Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

arXiv:2204.11602v5 Announce Type: replace-cross
Abstract: Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users.However, the DNNs-based models usually suffer from high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we propose a new broad recommender system called Broad Collaborative Filtering (BroadCF), which is an efficient …

abstract arxiv capability collaborative collaborative filtering complexity computational cs.ir cs.lg filtering networks neural networks recommendation relationships results type