# Recommendation with Attribute-aware Product Networks: A Representation Learning Model

## TL;DR

This paper introduces eRAN, a novel graph-based deep learning model that leverages attribute-aware product networks to improve recommendation accuracy, explainability, and cold-start item handling in e-commerce.

## Contribution

The paper presents a new attribute network structure and a neural model eRAN that effectively combines item attributes and co-purchase data for personalized, explainable recommendations.

## Key findings

- eRAN outperforms state-of-the-art methods in accuracy.
- eRAN effectively recommends cold-start items.
- The model provides insights into user preferences and co-purchasing behaviors.

## Abstract

With the prosperity of business intelligence, recommender systems have evolved into a new stage that we not only care about what to recommend, but why it is recommended. Explainability of recommendations thus emerges as a focal point of research and becomes extremely desired in e-commerce. Existent studies along this line often exploit item attributes and correlations from different perspectives, but they yet lack an effective way to combine both types of information for deep learning of personalized interests. In light of this, we propose a novel graph structure, \emph{attribute network}, based on both items' co-purchase network and important attributes. A novel neural model called \emph{eRAN} is then proposed to generate recommendations from attribute networks with explainability and cold-start capability. Specifically, eRAN first maps items connected in attribute networks to low-dimensional embedding vectors through a deep autoencoder, and then an attention mechanism is applied to model the attractions of attributes to users, from which personalized item representation can be derived. Moreover, a pairwise ranking loss is constructed into eRAN to improve recommendations, with the assumption that item pairs co-purchased by a user should be more similar than those non-paired with negative sampling in personalized view. Experiments on real-world datasets demonstrate the effectiveness of our method compared with some state-of-the-art competitors. In particular, eRAN shows its unique abilities in recommending cold-start items with higher accuracy, as well as in understanding user preferences underlying complicated co-purchasing behaviors.