Onboarding Survey

Avoid overwhelming new users with a lengthy questionnaire. Progressive profiling breaks the survey into micro-interactions triggered at contextually relevant moments. This reduces abandonment rates and captures higher-quality data.

• Initial gate: Ask only 2-3 critical questions (e.g., primary goal, category interest).
• Just-in-time prompts: Elicit deeper preferences when the user engages with a specific feature.
• Example: A streaming service asks for favorite genres on signup, then prompts for actor preferences after the first movie is watched.

Combine direct survey questions with passively observed implicit feedback. The survey provides a high-confidence initial vector, while clicks and dwell time refine it immediately.

• Explicit signals: Stated genre preferences, price ranges, or brand affinities.
• Implicit signals: Scroll depth on onboarding recommendations, time spent on a category card.
• Mechanism: Use the survey to initialize a content-based filtering profile, then let a contextual bandit algorithm adjust weights based on real-time behavior.

Map survey responses directly into a dense user embedding space. Instead of storing raw answers as categorical features, use a model like Sentence-BERT to encode textual preferences into a vector for immediate cosine similarity matching against item embeddings.

• Process: User selects 'Modern Minimalist Furniture' -> text is encoded to a 768-dim vector.
• Result: Instant approximate nearest neighbor (ANN) retrieval of visually and stylistically similar products.
• Advantage: Handles open-ended taste questions without rigid taxonomies.

Do not ask every user the same questions. Use active learning to select the most informative question at each step, maximizing the reduction in model uncertainty about the user's preference vector.

• Method: A Thompson Sampling or entropy-reduction model evaluates a pool of candidate questions.
• Outcome: The system asks a user who indicated 'Sports' interest whether they prefer 'Endurance' or 'Team' sports, rather than a generic question.
• Efficiency: Achieves a high-confidence profile with 60-70% fewer questions than a static survey.

Use initial survey responses to perform lookalike modeling. Identify a cohort of existing high-value users who gave identical initial answers and bootstrap the new user's recommendations using the cohort's proven personalization strategy.

• Technique: Cluster new users into micro-segments based on survey vectors.
• Application: A new user who selects 'Vegan, Quick Meals, Budget-Friendly' inherits the recipe ranking model of the top 1% of similar existing users.
• Benefit: Bridges the gap from zero behavioral data to a warm start instantly.

Design questions to explicitly capture side information that links to a knowledge graph. Asking for a user's location, device type, or professional role provides immediate contextual constraints for recommendations.

• Graph linking: 'San Francisco' connects to local inventory nodes and regional pricing rules.
• Constraint-based filtering: A 'Software Engineer' role may trigger recommendations for technical laptops, overriding general popularity trends.
• Zero-shot capability: Enables a cross-domain recommendation signal before any in-domain behavior occurs.