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Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals — arXiv:2608.21662

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21662
License (as archived): CC0-1.0
Archived: 2026-10-18T07:42:20.620Z
SHA-256 of the archived text: 22da33acedfb8e5b8d8376ca8511d762d8cbb22667bf3e54e0151924e18d6109
Stable URL of this page: https://metraipolis.com/documento/dc_ff2c8d36634b

How to cite

Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals — arXiv:2608.21662 — arXiv (en), https://arxiv.org/abs/2608.21662 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff2c8d36634b (Metraipolis archive dc_ff2c8d36634b, sha256 22da33acedfb8e5b8d8376ca8511d762d8cbb22667bf3e54e0151924e18d6109)

BibTeX

@misc{dcff2c8d36634b,
  title = {Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals — arXiv:2608.21662},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21662},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff2c8d36634b; sha256 22da33acedfb8e5b8d8376ca8511d762d8cbb22667bf3e54e0151924e18d6109},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21662. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The topic of explanation in recommender systems has seen steady research attention since the earliest days of the field. With some exceptions, this work has focused on the explanation of single items in a recommendation list and, especially recently, has emphasized approaches that are decoupled from the logic of the recommendation algorithm itself. Based on findings in the psychology of interpersonal communication, we propose a new task, pairwise interpretation of item rankings, asking the comparative question ``Why is item A ranked higher than item B?''. An effective solution to this task, we argue, is inherently grounded in the operation of the recommendation algorithm. We propose a class of techniques based on counterfactual learning to uncover the items in a user's profile that have contributed to the relative ranking of items. Using multiple datasets, we show that it is possible to identify such items as potential basis for comparative explanation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-25 in cs.AI).
Categorie arXiv: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.21662 — 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 2026-10-18T07:37:52.823Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).