User profiling for topic-based recommender system
I don't think that's working with lsa. 
But you maybe could do some sort of k-NN classification, where each user's coordinates are the documents viewed. Each object (=user) sends out radiation (intensity is inversely proportional to the square of the distance). The intensity is calculated from the ratings on the single documents.
Then you can place a object (user) in in this hyperdimensional space, and see what other users give the most 'light'.
But: Can't Apache Lucene do that whole stuff for you?
I'm trying to come up with a topic-based recommender system to suggest relevant text documents to users.
I trained a latent semantic indexing model, using gensim, on the wikipedia corpus. This lets me easily transform documents into the LSI topic distributions. My idea now is to represent users the same way. However, of course, users have a history of viewed articles, as well as ratings of articles.
So my question is: how to represent the users?
An idea I had is the following: represent a user as the aggregation of all the documents viewed. But how to take into account the rating?
Any ideas?
Thanks