User Behaviour Analysis & Recommender Systems Quarterly Report — Q2 2026

Q2 2026 was the quarter in which the LLM-as-re-ranker pattern crossed from early production into mainstream adoption among top-tier platforms, while simultaneously the EU Digital Services Act Article 27 algorithm transparency… 1. Executive Summary Q2 2026 was the quarter in which the LLM-as-re-ranker pattern crossed from early production into mainstream adoption among top-tier platforms, while simultaneously the EU Digital Services Act Article 27 algorithm transparency requirements came into force for Very Large Online Platforms, injecting a new compliance dimension into recommendation system architecture decisions. The Adobe Summit (April 19–22) positioned AI-generated personalised creative — powered by Adobe Firefly — as the next convergence point between recommendation systems and generative content, with several enterprise customers reporting early multimodal personalisation pilots. Spotify's publication on podcast recommendation with multimodal LLM signals demonstrated that audio-domain recommendation is catching up with the text-image fusion work dominant in retail. Amplitude's launch of Amplitude AI — enabling natural language queries over behavioural analytics — lowered the barrier for non-technical product teams to engage with recommendation data, a shift that is accelerating product-led iteration cycles. KDD 2026 (June/July boundary) produced a cohort of papers on causal graph-based debiasing and agentic recommendation patterns that will define the Q3 research agenda. The competitive and regulatory landscape both increased in complexity this quarter, and the gap between leading-edge LLM-powered platforms and median-capability deployments continued to widen. --- 2. Technical Updates — Previous Quarter (Q1 2026) 2.1 Recommendation Engine Updates Q1 2026 delivered the SASRec-v3.1 update (a minor patch improving training stability on sparse interaction histories) and the first production shadow test of the LLM re-ranker on live traffic. The shadow experiment — running a 3B parameter quantised model with speculative decoding against 10% of logged-in user traffic on the primary homepage surface — produced the following results over a six-week run: - LLM re-ranker vs. SASRec-v3.1 (shadow, no user impact): +6.1% CTR on shadow-ranked list vs. production-ranked list - p95 latency of LLM re-ranker: 83ms (within the <90ms target set at the end of Q4 2025) - GPU cost per 1,000 re-rank requests: $0.017 (within budget at projected traffic volumes) Based on these results, the GPU budget was approved and the LLM re-ranker was greenlit for a 20% holdout A/B test beginning in Q2. The cold-start content-based fallback model was rebuilt with stratified training data sampling and redeployed as v1.3, recovering the CTR regression observed in February 2026. Performance log: Metric Q4 2025 Q1 2026 Target (Q2) -------- --------- --------- ------------ Recommendation CTR (%) 3.79 3.91 4.10 Conversion lift from recs (%) 8.6 9.1 9.5 Coverage (% of catalogue recomme

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