Evolving Deep Neural Networks for Collaborative Filtering

TL;DR

This paper introduces a genetic algorithm-based method to automatically design deep neural networks for collaborative filtering, improving recommendation accuracy without requiring manual architecture engineering.

Contribution

It presents a novel approach combining genetic algorithms with DNN design for collaborative filtering, automating architecture creation and outperforming manual models.

Findings

Outperforms state-of-the-art manually designed neural networks

Demonstrates effectiveness on benchmark datasets

Automates neural network architecture design

Abstract

Collaborative Filtering (CF) is widely used in recommender systems to model user-item interactions. With the great success of Deep Neural Networks (DNNs) in various fields, advanced works recently have proposed several DNN-based models for CF, which have been proven effective. However, the neural networks are all designed manually. As a consequence, it requires the designers to develop expertise in both CF and DNNs, which limits the application of deep learning methods in CF and the accuracy of recommended results. In this paper, we introduce the genetic algorithm into the process of designing DNNs. By means of genetic operations like crossover, mutation, and environmental selection strategy, the architectures and the connection weights initialization of the DNNs can be designed automatically. We conduct extensive experiments on two benchmark datasets. The results demonstrate the proposed algorithm outperforms several manually designed state-of-the-art neural networks.