Yahoo! Inc.

Ya Zhang

Belle L Tseng

Srinivas Vadrevu

Xin Li

Bo Long

Jean-Marc Langlois

Zhaohui Zheng

Hongyuan Zha

Gordon Sun

David Mark Ciemiewicz

Sudarshan Lamkhede

Keke Chen

Gordon Guo-Zheng Sun

Various users' navigational behaviors relative to search results presented by a search engine are monitored. URLs that are visited and revised queries that are submitted after the submission of an original query are placed within a trail that begins with the original query. These trails are grouped based on the original queries with which they begin. For each trail group, a set of URLs that frequently occur in that group's trails, and a set of revised queries that frequently occur in that group's trails, are determined. These frequently occurring elements are mapped to the original queries with which all the trails in the corresponding trail group begin. In response to subsequent submissions of the same original query, the search engine ensures that URLs and revised queries that are mapped to the original query are prominently displayed on the search results pages that are initially returned in response to those submissions.

Automated user behavior feedback system for whole page search success optimization

According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.

System and method for cross domain learning for data augmentation

A system for determining the intent of query that includes a search engine that receives a first search query, a query/click log module configured to store log data associated with the first search query; and a computational module that generates metric values associated with the first search query based on the log data and that determines that the first search query is one of a regional specific query or a global query based on the metric values, where the metric values reflect a likelihood of local intent of the first search query, and where the search engine provides search results selected in part based on the metric values.

Identifying regional sensitive queries in web search

The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.

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Sunnyvale, CA, United States of America

Ya Zhang