A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes

TL;DR

This paper introduces HA-RNN, a novel sequential embedding model that effectively leverages heterogeneous attributes in item recommendation, addressing challenges like attribute heterogeneity and sparseness, and demonstrating superior performance on large datasets.

Contribution

The paper proposes HA-RNN, a new recurrent neural network model that incorporates hierarchical attribute inputs and attribute embeddings to improve item recommendation accuracy.

Findings

HA-RNN outperforms state-of-the-art models on large-scale datasets.

The hierarchical attribute and output embedding layers effectively handle attribute heterogeneity.

Sequence models become more effective as data scale increases.

Abstract

Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to combine recurrent neural networks which have recently shown effectiveness in item recommendations in applications such as video and music browsing. To overcome the challenges and to harvest the advantages of sequence models, we present a novel approach, Heterogeneous Attribute Recurrent Neural Networks (HA-RNN), which incorporates heterogeneous attributes and captures sequential dependencies in \textit{both} items and attributes. HA-RNN extends recurrent neural networks with 1) a hierarchical attribute combination input layer and 2) an output attribute embedding layer. We conduct extensive experiments on two large-scale datasets. The new approach show significant improvements over the state-of-the-art models. Our ablation experiments demonstrate the effectiveness of the two components to address heterogeneous attribute challenges including variable lengths and attribute sparseness. We further investigate why sequence modeling works well by conducting exploratory studies and show sequence models are more effective when data scale increases.