AAIN: Attentional aggregative interaction network for deep learning based recommender systems

Abstract

Feature engineering is a classical problem in recommender systems, and feature interactions is one of the most important parts of feature engineering. Factorization based models are widely used for explicit feature interactions. However, most current works utilize separate features to model cross features. Such a pattern limits the significance of cross features, since realistic recommendation scenarios are rich in associations between features. In this paper, we classify the basic feature interactions into sum-interaction and product-interaction, and improve the current general strategy of explicit feature interactions. Based on these theoretical studies, we propose a novel explicit feature interactions model Attentional Aggregative Interaction Network (AAIN), which models higher-order features using a cyclic explicit module. Specifically, we introduce attention mechanism for the reorganization of separate features, followed by product-interaction and higher-order features’ compression and output. The model is efficient since: 1) AAIN automatically learns high-order feature interactions and filters them with different weights. 2) AAIN optimizes the interaction between features into the interaction between feature groups, which allows for other relevant information to be considered when performing interactions. Furthermore, we integrate AAIN model with the classical deep neural network (DNN) model into a new model Deep Attentional Aggregative Interaction Network (DAAIN). Experiments on real-world datasets show that our models achieve state-of-the-art results.

Topics & Concepts