcovington 10y ago
  There are many close collaborations between product and research, as well as direct exchanges between different product areas. Close collaboration is key because those working directly on the product understand best the data, serving system and fundamental constraints. A recent article [1] revealed how engineers are trained in ML across Google. [1] https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70 https://backchannel.com/how-google-is-remaking-itself-as-a-m...
- 2bitencryption 10y ago
  What I've heard from Google employees is that if you work there, you are getting training on Deep Learning for sure. It doesn't matter what team you're on, Google is now essentially a deep learning company (which sells ads).
boulos 10y ago
Perhaps I missed it, but there doesn't seem to be a comparison to the previous system. Is Hidden Layers "None" a good proxy?

dsco 10y ago

One of the very best recommendation engines I've encountered is the "Discover Weekly" playlist from Spotify. It's helped me reconsider my relationship to music which I basically thought was dead since I had hit a rut on exploring new artists. There's an interesting presentation of how it's created on SlideShare http://www.slideshare.net/MrChrisJohnson/from-idea-to-execution-spotifys-discover-weekly/8-Discover_Weekly_Started_in_2006 http://www.slideshare.net/MrChrisJohnson/from-idea-to-execut...

seanwilson 10y ago
> One of the very best recommendation engines I've encountered is the "Discover Weekly" playlist from Spotify. The addition of Discover Weekly really confused me. Shouldn't the features that create a radio station from an artist or a playlist fill this need already? Why is it only updated weekly? I haven't tried other services much but it feels like Spotify isn't doing as much as they can with recommendations.
- makeee 10y ago
  I think it's because people are used to listening to their Playlists. It feels more natural to check your "Discover Weekly" playlist then a whole new section within Spotify. And I think the decision to only update it ever Monday was pretty genius. Most people aren't particularly excited when Monday rolls around.. but when they think about the fact that it's Monday they are likely to remember they have a brand new Discover Weekly playlist to listen to. It's one of the good things about their Monday and becomes a habit over time.
  - seanwilson 10y ago
    > I think it's because people are used to listening to their Playlists. I wonder if there's any data on how common this is. I listen to large shared playlists or the radio feature the vast majority of the time to try to find new music.
  - 2bitencryption 10y ago
    I have Android Auto in my car, and my favorite part of the terrible, traffic-ridden commute from Redmond to Bellevue is listening to my Discover Weekly playlist. Even though I only really like about 10% of what it picks, usually in that 10% I find an artist I really enjoy and dig into that.

rainy-day 10y ago

I don't feel like anyone has gotten recommendations right, even though one seemingly obvious approach has not been tried by anyone: allow ratings of favorite works across all media: movies, tv shows, books, music, radio programs, youtube videos. Make a very easy, efficient UI to add ratings. This way you will avoid superficial matches: if I just watched an excellent steampunk cartoon, let's offer a zillion of throwaway crap steampunk. It's not the steampunk part that I liked, it's that it was amazingly done. If I was a huge fan of books, movies, music, youtube picks of another user, it may be there is a deeper connection of the kind of quality we are both looking for, and so his or her recommendations would be highly relevant.

firasd 10y ago
You're kind of suggesting people go in reverse. Ratings were the initial way these things worked but then they moved to more implicit signals. Netflix used to be all about star ratings back in the day; now they want to measure what you're actually watching. I think the issue of a system determining whether you like the steampunk genre vs the quality of only that particular steampunk video is separate from the issue of ratings.
- pastullo 10y ago
  But also view-time or view-count don't tell the full story of how much you liked that video. I am not happy with YT recommendations because they suggest crap videos to me and not the finest one available for that topic, just as he said. The system should rather suggest me a different topic but with the best quality/content available, rather than a super similar video with crappier quality/content.
neeraj1987 10y ago
Recommender systems moved from explicit feedback (like ratings) to implicit feedback precisely because users are less likely to actually rate stuff and also because ratings are subjective; by which I mean your interpretation of 3 stars(good) may not agree with mine(average). I have watched tons of movies/shows on netflix or videos on YT for that matter but have not rated a single video. To address the other part of your suggestion i.e collapse ratings/feedback across media like movies,books,etc. usually it is very difficult to have a dataset that spans multiple media across the same set of users. Even if it is present it would be too sparse (more sparse than usual for a site like YT with a continuously changing content library) to actually help. Though I agree that if anybody can get the recommender right, it is Google with the sheer amount of info it has on each user.

