MOBILE SEARCH METHOD AND APPARATUS

• US 20110314059A1
• Filed: 08/26/2011
• Published: 12/22/2011
• Est. Priority Date: 02/27/2009
• Status: Abandoned Application

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Inventors: HU, Hanqiang
• Application Number:
  US13/219,058
• Publication Number:
  US 20110314059A1
• Time in Patent Office: Days
• US Class Current: 707/771
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A mobile search method and apparatus are provided. The method includes: receiving a search request, in which the search request includes one or more search keywords; calculating a score of each search category domain, in which the score is a score of any item or a comprehensive score of multiple items of the following: similarity between the search request and the search category domain, a mass search rate of the search category domain corresponding to the search request, and an individualized user interest score of the search category domain, and the mass search rate is the number of mass searches or the number of mass search result clicks; and selecting one or more of search category domains according to the score of each of the search category domains to search for the search keywords. Therefore, through the method and apparatus, an individualized accurate search result can be provided for a user.

Claims

1. A mobile search method, comprising:

receiving a search request, wherein the search request comprises one or more search keywords;

calculating a score of each search category domain, wherein the score is a score of any item or a comprehensive score of multiple items of the following;

similarity between the search request and the search category domain, a mass search rate of the search category domain corresponding to the search request, and an individualized user interest score of the search category domain; and

selecting one or more of search category domains according to the score of each search category domain to search for the search keywords.

2. The method according to claim 1, wherein the calculating the comprehensive score of each of the search category domains comprises:

calculating one of a product score, an average score, and a weighted score according to multiple items of the similarity between the search request and the search category domain, the mass search rate of the search category domain corresponding to the search request, and the individualized user interest score of the search category domain.

3. The method according to claim 1, wherein the calculating the similarity between the search request and the search category domain comprises:

setting a weight for the search keywords;

generating a query vector according to the weight of the search keywords;

generating a domain vector corresponding to the search category domain by using a weight of each word of the search category domain; and

acquiring the similarity between the search request and the search category domain by calculating the query vector and the domain vector.

4. The method according to claim 3, wherein the method further comprises one of:

determining a topic word and a relevant word of the search category domain and a weight of each of the words manually; and

determining the topic word and the relevant word of the search category domain and the weight of each of the words in an automatic learning manner.

5. The method according to claim 4, wherein the determining the topic word and the relevant word of the search category domain and the weight of each of the words in the automatic learning manner comprises:

acquiring a training text language material sample corresponding to each of the search category domains;

performing word cutting on the language material sample to generate a vocabulary of the search category domain;

calculating a weight of each word in the vocabulary; and

determining the topic word and the relevant word of the search category domain according to the weight of each word.

6. The method according to claim 5, wherein the determining the topic word and the relevant word of the search category domain and the weight of each word in the automatic learning manner further comprises:

dividing all words in the vocabulary into sets of different levels according to the weight; and

setting a final score for a set of each of the levels, and using the final score of each of the levels as the weight of each word of the level.

7. The method according to claim 3, wherein the setting the weight for the search keywords comprises one of:

setting a same weight for all of the search keywords; and

setting a maximum weight for the first keyword, setting a medium weight for a middle keyword, and setting a minimum weight for a last keyword.

8. The method according to claim 1, wherein the calculating the mass search rate of the search category domain corresponding to the search request comprises:

calculating a mass search rate of each search category domain corresponding to each of the search keywords in the search request; and

using a sum of mass search rates of a same search category domain corresponding to all of the search keywords in the search request as the mass search rate of the search category domain corresponding to the search request.

9. The method according to claim 8, wherein the mass search rate is the number of mass searches or the number of mass search result clicks.

10. The method according to claim 1, wherein the calculating the individualized user interest score of the search category domain comprises:

extracting a user interest model from user data, wherein the user interest model is a vector generated by scores of multiple interest dimensions according to the user data; and

using a sum of scores of one or more interest dimensions of the user interest model corresponding to the search category domain as the individualized user interest score of the search category domain.

