Ratingbased

Ratingbased refers to systems, methods, or approaches that rely primarily on user ratings to generate outputs such as rankings, recommendations, or scores. In ratingbased models, the central signal is the collective judgment expressed through ratings, rather than just content attributes or explicit objective metrics. The term is used across domains including e-commerce, streaming platforms, search, and user-generated content curation.

Data collection and processing involve gathering ratings from users, handling missing values, and applying techniques to normalize and aggregate ratings. Common practices include weighting recent ratings more heavily, adjusting for user bias, and combining ratings with decayed influence over time to reflect current preferences. The resulting rating signal can feed ranking algorithms, recommendation engines, or quality assessments.

Ratingbased implementations may use simple aggregation, such as average or median ratings, as well as probabilistic or machine learning models that estimate true quality from noisy ratings. Hybrid approaches may integrate ratings with additional signals such as interactions, demographics, or item metadata to improve robustness and personalization.

Applications span product search and recommendation, content ranking on video and music platforms, and moderation or trust scoring where higher-rated items are preferred or more visible. The approach emphasizes user feedback as a direct measure of value.

Limitations include susceptibility to popularity and rating biases, manipulation through rating gaming, cold-start problems for new items, and demographic or cultural biases in ratings. Systems often require safeguards such as anomaly detection, fairness considerations, and continuous calibration.

See also collaborative filtering, rating systems, and recommender systems.