Heterogeneous Information Network Embedding for Recommendation (1711.10730v1)

Abstract: Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.

Heterogeneous Information Network Embedding for Recommendation: Overview and Analysis

Introduction

The increasing complexities in user-item interactions necessitate the consideration of heterogeneous auxiliary data for recommender systems. Traditional methods like matrix factorization (MF) are often inadequate when it comes to effectively extracting and utilizing the vast amounts of side information now available. Heterogeneous Information Networks (HINs) have emerged as a promising approach to model such data heterogeneity. Despite their potential, existing HIN-based recommendation methods predominantly rely on meta-path based similarities, which fail to fully exploit the latent structural features of users and items.

HERec: A Novel HIN Embedding Approach

The paper introduces HERec (Heterogeneous network Embedding for Recommendation), a novel approach that leverages HIN embedding to enhance recommendation performance. The key contributions of HERec lie in its ability to generate meaningful node sequences through a meta-path based random walk, transforming these embeddings via a fusion process, and integrating the transformed embeddings into an extended MF model.

Methodological Details

Meta-Path Based Random Walk

HERec deploys a meta-path based random walk strategy to construct node sequences within HINs. This method ensures that the complex semantics represented by HINs are captured effectively. By focusing on homogeneous connections filtered via type constraints, the learned embeddings exhibit more accurate and informative characteristics.

Embedding Fusion

After obtaining node embeddings from various meta-paths, HERec employs fusion functions to transform these embeddings into a unified representation suitable for recommendation tasks. Three fusion techniques are proposed:

1. Simple Linear Fusion: Combines embeddings through linear transformation with a unified weight for each meta-path.
2. Personalized Linear Fusion: Incorporates user-specific weights for each meta-path, acknowledging individual user preferences.
3. Personalized Non-Linear Fusion: Utilizes non-linear functions to enhance the expressive power of the fusion mechanism, catering to complex data relations.

Integration with Matrix Factorization

The user and item embeddings, once transformed, are integrated into an extended MF model. This integration is optimized jointly for the rating prediction task, ensuring that both the embedding and recommendation models adapt together to maximize performance.

Experimental Validation

HERec was tested on three real-world datasets: Douban Movie, Douban Book, and Yelp. The results demonstrate significant improvements over traditional baselines such as PMF and SoMF, and even state-of-the-art HIN-based methods like SemRec and DSR. The proposed approach shows an amplified impact in cold-start scenarios, outperforming competitors by considerable margins.

Implications

From a practical perspective, HERec offers a robust framework for enhancing recommendation systems by effectively utilizing heterogeneous side information. This becomes particularly beneficial in scenarios with sparse user-item interactions, where traditional methods struggle. Theoretically, this work underscores the importance of task-specific network embedding methods over generic ones. It also highlights the need for more flexible and expressive fusion mechanisms to capture the nuanced information encoded in HINs.

Future Directions

The potential applications and extensions of HERec are manifold:

• Deep Learning Methods: Incorporating sophisticated deep learning architectures like convolutional neural networks or autoencoders could further enhance the fusion process.
• Generalization: Extending the model to accommodate any node types with arbitrary meta-paths could provide a more comprehensive recommendation strategy.
• Explainability: Enhancing the interpretability of recommendations by leveraging the semantic information encoded in meta-paths could foster greater user trust and engagement.

Conclusion

HERec represents a significant advancement in the field of recommendation systems by integrating heterogeneous information network embeddings in a principled and effective manner. The extensive empirical evidence underscores its efficacy in diverse scenarios, marking a substantial step towards more intelligent and adaptive recommendation models.