TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation (2202.05387v2)

Abstract: Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent one of many entity interactions (e.g, a user re-sharing content or "following" another). Interactions from multiple relation types can encode valuable information about social network entities not fully captured by a single relation; for instance, a user's preference for accounts to follow may depend on both user-content engagement interactions and the other users they follow. In this work, we investigate knowledge-graph embeddings for entities in the Twitter HIN (TwHIN); we show that these pretrained representations yield significant offline and online improvement for a diverse range of downstream recommendation and classification tasks: personalized ads rankings, account follow-recommendation, offensive content detection, and search ranking. We discuss design choices and practical challenges of deploying industry-scale HIN embeddings, including compressing them to reduce end-to-end model latency and handling parameter drift across versions.

• The paper leverages TransE-based KGE methods to model Twitter’s multi-relational network at scale.
• The study introduces mixture embeddings and inductive inference to capture multifaceted user interests without retraining.
• The research demonstrates practical latency reduction via product quantization for real-time personalized recommendation systems.

Analysis of "TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation"

The paper "TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation" presents a comprehensive paper on embedding the Twitter Heterogeneous Information Network (HIN) to enhance personalized recommendation services. Its primary contribution lies in leveraging Knowledge Graph Embedding (KGE) techniques to address the challenges associated with modeling and embedding large-scale, rich multi-relational networks such as Twitter's.

Technical Contributions

The authors effectively apply TransE, a translating embeddings approach, to encode nodes (entities) and edges (relations) in the large-scale heterogeneous network at Twitter, called TwHIN. The embedding process encapsulates several entity types and the multitude of interactions available on the platform, such as follows, tweets, and advertisement engagements. Scalability is achieved by partitioning the network into manageable segments and leveraging distributed training methodologies via PyTorch-BigGraph.

Important technical innovations include:

1. Integration of High and Low-Coverage Relations: By modeling both high-coverage relations (e.g., users-following-other-users) and low-coverage relations (e.g., user-to-ad interactions), the embeddings capture diverse interaction patterns which might enhance the semantic richness of feature representations utilized across recommendation tasks.
2. Multimodal User Representations: The paper acknowledges the limitations of traditional embeddings in capturing the multifaceted interests of users. By employing clustering techniques, the authors propose generating mixture embeddings that accommodate multi-interest user behavior within the recommendation framework.
3. Inductive Inference Capability: A noteworthy consideration is the approach's ability to generalize representations for out-of-vocabulary entities without retraining, which is crucial given the continuous nature of user and content updates on Twitter.

Methodological and Practical Insights

The research offers significant insights into the practical deployment of industry-scale heterogeneous network embeddings:

• Scalable Embedding Generation: The authors outline a systematic approach to train embeddings for graphs with over a billion nodes and hundreds of billions of edges, highlighting how KGE methods can be efficiently utilized in large-scale settings.
• Compression for Latency Reduction: The paper addresses latency concerns in deploying embeddings by adopting product quantization for reduced memory and faster inference times, essential for real-time ranking and prediction tasks on platform-wide scales.
• Parameter Drift Mitigation: Given the dynamic nature of social interaction networks, the paper discusses techniques to limit embedding parameter drift across consecutive updates, employing strategies such as warm starts and L2 regularization to maintain stability without excessive retraining.

Implications and Future Directions

The findings substantiate the value of using complex, multi-relational data embeddings in improving recommendation quality across various Twitter use cases, such as "Who to Follow" suggestions, advertisement ranking, search ranking, and even content moderation tasks like offensive content detection. The promising results indicate potential pathways for further enhancements:

• Extending Embedding Techniques: As KGE techniques evolve, exploring more expressive models like TransH or RotatE could capture additional nuances in interactions, potentially fine-tuning performance across dynamic user and content environments.
• Enhanced Heterogeneity Modeling: While TwHIN effectively captures user-generated interactions, integrating temporal dynamics or sentiment analysis could refine embeddings to mirror real-time trends more closely.
• Cross-Platform Applicability: Similar HIN embedding strategies might be evaluated for applicability to other social network platforms, offering a path for generalized solutions to multi-relational network embedding challenges.

In summary, this paper presents an insightful and technically robust approach to embedding the Twitter HIN for enhancing recommender systems. It successfully balances theoretical models and practical deployment considerations, offering a blueprint for scalable, heterogeneous network modeling in social media contexts. Future research might look at broadening the scope and feature richness of such embeddings to harness complex social patterns effectively.