Abstract: Topic-based language models for an application search engine enable a user to search for an application based on the application's function rather than title. To enable a search based on function, information is gathered and processed, including application names, descriptions and external information. Processing the information includes filtering the information, generating a topic model and supplementing the topic model with additional information.

Abstract: A user can input a search query. By assessing terms in the search query, the search query can be categorized (e.g., as a navigational, functional or browse query). The categorization can depend on an analysis of terms in the search query, a preliminary search, and/or empirical data tied to previous and similar searches. For example, searches estimated to be directed to finding a specific app can be categorized as navigational, based on capitalized letters, quotation marks, a short number of words in the query, and a tight distribution of apps typically downloaded subsequent to viewing results from the search. Based on the categorization, a search strategy can be identified. Search strategies can differentially weight and/or use text-based strategies.

Abstract: A set of potential search-query terms can be identified based on empirical queries for apps. For each potential search-query term, a subset of documents within a set of documents can be identified based on apps that users were likely to click on or download following entry of a search query with a comparable or same term. One or more other indicator terms can be identified as being related to the potential search-query term based on the one or more second indicator terms being prevalent within the subset of documents. Upon receipt of a subsequent search query, a search can then be performed using both a term within the search query and one or more related other indicator terms.

Abstract: A set of potential search-query terms can be identified based on empirical queries for apps. For each potential search-query term, a subset of documents within a set of documents can be identified based on apps that users were likely to click on or download following entry of a search query with a comparable or same term. One or more other indicator terms can be identified as being related to the potential search-query term based on the one or more second indicator terms being prevalent within the subset of documents. Upon receipt of a subsequent search query, a search can then be performed using both a term within the search query and one or more related other indicator terms.

Abstract: A user can input a search query. By assessing terms in the search query, the search query can be categorized (e.g., as a navigational, functional or browse query). The categorization can depend on an analysis of terms in the search query, a preliminary search, and/or empirical data tied to previous and similar searches. For example, searches estimated to be directed to finding a specific app can be categorized as navigational, based on capitalized letters, quotation marks, a short number of words in the query, and a tight distribution of apps typically downloaded subsequent to viewing results from the search. Based on the categorization, a search strategy can be identified. Search strategies can differentially weight and/or use text-based strategies.

Abstract: Topic-based language models for an application search engine enable a user to search for an application based on the application's function rather than title. To enable a search based on function, information is gathered and processed, including application names, descriptions and external information. Processing the information includes filtering the information, generating a topic model and supplementing the topic model with additional information.