A fastText-based hybrid recommender

Facebook Research’s new fastText library can learn the meaning of metadata from the text it labels. By labelling documents with the users who read them, we used fastText to hack together a “hybrid recommender” system, able to recommend documents to users using both collaborative information (“people who read this also liked that”) and whether the text in the documents is thematically similar to things they read previously. Early signs are it performs quite well, so we’ll continue to experiment with it.

How do machines learn meaning?

Computers consist of on/off switches and process meaningless symbols. So how is it that we can hope that computers might understand the meaning of words, products, actions and documents? If most of us consider machine learning to be magic, it is because we don’t yet have an answer to this question. Here, I’ll provide an answer in the context of machines learning the meaning of words. But as we’ll see, the approach is the same everywhere.

Recommending Stack Overflow Questions with Lateral

Stack Overflow is a programming Q&A website with over 4M users and 9M posts. It is one of many such sites on a variety of topics run by StackExchange. Stack Overflow is highly successful at gameifying the answering of questions through a reputation system based on up-votes and bounties. Users can use the reputation points and badges they win to support job applications, and employers can use the reputation to find the best employees. So answering many questions and earning tons of points is something that users take very seriously. But how can I find questions that I can answer?