Ranking item objects

A computer implemented method, comprising:

• receiving a query including a plurality of tokens;
• selecting a plurality of item objects based on the query, wherein each item object of the plurality of item objects has a corresponding title;
• obtaining feature data of the plurality of item objects;
• ranking the plurality of item objects to generate a ranked list of item objects based at least by:
• determining a plurality of text similarity metrics,
• generating, for each item object having the corresponding title, similarity scores based on the plurality of text similarity metrics, respectively, wherein each of the similarity scores indicates a text similarity between the corresponding title and the query based on a respective one of the plurality of text similarity metrics, and
• executing a machine learning model to the similarity scores of the plurality of item objects based on the feature data of the plurality of item objects to generate the ranked list of item objects;
• and
• providing the ranked list of item objects in response to the query.

A non-transitory computer-readable storage medium comprising executable instructions that, when executed by one or more processors of a computing device, cause the one or more processors to:

• receive a query including a plurality of tokens;
• select a plurality of item objects based on the query, wherein each item object of the plurality of item objects has a corresponding title;
• obtain feature data of the plurality of item objects;
• rank the plurality of item objects to generate a ranked list of item objects based at least by:
• determining a plurality of text similarity metrics,
• generating, for each item object having the corresponding title, similarity scores based on the plurality of text similarity metrics, respectively, wherein each of the similarity scores indicates a text similarity between the corresponding title and the query based on a respective one of the plurality of text similarity metrics, and
• executing a machine learning model to the similarity scores of the plurality of item objects based on the feature data of the plurality of item objects to generate the ranked list of item objects;
• and
• provide the ranked list of item objects in response to the query.