AI

A Smart Instant Book Filter Model based on Dual-Objective Optimization

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 28/10/2026, 04:03:25 UTC.

Briefings

A new smart instant book filter system was developed to improve online hospitality booking rates without reducing them. The system uses dual-objective optimization to promote high-quality listings while shifting requests to instant bookings intelligently. A/B testing with global users showed the model outperformed human-expert-tuned baselines in both instant book rate and overall booking rate, resulting in improved host adoption metrics.

Key points

• The system uses dual-objective optimization to maximize instant book rate while ensuring booking rate does not decrease.
• A/B testing with global users demonstrated the model outperformed human-expert-tuned models in both instant book rate and overall booking rate.

Context

Hospitality service in online marketplaces faces challenges with delayed responses and rejection when guests try to book listings. The proposed system addresses this by intelligently filtering listings to shift requests to instant bookings without hurting booking rates.

Limitations

The evidence is limited to a single preprint (arXiv:2610.36287v1) with no experimental data or deployment metrics from production systems. The study only covers A/B testing results with global users, and no specific geographic locations or user demographics are provided.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 28/10/2026, 05:01:59 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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