Abstract: A system and method for information navigation and filtration is provided. One or more query terms are received from a user. A preliminary relevance of one or more objects associated with an enterprise system is determined based on the query terms. The preliminary relevance may be propagated between objects. At least one rating is assigned to the one or more objects based on the preliminary relevance. An overall relevance of the one or more objects is established based on the at least one rating. The one or more objects are ranked according to the overall relevance. Data is provided as search results comprised of the one or more objects according to the ranking to the user. The search results may then be filtered based on at least one selected, dynamically generated filter. The filtered search results may be dynamically generated and provided to the user.

Abstract: Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

Abstract: A system, method, and computer program for establishing relevance of objects in an enterprise system is provided. One or more objects are assigned to content associated with an enterprise system. One or more query terms are received from a user. A preliminary relevance of the one or more objects is determined based on the query terms. Ratings are assigned to the one or more objects based on the preliminary relevance. An overall relevance of the one or more objects is established based on the ratings.

Abstract: A system and method for information navigation and filtration is provided. One or more query terms are received from a user. A preliminary relevance of one or more objects associated with an enterprise system is determined based on the query terms. The preliminary relevance may be propagated between objects. At least one rating is assigned to the one or more objects based on the preliminary relevance. An overall relevance of the one or more objects is established based on the at least one rating. The one or more objects are ranked according to the overall relevance. Data is provided as search results comprised of the one or more objects according to the ranking to the user. The search results may then be filtered based on at least one selected, dynamically generated filter. The filtered search results may be dynamically generated and provided to the user.

Abstract: The system implements a novel method for personalized filtering of information and automated generation of user-specific recommendations. The system uses a statistical latent class model, also known as Probabilistic Latent Semantic Analysis, to integrate data including textual and other content descriptions of items to be searched, user profiles, demographic information, query logs of previous searches, and explicit user ratings of items. The system learns one or more statistical models based on available data. The learning may be reiterated once additional data is available. The statistical model, once learned, is utilized in various ways: to make predictions about item relevance and user preferences on un-rated items, to generate recommendation lists of items, to generate personalized search result lists, to disambiguate a users query, to refine a search, to compute similarities between items or users, and for data mining purposes such as identifying user communities.

Abstract: The disclosed system implements a novel method for personalized filtering of information and automated generation of user-specific recommendations. The system uses a statistical latent class model, also known as Probabilistic Latent Semantic Analysis, to integrate data including textual and other content descriptions of items to be searched, user profiles, demographic information, query logs of previous searches, and explicit user ratings of items. The disclosed system learns one or more statistical models based on available data. The learning may be reiterated once additional data is available. The statistical model, once learned, is utilized in various ways: to make predictions about item relevance and user preferences on un-rated items, to generate recommendation lists of items, to generate personalized search result lists, to disambiguate a users query, to refine a search, to compute similarities between items or users, and for data mining purposes such as identifying user communities.

Abstract: The disclosed system implements a novel method for personalized filtering of information and automated generation of user-specific recommendations. The system uses a statistical latent class model, also known as Probabilistic Latent Semantic Analysis, to integrate data including textual and other content descriptions of items to be searched, user profiles, demographic information, query logs of previous searches, and explicit user ratings of items. The disclosed system learns one or more statistical models based on available data. The learning may be reiterated once additional data is available. The statistical model, once learned, is utilized in various ways: to make predictions about item relevance and user preferences on un-rated items, to generate recommendation lists of items, to generate personalized search result lists, to disambiguate a users query, to refine a search, to compute similarities between items or users, and for data mining purposes such as identifying user communities.