TAG-HGT: A Scalable and Cost-Effective Framework for Inductive Cold-Start Academic Recommendation

TL;DR

This paper introduces TAG-HGT, a scalable, cost-effective framework for inductive cold-start academic recommendation that combines semantic and structural information, achieving state-of-the-art accuracy with vastly reduced inference latency and costs.

Contribution

It proposes a neuro-symbolic framework that distills large language model knowledge into a lightweight graph transformer, enabling real-time, large-scale academic recommendations.

Findings

Achieves SOTA System Recall@10 of 91.97% on OpenAlex dataset.

Reduces inference latency by 500,000 times compared to generative models.

Cuts inference costs by over 99.9%, enabling democratized access.

Abstract

Inductive cold-start recommendation remains the "Achilles' Heel" of industrial academic platforms, where thousands of new scholars join daily without historical interaction records. While recent Generative Graph Models (e.g., HiGPT, OFA) demonstrate promising semantic capabilities, their prohibitive inference latency (often exceeding 13 minutes per 1,000 requests) and massive computational costs render them practically undeployable for real-time, million-scale applications. To bridge this gap between generative quality and industrial scalability, we propose TAG-HGT, a cost-effective neuro-symbolic framework. Adopting a decoupled "Semantics-First, Structure-Refined" paradigm, TAG-HGT utilizes a frozen Large Language Model (DeepSeek-V3) as an offline semantic factory and distills its knowledge into a lightweight Heterogeneous Graph Transformer (HGT) via Cross-View Contrastive Learning (CVCL). We present a key insight: while LLM semantics provide necessary global recall, structural signals offer the critical local discrimination needed to distinguish valid collaborators from semantically similar but socially unreachable strangers in dense embedding spaces. Validated under a strict Time-Machine Protocol on the massive OpenAlex dataset, TAG-HGT achieves a SOTA System Recall@10 of 91.97%, outperforming structure-only baselines by 20.7%. Most significantly, from an industrial perspective, TAG-HGT reduces inference latency by five orders of magnitude ($4.5 \times 10^{5}\times$) compared to generative baselines (from 780s down to 1.73 ms), and slashes inference costs from $\sim$$1.50 to $<$$0.001 per 1k queries. This 99.9% cost reduction democratizes high-precision academic recommendation.