3 ms·
What's merit in this context? What NLP/ML algorithms would you use? I can't think of any that are suited to or good enough for any definition of merit.
by cpa 12y ago
What's merit in this context? What NLP/ML algorithms would you use? I can't think of any that are suited to or good enough for any definition of merit.
- mercurialshark 12y agoSubstance is more accurate than "merit". You're right, not meritorious but substantive to pre-selected subject matter of interest. Using K-nearest neighbor (among others) against training documents to classify content, in areas you designate as interest. If they meet a predetermined relevance score and sufficient length range - they would then be considered for recommendation to the user.
- cpa 12y agoThat's basically what last.fm does, so that's doable and not too hard. As somebody with a formal training in ML, I'm not convinced by the quality of the results (as much as I'm not convinced but last.fm recommendations…) but it looks like there's a good business case to make.
- deleted 12y ago[deleted]
- mercurialshark 12y agoWell, it certainly would depend on the use case and the quality would be influenced by both the scoring threshold and extent/quality of training materials. Using large data sets, with a particularly difficult lowest sufficient score, could produce a small quantity of highly relevant and substantive content. If the purpose is to discover a handful of articles in a specific subcategory (ex. theoretical physics) that a user could confidently share to social media - then there is a significant utility to the service - not just a business case. What would this service have to offer for you to actually pay for it? Of course you could say you wouldn't pay for it, simply curious...