CATEGORY RECOMMENDATION USING STATISTICAL LANGUAGE MODELING AND A GRADIENT BOOSTING MACHINE

摘要

In accordance with an example embodiment, an input text string is received. Then a k nearest neighbor (KNN) algorithm is used on the input text string to identify a set of leaf categories of an item listing schema that corresponds to the input text string. The set of leaf categories is reordered based on a statistical language model (SLM) algorithm performed on the input text string and an SLM for each leaf category in the set of leaf categories from the KNN recommendation service. A gradient boosting machine (GBM) is then used to fuse the reordered set of leaf categories, a log prior probability for each of the leaf categories, and scores for the KNN algorithm for each of the leaf categories to calculate an ordered list of recommended leaf categories with corresponding scores.

主权项

1. A system comprising:
a k nearest neighbor (KNN) recommendation service executable by one or more processors and configured to perform a KNN algorithm on an input text string to identify a set of leaf categories of an item listing schema that corresponds to the input text string; a statistical language model (SLM) re-ranking module configured to reorder the set of leaf categories from the KNN recommendation service based on an SLM algorithm performed on the input text string and an SLM for each leaf category in the set of leaf categories from the KNN recommendation service; and a gradient boosting machine (GBM) configured to fuse the reordered set of leaf categories, a log prior probability for each of the leaf categories, and scores for the KNN algorithm for each of the leaf categories to calculate an ordered list of recommended leaf categories with corresponding scores.