KNN is not nearly as good as deeper probabilistic models. For example, if movie description mentions different subjects like "comedy" and "family", the model wo

KNN is not nearly as good as deeper probabilistic models. For example, if movie description mentions different subjects like "comedy" and "family", the model would not be able to differentiate them. It would only have a cluster "family comedies", but it won't be able to tell that a person likes "comedies". I've recently done a similar research on finding "company peers" using company descriptions. The descriptions are put through the LDA (http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) to find the topics expressed in each description. Then, similar companies are identified using K-L divergence (http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_diverg...) between their topic distributions. It worked much better than cosine similarity in my tests. My guess is that it would work better for movies too. If interested, have a look at my results here: http://akuz.me/2014/03/finding-company-peers-using-lda/ http://akuz.me/2014/03/finding-company-peers-using-lda/