Meta has shifted its ads recommendation system to leverage sequence learning, improving the personalization and relevance of ads shown to users. Instead of relying on human-engineered features, the new approach uses event-based learning, which captures richer, sequential information from user interactions. This transformation results in improved prediction accuracy, infrastructure efficiency, and increased conversions for advertisers.
Table of contents
The limits of DLRMs for ads recommendationsA paradigm shift with learning from sequences for recommendation systemsScaling the new sequence learning paradigmThe impact and future of sequence learningAcknowledgementsSort: