Abstract: Techniques are disclosed for dynamically generating a data set representative of search results in response to a query and using the data set to accurately rank search results in response to a domain specific search query. Upon receiving the search query, features of the query and features of each search result are extracted. A relevance ranking may be assigned to each search result based on a comparison of the features of the query and each search result. The relevance ranking of each search result may be adjusted based on metrics related to user interactions. A data set may be created which includes the query, search results, extracted features, and metrics. The data set may be used to train a machine learning model to accurately determine a ranking of search results in response to a subsequent search query.

Abstract: Techniques are disclosed for dynamically generating a data set representative of search results in response to a query and using the data set to accurately rank search results in response to a domain specific search query. Upon receiving the search query, features of the query and features of each search result are extracted. A relevance ranking may be assigned to each search result based on a comparison of the features of the query and each search result. The relevance ranking of each search result may be adjusted based on metrics related to user interactions. A data set may be created which includes the query, search results, extracted features, and metrics. The data set may be used to train a machine learning model to accurately determine a ranking of search results in response to a subsequent search query.

Abstract: Techniques are disclosed for dynamically generating a data set representative of search results in response to a query and using the data set to accurately rank search results in response to a domain specific search query. Upon receiving the search query, features of the query and features of each search result are extracted. A relevance ranking may be assigned to each search result based on a comparison of the features of the query and each search result. The relevance ranking of each search result may be adjusted based on metrics related to user interactions. A data set may be created which includes the query, search results, extracted features, and metrics. The data set may be used to train a machine learning model to accurately determine a ranking of search results in response to a subsequent search query.

Abstract: Disclosed methods and systems provide domain-specific incremental search results for search query terms with a customer self-service system for a financial management system, according to one embodiment. Incremental search results are search results that are generated for search query terms, as a user enters the search query terms as part of a search query to a customer self-service system, according to one embodiment. The customer self-service system receives search query terms (e.g., a partial search query) in a search text box, searches customer self-service system content for content that is relevant to the search query terms, and provides incremental search results in a results text box (e.g., drop down menu) that is proximate to (e.g., below) the search text box, according to one embodiment.

Abstract: Disclosed methods and systems provide domain-specific incremental search results for search query terms with a customer self-service system for a financial management system, according to one embodiment. Incremental search results are search results that are generated for search query terms, as a user enters the search query terms as part of a search query to a customer self-service system, according to one embodiment. The customer self-service system receives search query terms (e.g., a partial search query) in a search text box, searches customer self-service system content for content that is relevant to the search query terms, and provides incremental search results in a results text box (e.g., drop down menu) that is proximate to (e.g., below) the search text box, according to one embodiment.