The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Nov. 11, 2014
Filed:
Oct. 15, 2009
Anlei Dong, Fremont, CA (US);
Yi Chang, Santa Clara, CA (US);
Ruiqiang Zhang, Cupertino, CA (US);
Zhaohui Zheng, Mountain View, CA (US);
Gilad Avraham Mishne, Oakland, CA (US);
Jing Bai, San Jose, CA (US);
Karolina Barbara Buchner, San Jose, CA (US);
Ciya Liao, Fremont, CA (US);
Shihao Ji, Santa Clara, CA (US);
Gilbert Leung, Mountain View, CA (US);
Georges-eric Albert Marie Robert Dupret, Mountain View, CA (US);
Ling Liu, Mountain View, CA (US);
Anlei Dong, Fremont, CA (US);
Yi Chang, Santa Clara, CA (US);
Ruiqiang Zhang, Cupertino, CA (US);
Zhaohui Zheng, Mountain View, CA (US);
Gilad Avraham Mishne, Oakland, CA (US);
Jing Bai, San Jose, CA (US);
Karolina Barbara Buchner, San Jose, CA (US);
Ciya Liao, Fremont, CA (US);
Shihao Ji, Santa Clara, CA (US);
Gilbert Leung, Mountain View, CA (US);
Georges-Eric Albert Marie Robert Dupret, Mountain View, CA (US);
Ling Liu, Mountain View, CA (US);
Yahoo! Inc., Sunnyvale, CA (US);
Abstract
In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.