Abstract: Some aspects relate to technologies for intent-centric search query reformulation. In accordance with some aspects, a generative model is trained for query reformulation. User behavior data is accessed that includes session data for a number of user sessions, including a sequence of search queries for each user session. Query pairs are identified from the user behavior data, including in-session query pairs from in-session data and cross-session queries from cross-session data. The query pairs are labeled with search intent labels based on predefined search intents. A training dataset is generated in which each training sample comprises a labeled query pair. A generative model is then trained using the training dataset to provide a trained generative model that generates reformulated queries from input queries and search intents for the input queries.

Abstract: Methods, systems, and computer media are disclosed for generating enhanced query suggestions by integrating collaborative filtering, embedding-based similarity, and large language model generation techniques. The system accesses a query keyword and produces three types of suggestions: one derived from historical user behavior, one based on semantic similarity using embeddings, and one generated by an LLM. These suggestions are stored in association with the query keyword and may be retrieved for future queries. For new or previously unseen queries, the system generates a semantic hash and identifies the most similar stored keyword using hash comparison or embedding similarity. The technology improves upon traditional collaborative filtering by enabling relevant and diverse suggestions even for low-frequency or non-head queries.

Abstract: A search engine leverages a neural translation model to provide diverse and multiple query reformulations. Two decoders are injected and a diversity inducing optimization function is introduced. After a query input is received from a user, a number of items are retrieved from a database in response to the query input. In response to a determination the query input is a null and low query based on a number of responsive items, a plurality of decoders is injected and a diversity inducing optimization function is leveraged to generate a plurality of diverse reformulated queries. A plurality of query results corresponding to the plurality of diverse reformulated queries is provided as output.