Yahoo! Inc.

Ruiqiang Zhang

Yi Chang

Zhaohui Zheng

Anlei Dong

Jing Bai

Ciya Liao

Gilbert Leung

Karolina Buchner

Georges-Eric Albert Marie Robert Dupret

Gilad Avraham Mishne

Pranam Kolari

Shihao Ji

Ling Liu

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.

Incorporating recency in network search using machine learning

An information retrieval system is described herein that monitors a microblog data stream that includes microblog posts to discover and index fresh resources for searching by a search engine. The information retrieval system also uses data from the microblog data stream as well as data obtained from a microblog subscription system to compute novel and effective features for ranking fresh resources which would otherwise have impoverished representations. An embodiment of the present invention advantageously enables a search engine to produce a fresher set of resources and to rank such resources for both relevancy and freshness in a more accurate manner.

Ranking of search results based on microblog data

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Cupertino, CA, United States of America

Ruiqiang Zhang