CN108197308B - Keyword recommendation method and system based on search engine - Google Patents

Keyword recommendation method and system based on search engine Download PDF

Info

Publication number
CN108197308B
CN108197308B CN201810092422.4A CN201810092422A CN108197308B CN 108197308 B CN108197308 B CN 108197308B CN 201810092422 A CN201810092422 A CN 201810092422A CN 108197308 B CN108197308 B CN 108197308B
Authority
CN
China
Prior art keywords
explosion
spark
sparks
point
determining
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810092422.4A
Other languages
Chinese (zh)
Other versions
CN108197308A (en
Inventor
王春枝
王毅超
宗欣露
刘敏
陈颖哲
严灵毓
吴盼
任紫扉
蔡文成
王鑫
周方禹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiurun (Hubei) New Energy Technology Co.,Ltd.
Original Assignee
Hubei University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hubei University of Technology filed Critical Hubei University of Technology
Priority to CN201810092422.4A priority Critical patent/CN108197308B/en
Publication of CN108197308A publication Critical patent/CN108197308A/en
Application granted granted Critical
Publication of CN108197308B publication Critical patent/CN108197308B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a keyword recommendation method and system based on a search engine. The method comprises the following steps: acquiring input information input into a search engine by a user; acquiring data information related to the input information from an engine database according to the input information of the user; the data information related to the input information comprises N pieces of data information; acquiring the explosion times M of the firework algorithm; acquiring an explosion point of the Kth explosion of the firework algorithm according to the N pieces of data information and the explosion times M; acquiring the explosion radius and the number of sparks of the K-th explosion in the firework algorithm according to the N pieces of data information and the explosion times M; carrying out explosion by adopting a firework algorithm to obtain keywords related to the input information of the user; and recommending the keywords to a user through the search engine for the user to select. The method and the system of the invention improve the accuracy and efficiency of recommending the keywords.

Description

Keyword recommendation method and system based on search engine
Technical Field
The invention relates to the field of internet, in particular to a keyword recommendation method and system based on a search engine.
Background
With the rapid development of the internet, the resources on the internet have reached a mass scale, the structure is diversified, the distribution is unbalanced, the dynamic updating and changing speed is high, and the useful information updating speed and the storage condition are dynamic. The search engine technology is used as an effective information acquisition tool and important network application, and helps people to search needed information from a wide information resource sea. When a user searches through a search engine, the search engine will generally recommend related keywords according to the input of the user, and the user can preferentially select a matching search condition from the recommended keywords. However, the traditional search engine cannot provide interesting information for the user well in a big data environment, and the efficiency of searching keywords is not high, so that the accuracy of the presented information is low. The efficiency and performance of modern search engines must be improved in light of the need for personalized, intelligent retrieval. The frame structure and the core algorithm of the search engine basically determine the efficiency, the current particle swarm-based multi-target big data efficient intelligent search optimization algorithm is easy to fall into a local optimal solution, the local optimization capability is poor, and the accuracy of recommending keywords to a user is low and the efficiency is low.
Disclosure of Invention
The invention aims to provide a keyword recommendation method and system based on a search engine so as to improve the accuracy of keyword recommendation.
In order to achieve the purpose, the invention provides the following scheme:
a keyword recommendation method based on a search engine, the method comprising:
acquiring input information input into a search engine by a user;
acquiring data information related to the input information from an engine database according to the input information of the user; the data information related to the input information comprises N pieces of data information;
acquiring the explosion times M of the firework algorithm;
acquiring an explosion point of the K-th explosion of the firework algorithm according to the N pieces of data information and the explosion times M, wherein K is more than or equal to 1 and less than or equal to M;
acquiring the explosion radius and the number of sparks of the K-th explosion in the firework algorithm according to the N pieces of data information and the explosion times M;
carrying out explosion by adopting a firework algorithm to obtain keywords related to the input information of the user;
and recommending the keywords to a user through the search engine for the user to select.
