Incorporating recency in network search using machine learning

摘要

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.

主权项

1. A method, implemented on at least one machine having at least one processor, storage, and a communication platform connected to a network for ranking a search result, comprising:
accessing, by the at least one machine, a set of recency ranking data comprising one or more past search queries, one or more past search results, and one or more recency features, wherein the recency features comprise:
a time-sensitive feature representing a particular time period that was determined based on content of at least some of the past search queries, wherein the at least some of the past search queries were sensitive to the particular time period, anda query timestamp feature representing the time at which the past search queries were received at a search engine; training, by the at least one machine, a first ranking model via machine learning based on the recency features; and determining when recency is to be utilized for ranking a search result based on the ranking model.