11. The method according to claim 10, wherein the user interest model is one of a static interest model and a dynamic interest model;

extracting the user static interest model from the user data comprises;

calculating a sum of word occurrence frequencies of all words in a user static file that belong to each of the interest dimensions, and using the sum as a score corresponding to each of the interest dimensions;

or, calculating a score of similarity between the user static file and each of the interest dimensions, and using the score as a score corresponding to each of the interest dimensions; and

using a score corresponding to each of the interest dimensions as a vector to generate the user interest model; and

extracting the user dynamic interest model from the user data comprises;

calculating a sum of word occurrence frequencies of all words in a historical record of searches and clicks of a user that belong to each of the interest dimensions, and using the sum as a score corresponding to each of the interest dimension;

or calculating a score of similarity between the historical record of searches and clicks and each of the interest dimensions, and using the score as a score corresponding to each of the interest dimension; and

using a score of each of the interest dimensions as a vector to generate the user dynamic interest model.

12. The method according to claim 11, wherein the extracting the user interest model from the user data further comprises:

normalizing the static interest model and the dynamic interest model respectively; and

calculating a sum of one or more normalized static interest models and one or more normalized dynamic interest models, and using the sum as the user interest model.

13. The method according to claim 11, wherein the extracting the user interest model from the user data further comprises:

calculating a weighted sum of one or more static interest models and one or more dynamic interest models; and

normalizing the weighted sum, and using a normalized result as the user interest model.

14. The method according to claim 1, wherein the calculating the weighted score of each of the search category domains comprises:

calculating the similarity between the search request and the search category domain, and normalizing the similarity;

calculating the mass search rate of the search category domain corresponding to the search request, and normalizing the mass search rate;

calculating the individualized user interest score of the search category domain, and normalizing the individualized user interest score; and

calculating a weighted sum of any two or more of the normalized values to acquire the weighted score of the search category domain.

15. A mobile search apparatus, comprising:

a receiving unit, configured to receive a search request, wherein the search request comprises one or more search keywords;

a calculation unit, configured to calculate a score of each search category domain, wherein the score is a score of any item or a comprehensive score of multiple items of the following;

similarity between the search request and the search category domain, a mass search rate of the search category domain corresponding to the search request, and an individualized user interest score of the search category domain;

a selection unit, configured to select one or more of search category domains according to the score of each of the search category domains; and

a search unit, configured to search for the search keywords by using the one or more search category domains selected by the selection unit.

16. The apparatus according to claim 15, wherein the calculating, by the calculation unit, the comprehensive score of each of the search category domains comprises:

calculating one of a score of a product, an average score, and a weighted score according to multiple items of the similarity between the search request and the search category domain, the mass search rate of the search category domain corresponding to the search request, and the individualized user interest score of the search category domain.

17. The apparatus according to claim 15, wherein the calculation unit comprises any one or more of the following units:

a similarity calculation unit, configured to calculate the similarity between the search request and each of the search category domains;

a mass search rate calculation unit, configured to calculate the mass search rate of each of the search category domains corresponding to the search request; and

a user interest score calculation unit, configured to calculate the individualized user interest score of each of the search category domains.

18. The apparatus according to claim 17, wherein the similarity calculation unit comprises:

a weight setting subunit, configured to set a weight for the search keywords;

a query vector generation subunit, configured to generate a query vector according to the weight of the search keywords;

a domain vector generation unit, configured to generate a domain vector corresponding to the search category domain according to a weight of each word of the search category domain; and

a first calculation subunit, configured to acquire the similarity between the search request and the search category domain by calculating the query vector and the domain vector.

19. The apparatus according to claim 18, wherein the apparatus further comprises:

a setting unit, configured to determine a topic word and a relevant word of the search category domain and a weight of each of the words manually;

or

a learning unit, configured to determine the topic word and the relevant word of the search category domain and the weight of each of the words in an automatic learning manner.

20. The apparatus according to claim 19, wherein the learning unit comprises:

a language material sample acquisition subunit, configured to acquire a training text language material sample corresponding to each of the search category domains;

a vocabulary generation subunit, configured to perform word cutting on the language material sample to generate a vocabulary of the search category domain;

a weight calculation subunit, configured to calculate a weight of each word in the vocabulary; and

a topic word determination subunit, configured to determine the topic word and the relevant word of the search category domain according to the weight of each word.