What is explicit feedback in recommender systems?

Explicit feedback in recommender systems refers to direct, intentional input from users that explicitly indicates their preferences or opinions about items. This type of feedback is collected through deliberate actions, such as rating a product, liking a post, or filling out a survey. Unlike implicit feedback, which infers preferences from behavior (e.g., clicks, watch time), explicit feedback provides clear, unambiguous signals about user preferences. For example, a user giving a 5-star rating to a movie on a streaming platform is explicitly stating their approval, making it straightforward for the system to incorporate this data into recommendation models.

Explicit feedback is often used in collaborative filtering and hybrid recommendation algorithms. In collaborative filtering, user-item interaction matrices built from ratings or reviews help identify patterns, such as users who rate similar items highly. For instance, if User A and User B both rate a sci-fi movie 5 stars, the system might recommend other sci-fi movies User B liked to User A. Matrix factorization techniques decompose these matrices to uncover latent factors (e.g., genre preferences) that drive recommendations. Content-based systems also use explicit feedback to refine item profiles—for example, if users tag a song as “energetic,” the system associates those tags with the song’s attributes. However, explicit feedback can be sparse, as many users don’t consistently rate or review items, leading to challenges in modeling preferences for inactive users.

A key advantage of explicit feedback is its reliability, as it directly reflects user intent. Platforms like Netflix or Goodreads rely heavily on star ratings to personalize recommendations. However, its limitations include potential bias (users might rate extremes like 1 or 5 stars more often) and the “cold start” problem for new users who haven’t provided enough data. To address this, many systems combine explicit feedback with implicit signals (e.g., viewing duration) for a more complete picture. For example, a streaming service might prioritize a user’s 5-star-rated genres but also consider which titles they binge-watch. Developers should weigh the trade-offs: explicit feedback is valuable for accuracy but requires mechanisms to encourage user participation, like simplifying rating interfaces or offering incentives.