CONCEPT Cited by 1 source
Verbalization for recommendation¶
Definition¶
Verbalization for recommendation is the practice of representing user interaction histories, item metadata, and request context as natural-language text — rather than as dense feature vectors or sparse ID embeddings — so they can be processed by a large language model in its native semantic space.
Why It Matters¶
Traditional recommender systems operate on: - Hand-crafted dense features (numeric embeddings of user/item interactions) - Sparse categorical IDs (user_id, item_id, device_type) - Pre-computed interaction features (co-watch counts, session lengths)
Adding a new content type or surface requires significant feature engineering, architecture changes, and infrastructure work. Verbalization sidesteps this — a new content type is just a new text description the LLM can already parse.
The LLM's world knowledge and language understanding discover higher-level patterns (item relationships, evolving interests) that feature engineers would need to manually encode.
Trade-offs¶
| Property | Verbalization | Dense features |
|---|---|---|
| Onboarding new content types | Low cost (just describe in text) | High cost (new feature pipelines) |
| Signal density per token | Lower (natural language is verbose) | Higher (compressed numeric) |
| Model capability requirement | Needs strong LLM backbone | Works with simple MLPs/DLRMs |
| Serving cost | Higher (long token sequences) | Lower (fixed-size vectors) |
| Feature discovery | Implicit (model finds patterns) | Explicit (engineer designs features) |
Context Engineering as the New Discipline¶
When the prompt is the feature vector, context engineering becomes the primary modeling lever: - Retain high-signal engagements in full - Omit low-signal events (very short plays, hovers) - Summarize repetitive behaviors (binge-watching) - Elaborate selectively on important items (new releases, cold-start) - Structure for shared-prefix caching
The token budget is the new feature budget (Source: sources/2026-07-30-netflix-genrec-towards-llm-native-recommendation).
Seen in¶
- sources/2026-07-30-netflix-genrec-towards-llm-native-recommendation — GenRec "verbalizes rich user histories and context as natural language, encoding raw interaction signals directly in the LLM's semantic space"