arXiv (All)
open access
Enriching Sequential Recommendation with Graph Laplacian Positional Embeddings
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Redundancy Reduction Approach for Controllable Sequential Recommendations#1050041
- Higher-Order Positional Encodings for Graph Representation Learning#1157174
- Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs#1109667
- Retrieved but not ranked: surface-form bias in structural retrieval, from mathematics to agent trajectories#821585
- A user behavior inertia based spatio temporal next POI recommendation model.#221101
Record · ID 1105811
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive