Stavrosk,

If what you want is to only classify the stories in the front page and classify them based on a preset of categories, that's actually pretty simple to do.

I been working on a similar concept for personal project. Here are my recommendations:

- Be sure to remove stopwords from the titles before using the classifier. - The ankusa gem will help you greatly https://github.com/bmuller/ankusa

Ankusa is a naive bayesian text classifier that will come really handy for the task you are trying to achieve.

Also make sure your training data sets are pretty clean and with little overlapping as possible.

Finally have fun and let us knows how it goes!!

Cheers and let me know if you have more questions or if you want a hand coding this thing.

Thanks for your answer! What I'm thinking of making is basically separating posts into two categories, things that interest me and things that don't. Then, I want to receive emails at intervals I specify. This is so I no longer have the urge to check HN frequently, but still stay up t date.

The actual classification is probably the easy part, the hard part is training the model, which is why I wanted to ask if anyone had done it before. Have you managed to train anything to recognize your tastes, or is it objective categories? How well does it work?

Well, my classifier works based on categories like ruby, programming, php, magento etc.

To train the classifier I grabbed feeds from different reddits and used that as a based data set. What you are trying to achieve sounds more like a recommendation engine rather than a classifier maybe recommendify might come handy https://github.com/paulasmuth/recommendify

You still can use the bayesian classifier, for training it I would recommend the supervised training route, basically start with a small dataset(100 records) and manually classify each of the training examples.

Also you should leave some sort of way to provide feedback to your classifier to improve the results and make corrections

Yeah, I'll have upvotes and downvotes to tell it what I liked or didn't. Unfortunately, I can't see a way to do this without supervised learning (maybe semi-supervised would work), which is why I posted here for ideas (I want to avoid the costly supervision step if someone knows the result won't work).

Thanks for your comments, they help a lot.

I suppose you could train the classifier by having it record what you upvote, or which links you click on. Perhaps a Firefox/Chrome extension could do that?

Some people at Reddit were programming a recommender about a year ago: http://www.reddit.com/r/redditdev/comments/lowwf/attempt_2_w... It doesn't use a Naive Bayesian Classifier but it might still interest you.

I'm currently using a very simple bookmarklet scheme, one for upvote and one for downvote. It works very well for collecting data, I'll train it later tonight, I think.

Thank you for the link, it looks very extensive, I'll peruse it later on.

I've been thinking about a very similar idea. Mostly so I don't feel the compulsion to check the internet so frequently, the good stuff would just wait until I do.

Would searching through the text be enough though? If you could get several people to use it that would give it more information. You could rank content based on whether or not someone else with similar interests has liked it on top of that.

There are various things you can do and various machine learning techniques you can use, but I imagine that the single user version would be enough, to start with.

I'll give it a go and see if it works well. If it does, I might release it as a service.

That would be cool. The basic info might be enough. The thing about the previous guy who tried something like this which was posted above is, he was mostly sorting by the words in the title which seems like only a very weak predictor. Better would be the number of comments and votes, and maybe other stuff like how long the article is or whether or not certain words are in the comments. He also trained it on whether he thought it sounded interesting, not after reading the article and determining if it actually was.

I plan to implement votes (maybe), domain, actual raw text of the article, title, submitter (maybe) and show articles that are deemed "important" (i.e. have stayed on the front page for longer than X hours), as well as some random ones, to avoid a bubble. Plus, I've already started training the filter manually, I'll maybe write a simple web UI later on so I can up/downvote articles from there.

I think that should give a good first draft.

Here is a story from the beginning of last year of someone who did this. http://joelgrus.com/2012/02/16/hacking-hacker-news/

Link to HN Thread: https://hackernews.hn/item?id=3602407

That's exactly what I was looking for, thank you. He doesn't include results, but has some good ideas.