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Distilling Lexical Product Associations into Deep Transformers: An Extreme Multi-Label Approach for Natural Language E-Commerce Search — arXiv:2609.26921

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26921
License (as archived): CC0-1.0
Archived: 2026-10-08T12:27:46.636Z
SHA-256 of the archived text: 7980e052b7b2a4e435a4c143e51de5538d2a38c4692e7b4939d97c841d9ced72
Stable URL of this page: https://metraipolis.com/documento/dc_233832ed00a7

How to cite

Distilling Lexical Product Associations into Deep Transformers: An Extreme Multi-Label Approach for Natural Language E-Commerce Search — arXiv:2609.26921 — arXiv (en), https://arxiv.org/abs/2609.26921 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_233832ed00a7 (Metraipolis archive dc_233832ed00a7, sha256 7980e052b7b2a4e435a4c143e51de5538d2a38c4692e7b4939d97c841d9ced72)

BibTeX

@misc{dc233832ed00a7,
  title = {Distilling Lexical Product Associations into Deep Transformers: An Extreme Multi-Label Approach for Natural Language E-Commerce Search — arXiv:2609.26921},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26921},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_233832ed00a7; sha256 7980e052b7b2a4e435a4c143e51de5538d2a38c4692e7b4939d97c841d9ced72},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26921. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Traditional e-commerce search platforms rely heavily on inverted indices and token-level lexical matching algorithms (e.g., BM25 and TF-IDF), which frequently fail on conversational, intent-driven, or paraphrased user queries -- the classic vocabulary mismatch problem. We formulate conversational product recommendation as an Extreme Multi-Label Classification (XMLC) problem over an e-commerce catalog of N = 54,000 products spanning 27 balanced retail categories from the Amazon Reviews '23 benchmark. Using a pre-trained DistilBERT transformer encoder, we distill dense item-to-item similarity topologies (generated via TF-IDF cosine similarity over cumulative metadata with K = 50 nearest neighbours) into a deep contextual representation via a pseudo-label knowledge distillation framework. Evaluated on an exact 85/15 train/validation split (8,089 held-out products across C = 53,923 output classes) with strict self-exclusion enforced, the DistilBERT neural student achieves P@1 = 93.15%, P@5 = 90.08%, NDCG@10 = 0.8845, and MRR@10 = 0.9545, closely recovering the empirical ceiling established by the corrected TF-IDF teacher (P@1 = 98.10%, NDCG@10 = 0.9419, MRR@10 = 0.9882). Furthermore, a qualitative benchmark across ten structured natural language query archetypes -- encompassing situational, cross-category, paraphrased, and negative-constraint queries -- demonstrates that the transformer student generalises substantially beyond keyword matching, successfully resolving implicit user intent where lexical models fail completely. Finally, we analyse the architectural and memory scalability trade-offs of extreme classification projection layers at industrial catalog scale (> 10^6 items) and present a concrete deployment trajectory toward Dual-Encoder (Two-Tower) vector search. Code: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2609.26921 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:11:32.109Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).