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What do you mean "human powered" rather than ML? How are the implicit/explicit judgements of relevance (e.g. likes, clicks, views, star ratings) that are used i
by anjc 7y ago
What do you mean "human powered" rather than ML? How are the implicit/explicit judgements of relevance (e.g. likes, clicks, views, star ratings) that are used in rec algorithms any less human than, say, a recommendation from a single trusted expert?
Personally I think it's amazing that even basic recommendation algorithms can instantly select 10 mostly relevant items from corpora of millions of irrelevant items. I think people are too used to receiving excellent recommendations and do not realise what receiving bad recommendations would be like. Hybrid systems such as Spotify's are really amazing when you think about it.
- fjabre 7y agoHuman powered in the sense that the expert system we developed uses those human likes, views, star ratings and implements recommendation features to facilitate humans recommending titles to other humans in a very straightforward way. But there is no ML compiling a profile of every genre/title you've ever expressed interest in and then using that profile to guess what you want to see next like Netflix et al.
- anjc 7y agoAt some point you presumably have to assess user-similarity in order to let similar users recommend items to each other? How's this different from e.g. user-user CF in terms of compiling everyone's info into a single model? I'm not criticising but rather just curious
- fjabre 7y agoI haven't found enough of a need for it at this point to make the application more useful as it stands without making great effort in training the necessary models to assess user-similarity for example. It is certainly faster to build an expert system that makes a lot of assumptions and iterate from there. There are plenty of use cases for ML and I hope to integrate it more into app some day to help automate repetitive tasks.