Implicit data collection

Implicit data are used to construct a user model from interaction traces such as clicks, purchases, or dwell time. These signals enable information filtering and personalization in recommender systems and search.^[2]

In recommender systems, implicit feedback is often modeled through techniques such as matrix factorization and pairwise ranking, which treat user interactions as positive-only or preference signals.^[3][4]

Implicit signals include behavioral and contextual data, such as:

• interaction logs (clicks, views, purchases)
• dwell time and browsing patterns
• contextual and device information
• multimodal signals (e.g., gaze, voice, or facial expression)

These signals are typically noisy and require modeling assumptions to distinguish preference from exposure.^[5]