Thank's for the replies Chris and Alicez! Sorry for the late response.
@Chris, thanks for the article, it was very insightful. Item_similarity is useful, but one limitation is it does not take into account the number of times a user,item pair occurs in data (i.e., user X watched movie Y k times).
Furthermore, the method of 'bucketizing' the frequency of (user,item) pairs is less than ideal in the matrix factorization setting since it adds additional parameters to the 'bucketize' function.
@Alicez, what I had in mind for implicit feedback datasets was more in line with the model described in this paper: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.5120
In this model, matrix factorization is applied to binary indicators for item, user pairs with an additional 'confidence' weight multiplying the mean, squared error terms in the objective function.
Basically, it's standard matrix factorization on binary targets with additional weights added to the objective function proportional to the number of times a user, item pair is observed.
Reading the release notes for 1.0: "Added new recommender models for implicit data". Is there any where I can take a look at the models that were added in this release?