Neural Autoregressive Collaborative Filtering for Implicit Feedback
This paper proposes implicit CF NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback( e.g. click/watch/browse behaviors) We first convert a user's implicit feedback into a "like" vector and a confidence vector, and then model the probability of the "like" vector, weighted by the confidence vector. The training objective of implicit CF NADE is to maximize a weighted negative log likelihood. We test the performance of implicit CF NADE on a dataset collected from a popular digital TV streaming service. More specifically, in the experiments, we describe how to convert watch counts into implicit "relative rating" and feed into implicit CF NADE. Then we compare the performance of implicit CF NADE model with the popular implicit matrix factorization approach. Experimental results show that implicit CF NADE significantly outperforms the baseline.
DLRS@RecSys