Optionally, the obtaining of the explosion point of the kth explosion of the firework algorithm according to the N pieces of data information and the explosion times M specifically includes:
when K is 1, determining the N pieces of data information as an initial population of a firework algorithm, and determining N positions of the N pieces of data information as N explosion points of the 1 st explosion;
when K is more than or equal to 2 and less than or equal to M-1, obtaining the fitness value of the K-1 spark; the K-1 spark is a spark generated after the K-1 explosion; determining the spark with the minimum fitness value in the K-1 spark as an explosion point of the K explosion; determining the rest N-1 explosion points of the Kth explosion by using the selected probability according to the distance between sparks in the Kth spark-1;
when K is M, obtaining the fitness value of the M-1 spark; the M-1 spark is a spark generated after the M-1 explosion; and determining the spark with the minimum fitness value in the M-1 spark as an explosion point of the Mth explosion.
Optionally, the determining the remaining N-1 explosion points of the kth explosion by using the selected probability according to the distance between the sparks in the K-1 spark specifically includes:
using formulas
Figure BDA0001564080670000021
Calculating a selected probability P (X) for each of the K-1 sparki) (ii) a Wherein R (X)i) For the X-th spark in the K-1 sparkiThe sum of the distances between the spark and the other sparks,
Figure BDA0001564080670000022
R(Xj) For the X-th spark in the K-1 sparkjThe sum of the distances between the spark and other sparks;
ranking the selected probabilities of all sparks in the K-1 spark;
and determining the first N-1 sparks with the highest probability to be the rest N-1 explosion points of the K explosion.
Optionally, the obtaining of the explosion radius and the number of sparks of the kth explosion in the firework algorithm according to the N pieces of data information and the explosion number M specifically includes:
when K is 1, acquiring a preset explosion radius and a preset spark number as the explosion radius and the spark number of the 1 st explosion;
when K is more than or equal to 2 and less than or equal to M; using formulas
Figure BDA0001564080670000031
Determining the explosion point X of the Kth explosioniRadius of detonation Ri；
Using formulas
Figure BDA0001564080670000032
Determining the explosion point X of the Kth explosioniNumber of sparks Si；
Wherein, f (X)i) Is an explosion point XiFitness value of ymin＝min(f(Xj))，ymax＝max(f(Xj))，(j＝1,2,…N)，yminIs the minimum value of the fitness values of all n sparks in the K-1 spark, ymaxIs the maximum value of the fitness values of all n sparks in the K-1 spark;
Figure BDA0001564080670000033
is the maximum radius preset in the firework algorithm,
Figure BDA0001564080670000034
the maximum value of the number of sparks preset in the firework algorithm is obtained; ε is the minimum machine amount; the K-1 spark is the spark generated after the K-1 explosion.
Optionally, the determining KthExplosion point X of secondary explosioniAfter the number of sparks, the method further comprises:
using formulas
Figure BDA0001564080670000035
The explosion point X of the K-th explosioniIs corrected to Si', wherein a and b are arbitrary constants, and a < b < 1.
Optionally, the explosion by using the firework algorithm specifically includes:
fireworks explosion is adopted to generate explosion sparks and Gaussian variation sparks;
for explosion point XiAcquiring a plurality of explosion directions for generating explosion sparks; for the k explosion direction, the formula is used
Figure BDA0001564080670000036
Determining the position of an explosion spark
Figure BDA0001564080670000037
Wherein R isiIs an explosion point XiThe radius of detonation of;
Figure BDA0001564080670000038
is the position before explosion;
for explosion point XiAcquiring a plurality of explosion directions for generating Gaussian variant sparks; for the jth explosion direction, the formula is utilized
Figure BDA0001564080670000041
Determining the position of a Gaussian variant spark
Figure BDA0001564080670000042
Wherein e to N (1,1), N (1,1) represents a Gaussian distribution function with a mean value of 1 and a variance of 1.
