User Behaviour Analysis & Recommender Systems Quarterly Report — Q1 2026

Q1 2026 marked an inflection point for industrial recommendation systems as LLM-based ranking moved from proof-of-concept into selective production deployment. Meta's publication of LWRank — a large-language-model ranking layer applied… 1. Executive Summary Q1 2026 marked an inflection point for industrial recommendation systems as LLM-based ranking moved from proof-of-concept into selective production deployment. Meta's publication of LWRank — a large-language-model ranking layer applied to Reels and Feed — demonstrated an 8% engagement lift over their existing two-tower retrieval baseline, accelerating industry interest in hybrid neural-LLM pipelines. Netflix confirmed LLM-enhanced personalisation results at WSDM 2026, citing improved cold-start coverage and richer contextual understanding of item semantics. On the regulatory side, Google's Privacy Sandbox timeline was updated again, granting publishers additional runway but also deepening uncertainty about third-party signal availability into 2027. Several US states (New Jersey, Tennessee, Indiana) saw newly enacted comprehensive privacy laws come into effect during Q1, adding to the compliance surface for any personalisation system collecting behavioural data at scale. Collectively these forces are accelerating the shift toward first-party data architectures, consent-based profiling, and LLM-assisted recommendation pipelines that can reason over item content rather than relying solely on historical click patterns. --- 2. Technical Updates — Previous Quarter (Q4 2025) 2.1 Recommendation Engine Updates Q4 2025 saw the core two-tower retrieval model re-trained on an expanded 18-month interaction corpus (versus the previous 12-month window), improving recall@100 from 71.2% to 74.8% on holdout sets. A SASRec-v3 sequential model (successor to the BERT4Rec family) was promoted to production for logged-in users, replacing a GRU-based session model that had been in place since Q2 2024. A/B testing over a six-week holdout (October–November 2025) showed: - Recommendation CTR increased from 3.41% to 3.79% (+11.1% relative) - Conversion lift from recommendations increased from 7.2% to 8.6% - Catalogue coverage improved marginally from 38% to 41% of available items surfaced at least once per day An LLM-as-re-ranker prototype was run as a shadow experiment against 5% of traffic in December 2025 using a quantised 7B parameter model. Latency at p95 was 143ms, exceeding the 100ms serving budget, so it was held in staging. Prompt engineering and speculative decoding are planned for Q2 2026. Performance log: Metric Q3 2025 Q4 2025 Target -------- --------- --------- -------- Recommendation CTR (%) 3.31 3.79 4.00 Conversion lift from recs (%) 7.0 8.6 9.0 Coverage (% of catalogue recommended) 36 41 45 Diversity score (ILS, 0–1 scale) 0.61 0.63 0.68 Cold-start performance (CTR, new users) 1.82 2.07 2.50 --- 2.2 User Behaviour Analytics Pipeline Event tracking was migrated from a legacy Kinesis-based pipeline to Apa

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