This is interesting, I've worked on a similar project before at my former workplace, but on text data. Are the 68 thousand tags organized into some kind of hierarchical taxonomy? Seems like a whole lot of tags! When you say you're looking to provide "clearer labels" for video, does that mean there's an existing set of tags, but they're messy, or does it mean you're looking to improve the API's ability to tag videos in general (i.e. finding new sources of labeled data, automating labeling using ML/AI, etc.)?
So a couple of things, we have a core set of tags, consider them categories. At the moment we have 8 or so, our 46k pieces of content have been tagged with 68k times with the aforementioned categories. New categories/tags will be added going forward.
We are primarily using text, at this time but are exploring other media and more ML-like predictive techniques, using the approach of new sources of data and tagging accordingly. I imagine the service will become more sophisticated over time.
The best way to think about it is what Film ratings provides but a degree where we utilize raw community, review data, and anything we can get our hands-on.
So for the Alexa skill we are building, imagine being able to tell save your content preferences for your friends and each member of your family, then ask if a film is suitable based on the people sitting on the sofa with you.
With this feedback I probably need to get my copy on my landing page a touch clearer.
Thanks
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This is interesting, I've worked on a similar project before at my former workplace, but on text data. Are the 68 thousand tags organized into some kind of hierarchical taxonomy? Seems like a whole lot of tags! When you say you're looking to provide "clearer labels" for video, does that mean there's an existing set of tags, but they're messy, or does it mean you're looking to improve the API's ability to tag videos in general (i.e. finding new sources of labeled data, automating labeling using ML/AI, etc.)?
Hey, 👋
So a couple of things, we have a core set of tags, consider them categories. At the moment we have 8 or so, our 46k pieces of content have been tagged with 68k times with the aforementioned categories. New categories/tags will be added going forward.
We are primarily using text, at this time but are exploring other media and more ML-like predictive techniques, using the approach of new sources of data and tagging accordingly. I imagine the service will become more sophisticated over time.
The best way to think about it is what Film ratings provides but a degree where we utilize raw community, review data, and anything we can get our hands-on.
So for the Alexa skill we are building, imagine being able to tell save your content preferences for your friends and each member of your family, then ask if a film is suitable based on the people sitting on the sofa with you.
With this feedback I probably need to get my copy on my landing page a touch clearer.
Thanks