Optionally, after determining the position of the gaussian variant spark, the method further includes:
judging whether the position of the explosion spark exceeds the boundary range of a feasible region or not to obtain a first judgment result;
when the first judgment result shows that the position of the explosion spark exceeds the boundary range of a feasible region, a formula is used
Figure BDA0001564080670000043
Updating the position of the explosion spark in the K-th explosion direction to
Figure BDA0001564080670000044
Wherein XUB,kIs the upper boundary of the feasible region, XLB,kIs the lower boundary of the feasible region;
judging whether the position of the Gaussian variation spark exceeds the boundary range of a feasible region or not to obtain a second judgment result;
when the second judgment result shows that the position of the Gaussian variation spark exceeds the boundary range of a feasible region, a formula is used
Figure BDA0001564080670000045
Updating the position of the Gaussian variation spark in the jth explosion direction to
Figure BDA0001564080670000046
The invention also provides a keyword recommendation system based on the search engine, which is applied to the method and comprises the following steps:
the input information acquisition module is used for acquiring input information input into the search engine by a user;
the data information acquisition module is used for acquiring data information related to the input information from an engine database according to the input information of the user; the data information related to the input information comprises N pieces of data information;
the explosion times acquisition module is used for acquiring the explosion times M of the firework algorithm;
the explosion point acquisition module is used for acquiring the explosion point of the K-th explosion of the firework algorithm according to the N pieces of data information and the explosion times M, wherein K is more than or equal to 1 and less than or equal to M;
the explosion radius and spark number obtaining module is used for obtaining the explosion radius and spark number of the Kth explosion in the firework algorithm according to the N pieces of data information and the explosion times M;
the explosion module is used for carrying out explosion by adopting a firework algorithm to obtain keywords related to the input information of the user;
and the keyword recommending module is used for recommending the keywords to the user through the search engine for the user to select.
Optionally, the explosion point obtaining module specifically includes:
a first explosion point determining unit, configured to determine the N pieces of data information as an initial population of a firework algorithm when K is 1, and determine N positions of the N pieces of data information as N explosion points of a 1 st explosion;
the second explosion point determining unit is used for obtaining the fitness value of the K-1 spark when K is more than or equal to 2 and less than or equal to M-1; the K-1 spark is a spark generated after the K-1 explosion; determining the spark with the minimum fitness value in the K-1 spark as an explosion point of the K explosion; determining the rest N-1 explosion points of the Kth explosion by using the selected probability according to the distance between sparks in the Kth spark-1;
a third explosion point determination unit for obtaining a fitness value of an M-1 th spark when K is M; the M-1 spark is a spark generated after the M-1 explosion; and determining the spark with the minimum fitness value in the M-1 spark as an explosion point of the Mth explosion.
Optionally, the module for acquiring the explosion radius and the number of sparks specifically includes:
a first acquisition unit, configured to acquire a preset explosion radius and a preset number of sparks as an explosion radius and a preset number of sparks of a 1 st explosion when K is 1;
a second obtaining unit, configured to, when K is greater than or equal to 2 and less than or equal to M; using formulas
Figure BDA0001564080670000051
Determining the explosion point X of the first explosioniRadius of detonation Ri(ii) a Using formulas
Figure BDA0001564080670000052
Determining the explosion point X of the Kth explosioniNumber of sparks Si(ii) a Wherein, f (X)i) Is an explosion point XiFitness value of ymin＝min(f(Xj))，ymax＝max(f(Xj))，(j＝1,2,…N)，yminIs the minimum value of the fitness values of all n sparks in the K-1 spark, ymaxIs the maximum value of the fitness values of all n sparks in the K-1 spark;
Figure BDA0001564080670000053
is the maximum radius preset in the firework algorithm,
Figure BDA0001564080670000054
the maximum value of the number of sparks preset in the firework algorithm is obtained; ε is the minimum machine amount; the K-1 spark is the spark generated after the K-1 explosion.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the search engine adopting the firework algorithm can enable users to participate in the search process, so that the search result is closest to the user requirement, and the accuracy and the satisfaction of the search result can be ensured; in the firework algorithm searching process, fireworks are simultaneously exploded for searching, so that the retrieval efficiency is effectively improved; the quality of the firework population is continuously optimized, and lean and refined, so that the matching degree of the search result and the keywords is optimal; the method for searching the global optimal solution by utilizing the fast convergence speed and the good search capability of the firework algorithm is a good application method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a keyword recommendation method based on a search engine according to the present invention;
FIG. 2 is a schematic structural diagram of a keyword recommendation system based on a search engine according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Taking the example that a user needs to search for ' children's suitable extraclass reading materials ', the scheme of the invention is explained by combining with fig. 1, and fig. 1 is a flow diagram of the keyword recommendation method based on the search engine; as shown in fig. 1, the method includes:
step 100: acquiring input information input into a search engine by a user. The user's input information may be "child" at this time.
