aiqbench analysis
TailSpec-EASE demonstrates how integrating knowledge graph priors into lightweight linear models can significantly boost long-tail discovery in recommender systems without the heavy computational cost of graph neural networks. By adapting prior strength to item popularity, this approach achieves superior accuracy and training efficiency compared to standard baselines like LightGCN and KGAT.
TailSpec-EASE: Knowledge-Graph-Regularized Linear Recommendation for Web Long-Tail Discovery
arXiv:2609.26143v1 Announce Type: cross Abstract: 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 stronger semantic guidance to long-tail items. Across four public benchmarks and a broad set of classical, linear, graph-CF, KG-aware neural, and score-level KG baselines, TailSpec-EASE attains a favorable trade-off between overall accuracy, long-tail performance, and training cost. It improves NDCG@20 by up to 24% over its counterpart without KG information. All tail-metric improvements over the no-KG counterpart are statistically significant under a paired bootstrap, and overall NDCG improves significantly on three of the four datasets. In a representative Amazon-book timing study, TailSpec-EASE trains in 37 seconds on CPU, compared with 2,584 seconds for a GPU-trained KGAT run and 15,800 seconds for CPU LightGCN, while attaining higher NDCG@20 and Tail Recall@20 on that dataset. It also remains feasible on catalogs where the global closed-form model runs out of memory.
Abstract: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 stronger semantic guidance to long-tail items. Across four public benchmarks and a broad set of classical, linear, graph-CF, KG-aware neural, and score-level KG baselines, TailSpec-EASE attains a favorable trade-off between overall accuracy, long-tail performance, and training cost. It improves NDCG@20 by up to 24% over its counterpart without KG information. All tail-metric improvements over the no-KG counterpart are statistically significant under a paired bootstrap, and overall NDCG improves significantly on three of the four datasets. In a representative Amazon-book timing study, TailSpec-EASE trains in 37 seconds on CPU, compared with 2,584 seconds for a GPU-trained KGAT run and 15,800 seconds for CPU LightGCN, while attaining higher NDCG@20 and Tail Recall@20 on that dataset. It also remains feasible on catalogs where the global closed-form model runs out of memory.
Information Retrieval (cs.IR); Machine Learning (cs.LG)
arXiv:2609.26143 [cs.IR]
(or arXiv:2609.26143v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2609.26143 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 320 words
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