noiv 10y ago

I use YT mostly for new music, interesting documentaries and the occasional fun. Beside that there are one-off searches for random topics. Regarding the latter recommendation won't help, because it's not fast enough to tell me which aspect I'm missing. Regarding the former three I'd love to know whether there are users who liked the same videos. So please, recommend users not videos and let me do the rest.

qd6pwu4 10y ago

I hate that one day I happened to click one video and watched it, then youtube starts to recommend videos on the same topic day after day, even if I marked them as not interested, they still show up time to time...

aedron 10y ago
Or you watch something in privacy and now you can't open up Youtube in front of anyone anymore.

fatman13gg 10y ago

Go through the comments of this video https://www.youtube.com/watch?v=tGe4uWEvwe8 https://www.youtube.com/watch?v=tGe4uWEvwe8 People were literally bizarred by youtube, saying they were there by recommendation. (I have this video in the recommendations also...)

eva1984 10y ago
Ahh...This happens to me also, there is one day, my recommendations is flooded with this CandianMum, and I didn't really watch that much, if any, video of relationship on Youtube.

spynxic 10y ago

would it be legal to re-use this technique on another commercial project without getting consent?

catnaroek 10y ago

During the last few months, YouTube has consistently recommended me videos I wasn't interested in (to put it in polite terms), in spite of the fact by now Google knows enough about me to answer quite reliably what I'm likely to be interested in. The only explanation that I can find is that their need to show me specific videos (what do they call it nowadays? “sponsored content”?) prevails over other considerations.

kbart 10y ago

I'm skeptic about recommendation feature. My reaction to them, in these rare occasions when not get ignored all together, in all sites ranges from 'huh?' to 'wtf?'. Specifically Youtube gives geographic location too much weight -- just because I live in a certain country, it doesn't I'm interesting in all these trending local shit-pop music or stupid 'fun' videos.

thr0waway1239 10y ago
I can confirm this. I saw a lot of region-based, really stereotyping videos when I got a new computer and visited YouTube. While I understand they need to fill up the home page with something, I just prefer they can be a little more creative about the whole thing. After glancing through the top few, I quickly went for the search box. To their credit, once I do a few searches, the recommendations drastically improved when visiting next time. A side effect of this, of course, is that you can study all kinds of stereotyping and biases by repeating my experiment in various regions I suppose.
- genofon 10y ago
  if they don't have any good information about you other than your location what else would you recommend rather than the most popular in that location?
  - Ntrails 10y ago
    When you lack the information to make a good recommendation, "the best we can guess from really generic or sparse information" tends to be annoying. That's really bad. Yes, I watched a bunch of Dota replays during a recent tournament. No, I don't normally go on youtube. So I watched a daily show video clip that was linked. All my "watch next" and "recommended" are Dota. That's not smart, that's aggravating. I would have been ok watching a couple more ds clips, but instead exclusively bad recommendations were made based on poor data. I dislike the idea that my world gets filtered by algorithms, but I really hate when they're obviously bad at it. Although I suppose I should be grateful that it's easily spotted when it's bad?

intoverflow2 10y ago

I'd be more interested if YouTube recommendations took into account users I've blocked. Sometimes YouTube recommends me videos with clickbaity gross thumbnails or from YouTubers I dislike or have no desire to watch but there is nothing I can do to to stop it recommending these to me, why can't I just go to these users profiles and block them and have them removed from my YT experience? Block just seems to stop people from messaging you, not from you being shown their videos by an algorithm.

supergirl 10y ago

Like others here I am also disappointed by the youtube recommended videos. So I was investigating building a better recommender myself. I was actually searching for how the youtube recommender works yesterday but could only find the 2010 paper. Now I am starting to believe that it is not the recommender that is the problem. It is that youtube consists of 99% low quality videos.

bryanrasmussen 10y ago

Is this the source of all those Recommendations that I look at Ben and Holly cartoons in the middle of the day when my daughter is at school? Or more Italian daytime television when I've just watched a video my wife never would? In short, is this the reason why there's never anything interesting for me when I go to the frontpage of youtube?!?!

londons_explore 10y ago

Your recommender seems to be trying to predict (from logs) which video the user will watch next/soon, and how much watch time it will lead to. If you used to have a bad recommendation system, and then you switch over to this system, then it will still be trained with data generated by users who saw the old recommendations, leading it to have a bias towards the same bad predictions. Is there any way around that?

londons_explore 10y ago

You seem to be doing all the right things in this paper, yet user sentiment seems to still be negative. Do you think that's because maximizing watch time and impressing users are conflicting goals, or are there perfect recommendations out there which both impress users and maximize watch time, yet they haven't yet been found?