Learning Disentangled Representations for Recommendation (1910.14238v1)

Abstract: User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user's preference when executing an intention. Learning representations that uncover and disentangle these latent factors can bring enhanced robustness, interpretability, and controllability. However, learning such disentangled representations from user behavior is challenging, and remains largely neglected by the existing literature. In this paper, we present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user behavior. Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e.g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately. A micro-disentanglement regularizer, stemming from an information-theoretic interpretation of VAEs, then forces each dimension of the representations to independently reflect an isolated low-level factor (e.g., the size or the color of a shirt). Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines. We further demonstrate that the learned representations are interpretable and controllable, which can potentially lead to a new paradigm for recommendation where users are given fine-grained control over targeted aspects of the recommendation lists.

• The paper presents MacridVAE, a model that disentangles high-level user intentions from specific item preferences.
• It achieves macro-level disentanglement for broad categories while preserving micro-level nuances such as color and size.
• Empirical results on five real-world datasets show significant improvements in NDCG and Recall, enhancing both accuracy and interpretability.

Learning Disentangled Representations for Recommendation

The paper "Learning Disentangled Representations for Recommendation" presents a novel approach to enhancing the robustness, interpretability, and controllability of recommender systems through disentangled representation learning. The authors propose the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) model, which aims to achieve both macro and micro disentanglement of user behavior data. This task involves separating high-level concepts, such as user intentions, from low-level preferences, such as specific attributes of items.

Problem Context and Methodological Innovation

Recommender systems often rely on extracting features from user interaction data to predict future user preferences. These interactions are influenced by various latent factors, which are usually entangled. This entanglement poses challenges in interpretability and accuracy, as the factors are not independently discernible in most existing models. The paper argues that previous models fail to address these challenges effectively due to their inability to disentangle these latent user intents from preferences.

MacridVAE achieves macro-level disentanglement by learning high-level user intentions like purchasing broad categories of items, e.g., clothes or electronics. Simultaneously, it maintains micro-level disentanglement by isolating granular preferences, such as item color or size. The approach incorporates a regularizer that stems from the information-theoretic perspective of VAEs to enforce the independence of the representation dimensions.

Empirical Results and Key Findings

The authors validate their approach using five real-world datasets, including large-scale datasets like the Netflix Prize and multiple MovieLens datasets. MacridVAE demonstrates substantial improvements over state-of-the-art baseline models, particularly on smaller datasets where disentanglement adds significant value in handling sparseness. The paper reports strong numerical results, highlighting improved NDCG and Recall metrics, indicating enhanced recommendation accuracy.

Furthermore, the learned representations exhibit high interpretability, as they align with human-understandable concepts, such as item categories and attributes. This property is crucial for building systems that can provide users with intuitive recommendations and allow them to customize their preferences actively.

Implications and Future Directions

The paper underscores the importance of disentangled representation learning in recommending systems, suggesting that such an approach offers a promising avenue for more nuanced user control and explicit preference management. By providing users with fine-grained control over aspects of recommendations, MacridVAE can support a more interactive and engaging user experience.

The use of cosine similarity over inner product for computing item-user similarity is a notable methodological choice, effectively combating mode collapse, where items are erroneously clustered in representation space. This choice further strengthens the macro-disentanglement capability of the model.

In terms of future developments, the research opens the door to exploring semi-supervised or supervised methods that can leverage external knowledge to improve the quality and interpretability of disentangled representations.

The contribution marks a significant step toward enhancing recommendation systems' transparency and user satisfaction, although further explorative studies and industrial applications will be necessary to assess scalability and adaptation across diverse domains. The potential to revolutionize user interaction paradigms within recommendation systems with controllable AI remains a compelling horizon for AI researchers and practitioners.