Automatic Feature Induction for Stagewise Collaborative Filtering
Abstract
Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings. In this paper we observe that different models have relative advantages in different regions of the input space. This motivates our approach of using stagewise linear combinations of collaborative filtering algorithms, with non-constant combination coefficients based on kernel smoothing. The resulting stagewise model is computationally scalable and outperforms a wide selection of state-of-the-art collaborative filtering algorithms.
Cite
Text
Lee et al. "Automatic Feature Induction for Stagewise Collaborative Filtering." Neural Information Processing Systems, 2012.Markdown
[Lee et al. "Automatic Feature Induction for Stagewise Collaborative Filtering." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/lee2012neurips-automatic/)BibTeX
@inproceedings{lee2012neurips-automatic,
title = {{Automatic Feature Induction for Stagewise Collaborative Filtering}},
author = {Lee, Joonseok and Sun, Mingxuan and Kim, Seungyeon and Lebanon, Guy},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {314-322},
url = {https://mlanthology.org/neurips/2012/lee2012neurips-automatic/}
}