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arXiv · 2609.31253

Enriching Sequential Recommendation with Graph Laplacian Positional Embeddings

Ekaterina Trushkova·Artur Gimranov·Anton Lysenko
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Abstract

Sequential recommenders typically rely on learnable positional embeddings to encode the order of user interactions. In this work, we ask whether this ordinal signal can be replaced by a structural one derived from the item space. We propose to use Laplacian positional embeddings in SASRec: we build an item co-occurrence graph from training interactions, compute eigenvectors of its symmetric normalized Laplacian, and use them as frozen graph-derived positional embeddings. The backbone architecture and training objective remain unchanged. Experiments on four public sequential-recommendation benchmarks show that this simple replacement improves SASRec performance on most ranking metrics and remains competitive with strong positional and temporal encoding baselines. These findings indicate that item-item graph structure can be an effective substitute for standard ordinal positional embeddings in sequential recommendation.

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Ekaterina Trushkova, Artur Gimranov, Anton Lysenko. 2026-09-25. Enriching Sequential Recommendation with Graph Laplacian Positional Embeddings. https://arxiv.org/abs/2609.31253

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