HORIZON: A Benchmark for In-the-wild User Behaviour Modeling
User behavior in the real world is diverse, cross-domain, and spans long time horizons. Existing user modeling benchmarks however remain narrow, focusing mainly on short sessions and next-item prediction within a single domain. Such limitations hinder progress toward robust and generalizable user models. We present HORIZON, a new benchmark that reformulates user modeling along three axes i.e. dataset, task, and evaluation. Built from a large-scale, cross-domain reformulation of Amazon Reviews, HORIZON covers 54M users and 35M items, enabling both pretraining and realistic evaluation of models in heterogeneous environments. Unlike prior benchmarks, it challenges models to generalize across...
| Authors | Arnav Goel, Pranjal A Chitale, Bhawna Paliwal et al. |
|---|---|
| Primary category | cs.IR |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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