Hello everyone I'm Vanya, from India. I'm a candidate for OPW Round9 internship with evergreen.
While discussing the idea of Awesome Box integration with Evergreen, Kathy and I discussed the possibility of making the Evergreen support for Awesome Box more interpretive using Artificial Intelligence. What if we could train the system to give weightage to people's "awesome" tags on items, depending upon how much their previous tags are appreciated by other people. For example: Let's say you tag a book to be awesome. Now, if 100 other people check that book in, and (lets say) 80 of them also tag it to be awesome- it will mean that your opinion matches a majority of people. On the other hand, if 100 other people check that book in and (say) only 5 of them tag it as awesome, this would mean that your awesome tag is not in coherence with the majority. So, in the former case, your awesome tag can be given more weightage as compared to the latter. Also, the weightage may vary according to genres. So- you may have a good taste in mystery books but your taste in classical literature might not be the same as the majority crowd. So- the weightage of your awesome tag in mystery would be higher than classical literature. We can even extend it to provide recommendations to users depending on their coherence with other users with similar taste. I am looking forward to your suggestions and feedback on this. Thank you for your time Vanya
