Abstract
Factorization Machine (FM) is an effective solution
for context-aware recommender systems (CARS)
which models second-order feature interactions by
inner product. However, it is insufficient to capture
high-order and nonlinear interaction signals. While
several recent efforts have enhanced FM with neural networks, they assume the embedding dimensions are independent from each other and model
high-order interactions in a rather implicit manner. In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations. Specifically, CFM models second-order
interactions with outer product, resulting in “images” which capture correlations between embedding dimensions. Then all generated “images” are
stacked, forming an interaction cube. 3D convolution is applied above it to learn high-order interaction signals in an explicit approach. Besides, we
also leverage a self-attention mechanism to perform
the pooling of features to reduce time complexity.
We conduct extensive experiments on three realworld datasets, demonstrating significant improvement of CFM over competing methods for contextaware top-k recommendation