TailSpec-EASE: Knowledge-Graph-Regularized Linear Recommendation for Web Long-Tail Discovery
Recommender systems on Web platforms tend to over-serve popular items and neglect the long tail. Item-side knowledge graphs (KGs), often available as linked data or RDF-style Web resources, can help by connecting sparse items through shared semantic attributes. Many competitive KG-aware recommenders rely on graph neural architectures, whereas strong shallow linear models such as EASE-R typically ignore side information and may become infeasible in their global closed-form version. We introduce TailSpec-EASE, a lightweight recommender that injects a relation-aware spectral KG prior into a local closed-form reconstruction objective. The prior strength adapts to item popularity, giving stron...
| Authors | Jianru Shen |
|---|---|
| Primary category | cs.IR |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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