Bloomreach Inc.

Antariksh Vinod Bothale

Ashutosh Garg

Ramkumar Rajendran

Apurva Kumar Gupta

Warren Mar

Ailian Gan

Wally Chien Ching Ye

Padmini Jaikumar

Sneha Venkatesh Yelimeli

Omar Eduardo Fernandez

Mohammad Salim Ahmed

A system for generating structured queries and carrying out searches using the structured queries may result in high quality queries and may adequately simulate natural language for suggested search queries. In embodiments, attributes and matching product/services may be extracted from various data sources. Structured queries that include the attributes, products/services, and/or additional terms may be combined to form a structured query. In various embodiments, each structured query may be structured according to a pre-defined pattern that is selected based on the type of relevant product and/or service.

Structured search queries

A system for extracting attributes can analyze text from data sources, extract n-grams from the text as candidate attribute and service/product pairs, prompt a human operator to rate the suitability of the candidate attribute and service/product pairs, and, based on the ratings, add the candidate attribute and service/product pairs to an attribute dictionary. In embodiments, an attribute extraction system includes an n-gram extractor, an attribute selector interface, and a dictionary builder. Data sources may include product titles, category descriptions, product descriptions, and like data from one or more product databases. In embodiments, the attribute dictionary is analyzed to determine canonical names for products or services and name variants for the products or services.

Attribute extraction

A system for synonym generation and/or identification can be utilized to make a search engine more effective in finding relevant search results. In embodiments, a synonym generation system includes a phrase vector module, a vector similarity module, and a vector filter module. Candidate synonym phrase pairs are selected from data sources for analysis. Data sources may include a log of search queries, a corpus of web text, and a set of merchant descriptions of products. The data sources may be analyzed with respect to the phrase pairs to generate vector representations of the phrase pairs. The vector representations may then be analyzed to determine a similarity vector. The similarity vector allows the synonym generation system to filter synonyms from the candidate phrase pairs.

Synonym generation

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Mountain View, CA, United States of America

Antariksh Vinod Bothale