Abstract: An enhanced ranking approach is used to evaluate selected metrics for various services, including search and recommendations for online marketplaces and other search engine-related applications. This includes a ranking system capable of learning neural networks which efficiently tradeoff between different business objectives. For instance, a hybridized ranking system combines the strength of relevancy focused models with the flexibility of ES via ensembling to solve multi-objective ranking problems.

Abstract: A unified system with a machine learning feature data pipeline that can be shared among various product areas or teams of an electronic platform is described. A set of features can be fetched from multiple feature sources. The set of features can be combined with browsing event data to generate combined data. The combined data can be sampled to generate sampled data. The sampled data can be presented in a format having a structure that is agnostic to a feature source from which the set of features was fetched. The sampled data can be joined with old features by a backfilling process to generate training data designed to train one or more machine learning models. Related methods, apparatuses, articles of manufacture, and computer program products are also described.