A collaborative filtering model with heterogeneous neural networks for recommender systems

TL;DR

This paper introduces a hybrid neural network model with heterogeneous structures to improve collaborative filtering in recommender systems, addressing complexity and accuracy issues associated with deep neural networks.

Contribution

It proposes a novel hybrid neural network approach combining different structures to enhance recommendation accuracy and efficiency.

Findings

Outperforms state-of-the-art methods in item ranking

Addresses complexity and accuracy issues of deep neural networks

Demonstrates effectiveness on real datasets

Abstract

In recent years, deep neural network is introduced in recommender systems to solve the collaborative filtering problem, which has achieved immense success on computer vision, speech recognition and natural language processing. On one hand, deep neural network can be used to model the auxiliary information in recommender systems. On the other hand, it is also capable of modeling nonlinear relationships between users and items. One advantage of deep neural network is that the performance of the algorithm can be easily enhanced by augmenting the depth of the neural network. However, two potential problems may emerge when the deep neural work is exploited to model relationships between users and items. The fundamental problem is that the complexity of the algorithm grows significantly with the increment in the depth of the neural network. The second one is that a deeper neural network may undermine the accuracy of the algorithm. In order to alleviate these problems, we propose a hybrid neural network that combines heterogeneous neural networks with different structures. The experimental results on real datasets reveal that our method is superior to the state-of-the-art methods in terms of the item ranking.