User Profiling
Definition
User Profiling is the task of inferring a personal preference profile (taste, context, latent patterns) from a customer’s behavioral history and representing it as a vector. It is the shared input layer for targeting, segmentation, and recommendation — the industry-side representation corresponding to patient covariate/multimodal representations (Multimodal Clinical Data) in the clinical domain.
Intuitive Understanding
It reverse-infers “what this person consumes, why, and through which hidden patterns” from behavioral traces. The key is behavior-based latent preference, not mere demographics.
Multi-Layer Profile
The 3-Layer design from LLM Factor Rec:
| Layer | Question | Example (Music) |
|---|---|---|
| L1 Product | What is consumed? | Genre, BPM, price range (objective) |
| L2 Perceived | What mood, what context? | Mood, energy, listening context |
| L3 Theoretical | Unconscious patterns? | Tonality, chord progression, rhythmic complexity |
It mirrors item attributes to construct a user preference vector, and gains efficiency through incremental updates (summary of the existing profile + new behavior).
Role in Targeting
- The atomic input of Customer Segmentation (profile → clusters).
- An Affinity Matrix (user preference × product attribute similarity) yields latent affinity → combinations that are “high in latent affinity but low in realization” are targeting opportunities.
- Part of the uplift covariate .
Related Concepts
- Targeting Overview ← hub
- Customer Segmentation — cluster summary of profiles
- Multimodal Clinical Data — clinical-side counterpart representation (personalization duality)
References
- MOC-Targeting