Hi I started diving into mahout a few days ago. I've a basic understanding of the machine learning concepts behind it, however I'm not all too familiar with mahout beyond the first 6 chapters of "Mahout in action".
I'm looking to implement the following kind of a recommendation engine (it's not about movies but it's easiest to explain in this manner): Let's say I've the Movie Lens dataset. Complete with ratings, genres etc. I'd want a recommender that would recommend only from a list of movies that are showing in cinemas right now. That would be a list of 10-20 movies out of 5000 for which there are ratings in the dataset. Given these are relatively new movies there will be a relatively low number of ratings for them. So I guess I'd have to rely on content-based recommendation of some kind. The first question is how would it affect performance if I use IDRescorer for the purpose of just displaying an ordered list of recommendations in the set of available movies (by implementing isFiltered, where the result would be false most of the time 10/5000)? I know the simple way to implement content based CF would be to implement my own ItemSimilarity based on ratings + movie genre information. However in the case of the MovieLens dataset if I combine say pearson correlation for ratings + tanimoto coefficient for genres (or whatever combination makes sense here) it degrades performance (score) slightly for that dataset compared to using pearson alone. Should I ditch this method just because of this reason? Further what would be other ways to implement content-based data in order to improve a recommender for the described use case? Is there a straightforward way to integrate content-based knowledge into a slope-one recommender? Thanks
