Microsoft Technology Licensing, LLC

Ebay, Incorporated

Kira Radinsky

Eric J Horvitz

Susan Theresa Dumais

Krysta Marie Svore

Jaime Brooks Teevan

Ido Guy

Roy Varshavsky

Idan Schwartz

David Benson Cross

Vladimir Holostov

Jack W Stokes

Eugene (John) Neystadt

Ron Jack Karidi

Avigad Oron

Edward William Schaefer

Yitzhak Tzahi Weisfeild

Various methods and systems for processing web crawling queries using a web crawling prioritization model based on classification operation performance. A classification operation for organizing products in a product listing platform is accessed. A web crawling engine is accessed for the classification operation. The web crawling engine operates based on a web crawling query prioritization model that supports determining web crawling priority scores that indicate a predicted performance improvement for classification operations executed with known data and web crawled data to be retrieved from executing a web crawling query operation. Using the web crawling prioritization model, a web crawling priority score is determined for a web crawling query for the corresponding classification operation. The classification operation is associated with a product in a product listing platform and known data for the product. Based on the web crawling priority score, the web crawling query is executed to identify web crawled data.

Search system for providing web crawling query prioritization based on classification operation performance

Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Time-aware ranking adapted to a search engine application

A ranked set of users may be calculated from an expertise categorization for each user and a person's trust in the users for specific categories. The ranked set of users may be used for presenting search results, recommendations, social marketing, or other uses. A person's reputation may be determined through various online activities. A person's trust in another person may be related to their proximity and activity in one or more social networks.

Social network based contextual ranking

An active filter monitors a web browser session to identify executable code transmitted in the session. The executable code may be analyzed to determine if the code is digitally signed. When the code is digitally signed by the web server or by another trusted source, the code may be executed. When the code is neither digitally signed or when the source is not trusted, the code may be rejected and not executed. The filter may be implemented as a web browser component or plugin, as well as a gateway device, proxy, or other service. The filter may also be implemented on the server side to reject incoming data that may include unauthenticated code.

Executable code validation in a web browser

Architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity. One or more of the highest ranked engines are activated in order to achieve the desired performance. A statistical, performance-light module is employed to skip or select several performance-demanding processes. Methods and algorithms are utilized for learning based on matching the best classification engine(s) to detect the entity class based on the entity properties. A user selection option is provided for specifying a maximum number of ranked, classification engines to consider for each state of the machine. A user can also select the minimum probability of detection for a specific entity (e.g., unknown file). The best classifications are re-evaluated over time as the classification engines are updated.

Realtime multiple engine selection and combining

Growing community of inventors

Zichron Yaakov, Israel

Kira Radinsky