Deep Factorization Machines network with Non-linear interaction for Recommender System
Chuchu Yu, Xinmei Yang, Han Jiang · 2020
In recent years, leveraging the characteristics of users’ historical behavior to predict click-through rates (CTRs) has become a key point of interest in studies of recommender systems. Although theoretical and experimental investigations of CTR models have increased substantially, most models focus on linear feature interaction; however, crucial user characteristics in the real world are discovered implicitly by non-linear features. In this paper, we propose a novel model that integrates the advantages of linear and non-linear feature interaction. Our deep factorization machines network with non-linear interaction for recommend systems (DFNR) model identifies non-linear feature interactions by designing a new Non-linear interaction (NL-interaction) layer. We also incorporate a deeper multilayer perceptron (MLP) than other CTR models, which yields more accurate information about higher-order feature interactions. The MLP in the proposed model is unique because we use the residual structure to correct problems caused by a deeper network structure. Findings show that our DFNR model performs better on a CTR prediction task compared to other models. Results demonstrate the effective-ness of our model based on its non-linear interaction layer and deeper neural network architecture.