Outer Product-based Neural Collaborative Filtering

TL;DR

This paper introduces ONCF, a neural collaborative filtering model that uses outer product to explicitly model pairwise embedding correlations, enhanced by CNNs to learn high-order interactions, showing improved performance on implicit feedback datasets.

Contribution

The paper proposes a novel neural architecture, ONCF, that employs outer product for explicit correlation modeling and CNNs for high-order interaction learning in recommender systems.

Findings

Outer product improves correlation modeling between embeddings.

CNNs effectively learn high-order interactions.

ONCF outperforms existing models on benchmark datasets.

Abstract

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model. The experiment codes are available at: https://github.com/duxy-me/ConvNCF