Incremental Recommendation via Causal Models
Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no incremental value and displaces other recommendations that could. We address this by extending an existing production recommendation model to a causal architecture using holdback data that is already collected as part of routine experimentation infrastructure, requiring no new data collection. A central challenge is that attribution windows differ between treated and holdback observations: treated users are attributed a stream within a short direct-response window, while holdback users are attributed organic streams over a multi-day window. Thi...
| Authors | Athanasios Vlontzos, David Gustafsson, Michael O'Riordan et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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