Heterogeneous Neural Collaborative Filtering for a Business Recommender System

Abstract

In recent years, lots of techniques applied and optimized to make the recommender system better. Most of them mainly focus on the target interaction between users and items or a part of auxiliary information but hardly leverage other useful information relevant to customer transactions. In this paper, we want to propose a new model named Heterogeneous Neural Collaborative Filtering (HNCF) for learning recommender systems from two important parts: multi-aspects and multi-behaviors. The HNCF algorithm proposed is divided into four parts: Commuting similarity matrix, Multi-Layer Perceptron, Fusion by the Attention Mechanism, Multi-behavior Prediction. The model exploits characteristics of customers and properties of products from different aspects besides the aspect of purchase by building meta paths then commuting similarity matrices. Aspect-level latent factors fusion gives results as factors representing each user and item. These factors synthesizing with multi-behaviors prediction layers is the highlight of the model to make a better recommender system.