SEARCHING FOR INFORMATION BASED ON GENERIC ATTRIBUTES OF THE QUERY

• US 20160085871A1
• Filed: 12/03/2015
• Published: 03/24/2016
• Est. Priority Date: 09/27/2009
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Lin, Feng, Kang, Zhanhui, Huang, Peng
• Granted Patent: US 9,846,748 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: H04L 67/01 Protocols

Abstract

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.

Claims

1. A method for searching information, comprising:

receiving current query data from a client;

computing, using one or more processors, a plurality of confidence degrees associated with the current query data, wherein the plurality of confidence degrees respectively indicate a degree of confidence that the current query data is associated with a corresponding one of a plurality of categories;

selecting, using one or more computer processors, a category based at least in part on the plurality of confidence degrees, the selected category being one of the plurality of categories and to having a confidence degree higher than a confidence degree of another category;

in response to selecting the category based at least in part on the plurality of confidence degrees, searching in the selected category for a search result that corresponds to the current query data; and

returning the search result to the client.

2. The method of claim 1, further comprising:

extracting generic attribute features of the current query data, wherein the generic attribute features are used for computing the plurality of confidence degrees of the current query data.

3. The method of claim 2, wherein the generic attribute features comprise a word-level feature that corresponds to a keyword in the current query data.

4. The method of claim 2, wherein the generic attribute features comprise a syntax feature that corresponds to an etymon corresponding to a headword in the current query data.

5. The method of claim 4, wherein extracting the generic attribute features including the syntax feature comprises:

performing part-of-speech tagging of a keyword in the current query data and identifying a phrase including the keyword in the current query data;

obtaining a noun phrase in the current query data based at least in part on results of the part-of-speech tagging and the identification of the phrase;

extracting the etymon associated with the headword of the noun phrase.

6. The method of claim 2, wherein the generic attribute features comprise a semantic feature that corresponds to a semantic tag corresponding to the keyword.

7. The method of claim 6, wherein extracting the generic attribute features including the semantic feature comprises:

identifying the type of the keyword of the current query data; and

acquiring an action verb tag, a product tag, a brand tag, a model tab, or another tag of the keyword according to the identified type.

8. The method of claim 2, wherein the generic attribute features comprise a click feature that corresponds to a clicked category corresponding to the current query data in a click log.

9. The method of claim 8, wherein extracting the generic attribute features including the click feature comprises:

determining whether the current query data is present in the click log, wherein the click log is adapted to store a correspondence relationship between the query data and the click feature;

in the event that the current query data is present in the click log, acquiring a first predetermined number of clicked categories corresponding to the current query data from the preset click log, wherein the clicked categories are categories to which a corresponding product clicked by a user after searching for the query data belongs; and

in the event that the current query data is not present in the click log, inquiring in the click log about clicked query data closest to the current query data, wherein the clicked query data is at the shortest edit distance in the click log from the current query data, and acquiring the first predetermined number of categories corresponding to the clicked query data as the click feature.

10. The method of claim 2, wherein the generic attribute features comprise a selection feature that corresponds to at least a selected category corresponding to the current query data in a selection log.

11. The method of claim 10, wherein extracting the generic attribute features including the selection feature comprises:

determining whether the current query data is present in the selection log, wherein the selection log is adapted to store a correspondence relationship between the query data and the selection feature;

in the event that the current query data is present in the selection log, acquiring a first predetermined number of selected categories corresponding to the current query data from the selection log, wherein the selected categories represent categories associated with the current query data in a mapping table of a search engine; and

in the event that the current query data is not present in the selection log, inquiring in the selection log about selected query data closest to the current query data, wherein, the selected query data is at the shortest edit distance in the selection log from the current query data, and acquiring the first predetermined number of categories corresponding to the selected query data as selection features of the current query data.

12. The method of claim 2, wherein the generic attribute features comprise a session feature that corresponds to a substantially optimal category corresponding to a previous one query preceding the current query data in the same session.

13. The method of claim 12, wherein extracting the generic attribute features including the session feature comprises:

acquiring preceding query data that precedes the current query data in the same session;

acquiring a category to which an optimal categorizing result corresponding to the preceding query data belongs; and

setting the category to which the optimal categorizing result belongs as a session feature of the current query data.

14. The method of claim 1, further comprising determining the plurality of confidence degrees of the current query data based at least in part on the generic attribute features, wherein the determining of the plurality of confidence degrees of the current query data includes:

setting the generic attribute features as given information of a conditional probability model;

calculating a plurality of posterior probabilities that the current query belongs to the respective categories under condition of the given information using the conditional probability model; and

setting the plurality of posterior probabilities as the plurality of confidence degrees.

15. The method of claim 1, further comprising:

identifying selected ones of the plurality of confidence degrees that meet a preset first threshold and their corresponding categories; and

returning the selected ones of the plurality of confidence degrees and their corresponding categories to the client for selecting the selected category for the current query data.

16. The method of claim 1, wherein the chosen category is chosen by a user.

17. The method of claim 1, wherein the chosen category has a maximum calculated confidence degree for the current query data.

18. The method of claim 1, wherein the one or more categories relate to a respective one or more industry categories in which a product associated with a generic attribute feature belongs.

19. The method of claim 1, wherein the selecting of the category based at least in part on a plurality of confidence degrees comprises:

selecting a plurality of categories based at least in part on the plurality of confidence degrees;

providing the plurality of categories to a user; and

receiving an indication from the user of a selection of one of the plurality of categories,

wherein the searching in the selected category for the search result comprises searching in the category corresponding to indication of the selection received from the user.

20. The method of claim 1, wherein the selecting of the category based at least in part on a plurality of confidence degrees comprises:

selecting one or more categories based at least in part on whether the one or more categories are included on a selection log relating to the current query data.

21. A system for searching information, comprising:

one or more processors configured to;

receive current query data from a client;

computing a plurality of confidence degrees associated with the current query data, wherein the plurality of confidence degrees respectively indicate a degree of confidence that the current query data is associated with a corresponding one of a plurality of categories;

select 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;

in response to selecting the category based at least in part on the plurality of confidence degrees, search in the selected category for a search result that corresponds to the current query data; and

return the search result to the client; and

one or more memories coupled to the one or more processors, configured to provide the processors with instructions.

22. A computer program product for searching information, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:

causing at least one processor to receive current query data from a client;

causing at least one processor to compute a plurality of confidence degrees associated with the current query data, wherein the confidence degrees respectively indicate a degree of confidence that the current query data is associated with a corresponding one of a plurality of categories;

causing at least one processor to select 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;

causing at least one processor to search in the selected category for a search result that corresponds to the current query data in response to the category being selected based at least in part on the plurality of confidence degrees; and

causing at least one processor to return the search result to the client.