cs.LGOct 11, 2026
SaveDRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage
Organizations: The Walt Disney Company United States
Abstract
We present DRanker (Disney+ Ranker), a transformer-based multitask ranking model serving personalized recommendations on the Disney+ homepage. We describe the encoder-only architecture over enriched engagement-history sequences paired with a lightweight causal debiasing mechanism, and the inference stack that enables transformer-based ranking at scale. Through iterative development, we systematically evaluated more complex alternatives and found that simpler designs consistently outperformed them on offline metrics, qualitative behavioral evaluation, and in production. Deployed globally, DRanker drives over 1% lift in our key engagement metric and 0.5% lift in daily active users across all experiences powered by the ranker. We share practical lessons on data engineering, debiasing, and architectural choices that shaped our ranking system at scale.
Figures & tables
Figure 1. Training data construction funnel. Each stage progressively filters and refines the raw viewing signal into high-quality training samples. Four-stage funnel diagram. Top layer: All Engagement. Second: Qualified by Engagement Length. Third: First-Time Interactions. Bottom: Stratified Sampling. Each stage is narrower than the previous, with annotations on the right explaining the filtering rationale.
Figure 2. DRanker model architecture. The transformer encoder processes enriched engagement-history sequences. The shared MLP backbone receives embeddings from the sequential encoder, candidate item, contextual features, and causal tower. Two task-specific heads predict engagement probability and expected engagement length. The causal tower’s pop/rel output heads are discarded at inference; only its learned embeddings are retained. Model architecture diagram. Bottom: input features per sequence title (content embedding, contextual features, engagement features) feed into a transformer encoder with self-attention and mean pooling. Middle: candidate embedding, sequence embedding, session/contextual features, and causal tower embeddings feed into a shared ReLU MLP backbone. Top: two task-specific heads output engagement probability and engagement length. A separate causal tower on the left produces popularity and relevance embeddings with gradient isolation.
| Experiment | Attention | Causal | MTL | Result |
|---|---|---|---|---|
| DRanker (final) | Self-attn | Yes | YT | Best overall |
| Base (no causal) | Self-attn | No | YT | Lower entity coverage |
| Weighted loss | Self-attn | No | YT | Unstable training loss |
| Causal + weighted | Self-attn | Yes | YT | No gain over causal-only |
| Cross-attention | Cross-attn | Yes | YT | Contextual features dominated |
| PLE | Self-attn | Yes | PLE | Gating saturation |
Table 1. Summary of architectural experiments. All experiments use LLM-derived + ID-based content embeddings, a fixed positive-to-negative ratio, and a short-term sequence length. “YT” denotes YouTube-style stacked ReLU heads. Best configuration in bold.
| Variant | AP@10 | Top-50 Overlap |
|---|---|---|
| Self-Attn + No Causal + YT | 0.035 | 10% |
| Self-Attn + Causal + YT | 0.028 | 5% |
| Self-Attn + Causal + PLE | 0.018 | 1% |
| Cross-Attn + Causal + YT | 0.015 | 4% |
Table 2. Offline evaluation — accuracy vs. popularity concentration. AP@10 and Top-50 Overlap (fraction of a user’s top-100 recommendations appearing in the platform’s daily top-50 titles). Evaluated on 100k daily interactions (US, 10-day holdout). Missing interactions are imputed as not relevant. Higher AP@10 is better; lower Top-50 Overlap indicates less popularity bias. The same variant ranking held for NDCG@k and Recall@k across all cutoffs.
| Base | Base | Tower | Tower | |
|---|---|---|---|---|
| Metric | (prob) | (combined) | (prob) | (combined) |
| Engagement/User | +0.43 | +0.41 | +0.35 | +0.48 † |
| Title Diversity | +1.02 † | +0.50 | +0.84 | +0.32 |
| DAU | +0.29 † | +0.13 | +0.20 | +0.24 |
| % Sessions w/ Search | 35.1 | 49.3 | 41.8 | 61.6 † |
Table 3. Online A/B results — relative lift (%) over control. Bold = statistically significant ( ). Best per metric highlighted with .
Figure 3. Multi-user profile test. Top: synthetic shared engagement history with two distinct patterns (children’s content during daytime on mobile, general entertainment during evening on TV). Bottom: the model correctly surfaces children’s content for daytime/mobile sessions and general entertainment for evening/TV sessions. Two-panel figure. Top panel: a synthetic shared engagement history with two distinct patterns, children's content engaged during daytime on mobile and general entertainment content engaged during evening on TV. Bottom panel: model recommendations split by session context, surfacing children's content for daytime/mobile sessions and general entertainment for evening/TV sessions.
Figure 4. Relative model scores by local hour of day. Bluey (children’s) peaks during daytime; Andor (mature) peaks in the evening, matching real engagement patterns. Line chart with two curves plotted against local hour of day. Bluey (children's title) scores peak during midday hours. Andor (mature title) scores peak during evening hours.
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