Alibaba Group Holding Limited

Peng Huang

Feng Lin

Wei Zheng

Qin Zhang

Xu Yang

Huaxing Jin

Zhanhui Kang

Jiong Feng

Tao Wang

Linfeng Zhang

Xipu Wang

Wenbin Zheng

A method and an apparatus of matching an object to be displayed are disclosed. The method includes obtaining a plurality of search keywords and released product information and grouping each of the plurality of search keywords with the released product information to form a plurality of search keyword and released product information pairs, with each search keyword and released product information pair comprising a respective search keyword and the released product information; determining and matching a plurality of features for the plurality of search keyword and released product information pairs according to a constructed first decision tree; and determining respective correlation classes of the plurality of search keyword and released product information pairs based at least in part on a result of determining and matching of the plurality of features. The disclosed method and apparatus are able to accurately and conveniently determine a matching degree between a search keyword and released product information.

Method and apparatus of matching an object to be displayed

Acquiring dynamic data is disclosed including extracting a search term from a search request string that is received, looking up the search term in a threshold value dictionary to acquire a dynamic threshold score corresponding to the search term, using the search term as a query condition and the dynamic threshold score corresponding to the search term as a filter condition to acquire, in an index data table, one or more corresponding pieces of index information, acquiring data information corresponding to the search term based on the index information in the index data table, and sending the data information to be displayed in a page of a website. The dynamic threshold score varies based on a characteristic factor.

Dynamic data acquisition method and system

Searching information includes: receiving current query data from a client; extracting generic attribute features of the current query data, wherein the generic attribute features are used for calculating a plurality of confidence degrees of the current query data that correspond to a plurality of categories, each of the confidence degrees indicating a degree of confidence that the current query data belongs to a respective one of the plurality of categories; determining the plurality of confidence degrees of the current query data based at least in part on the generic attribute features; selecting a category based at least in part on the plurality of confidence degrees, the selected category being one of the plurality of categories and having a confidence degree higher than a confidence degree of another category; searching in the selected category for a search result that corresponds to the current query data; and returning the search result.

Searching for information based on generic attributes of the query

Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Method and apparatus of ordering search results

Searching information includes: receiving current query data from a client; extracting generic attribute features of the current query data, wherein the generic attribute features are used for calculating a plurality of confidence degrees of the current query data that correspond to a plurality of categories, each of the confidence degrees indicating a degree of confidence that the current query data belongs to a respective one of the plurality of categories; determining the plurality of confidence degrees of the current query data based at least in part on the generic attribute features; searching in a chosen category for a search result that corresponds to the current query data, the chosen category being one of the plurality of categories and being chosen based at least in part on the plurality of confidence degrees; and returning the search result.

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Hangzhou, China

Peng Huang