Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include retrieving an initial ranking data object associated with a plurality of search result data objects, retrieving a plurality of relevance score data objects, generating a plurality of ranking comparison score data objects, generating a multi-measure optimized ranking data object associated with the plurality of search result data objects, and performing one or more prediction-based actions based at least in part on the multi-measure optimized ranking data object.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing personalized autocomplete predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform personalized autocomplete predictions using a general search corpus and/or individual curated search corpus.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.

Abstract: A method for user identification of a desired document is provided. The method includes receiving an identification of a prototype document, providing a database identifying a catalog of documents, identifying as candidate documents all documents within the catalog of documents which are within a threshold T1 relative to the prototype document, the threshold T1 being a member of the group consisting of (i) a distance representing dissimilarity and (ii) a score determined in dependence on a view of user preferences and dissimilarity, identifying a collection of fewer than all of the candidate documents, receiving, from the user, a selection of one or more documents from the collection identified toward the user, reducing the threshold T1 by a predetermined amount, and removing, from the candidate documents, all documents within the catalog of documents having a distance greater than the reduced threshold T1 from the selected one or more documents.