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 users 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.
@article{arxiv.1606.07674,
title = {Neural Autoregressive Collaborative Filtering for Implicit Feedback},
author = {Yin Zheng and Cailiang Liu and Bangsheng Tang and Hanning Zhou},
journal= {arXiv preprint arXiv:1606.07674},
year = {2016}
}
Comments
5 pages, 2 figures, accepted by DLRS2016 http://dlrs-workshop.org/