Salesforce Com, Inc.

Guillaume Kempf

Naren M Chittar

Marc Brette

Anuprit Kale

Francisco Borges

Jayesh Govindarajan

Edgar Gerardo Velasco

Arvind Srikantan

Christian Posse

Rohit Kapoor

Yasaman Mohsenin

Pranshu Sharma

Jacob Alexander Mannix

In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.

Classifying different query types

Methods, systems, and devices supporting determining user and data record relationships based on vector space embeddings are described. Some database systems may receive data record access indications corresponding to data records accessed by users. A database system may generate, based on the data record access indications, user sessions for the users, data record sessions for the data records, or a combination for users and data records. For example, a user session may correspond to a respective user and include a record identifier associated with each data record accessed by the user. The system may generate, in a vector space, vectors from the sessions using an embedding operation, where each vector corresponds to a respective user or data record. The system may determine relationships between the users, data records, or both based on the vectors and may transmit an indication of at least one data record based on the relationships.

Determining user and data record relationships based on vector space embeddings

As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.

Predicting a type of a record searched for by a user

Predicting entities for database query results are described. A system receives a query that includes a query term. The system outputs a query result that identifies at least one record that includes the query term. The system identifies a selection of a record that is identified by the query result and that includes the query term. The system stores information that associates the query term with an entity that corresponds to the selected record. The system scales the information that associates the query term with the entity. The system receives another query that includes the query term. The system outputs another query result in response to the other query, the other query result being based on the scaled information that associates the query term with the entity.

Predicting entities for database query results

An online system stores objects that may be accessed by users. The online system also stores indexes of terms related to different entity types of objects. When a user provides a search query, the online system compares the search terms with terms stored in the indexes. Based on the comparisons, the online system determines term features for entity types associated with an index. The online system provides the term features as inputs to a machine learning model. The machine learning model outputs a score for each entity type indicating a likelihood that the search query is for an object associated with the entity type. The machine learning model output is used by the online system to select one or more entity types that the user is likely searching for. The online system offers objects of the likely entity types to the user as results of the search query.

Predicting user intent based on entity-type search indexes

Methods, systems, and devices for user clustering on a cloud platform are described. A user associated with a client may implement a search function to query objects in a database system, and may select an entity (i.e., the clicked entity) from the search results. Each client may utilize the cloud platform in a specific manner, where users associated with the client may frequently search for certain types of objects. In some cases, one or more clients may share similar search histories or clicked entities. A clustering server may group clients with similar search histories or click distributions into common clusters. For future searches, the clustering server may utilize a machine learning model to predict the type of object being searched for based on the clustering. For example, user devices associated with a particular cluster may display similar groups and orders of object types in response to similar queries.

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Grenoble, France

Guillaume Kempf