Step 200: and acquiring data information related to the input information from the engine database according to the input information of the user. The data information related to the input information includes N pieces of data information, and the data information may be a keyword, a phrase, or a sentence. For example, the N pieces of data information obtained at this time may include: children's toys, children's age range, what medicine children eat for a fever, children's glasses, etc.
Step 300: and acquiring the explosion times of the firework algorithm. The search engine needs to set the number M of fireworks explosions, i.e. the number of iterations, when searching using the fireworks algorithm.
Step 400: and acquiring the explosion point of the K-th explosion of the firework algorithm according to the N pieces of data information and the explosion times M. Wherein K is more than or equal to 1 and less than or equal to M. In the explosion process, the iteration times are different, and the corresponding determination methods of the explosion points are different, specifically:
when K is 1, namely the first explosion, the N pieces of data information are determined as the initial population of the firework algorithm, and N positions of the N pieces of data information are determined as N explosion points of the 1 st explosion.
When K is more than or equal to 2 and less than or equal to M-1, obtaining the fitness value of the K-1 spark; the K-1 spark is a spark generated after the K-1 explosion; determining the spark with the minimum fitness value in the K-1 spark as an explosion point of the K explosion; and determining the rest N-1 explosion points of the K explosion by using the selected probability according to the distance between the sparks in the K-1 spark.
Using formulas
Figure BDA0001564080670000071
Calculating a selected probability P (X) for each of the K-1 sparki) (ii) a Wherein R (X)i) For the X-th spark in the K-1 sparkiThe sum of the distances between the spark and the other sparks,
Figure BDA0001564080670000072
R(Xj) For the X-th spark in the K-1 sparkjThe sum of the distances between the spark and other sparks;
ranking the selected probabilities of all sparks in the K-1 spark;
and determining the first N-1 sparks with the highest probability to be the rest N-1 explosion points of the K explosion. In selection, if the individual density of sparks is high, i.e., there are many other candidate individuals around the individual, the probability that the individual is selected is reduced.
When K is M, namely the last explosion, obtaining the fitness value of the M-1 spark; the M-1 spark is a spark generated after the M-1 explosion; the sparks generated after the M-1 explosion comprise explosion sparks generated by an explosion operator and Gaussian variant sparks generated by variant sparks; and determining the spark with the minimum fitness value in the M-1 spark as an explosion point of the Mth explosion.
Step 500: and acquiring the explosion radius and the number of sparks of the K-th explosion in the firework algorithm according to the N pieces of data information and the explosion times M. Different iteration times correspond to different determination methods of explosion radius and spark quantity, and specifically:
when K is 1, acquiring a preset explosion radius and a preset spark number as the explosion radius and the spark number of the 1 st explosion; that is, the explosion radius and the number of sparks of the explosion point at the first explosion are preset.
When K is more than or equal to 2 and less than or equal to M; using formulas
Figure BDA0001564080670000081
Determining the explosion point X of the Kth explosioniRadius of detonation Ri(ii) a Using formulas
Figure BDA0001564080670000082
Determining the explosion point X of the Kth explosioniNumber of sparks Si(ii) a Wherein, f (X)i) Is an explosion point XiThe value of the fitness value of (a) is,
Figure BDA0001564080670000083
ymin＝min(f(Xj))，ymax＝max(f(Xj))，(j＝1,2,…N)，yminis the minimum value, y, of the fitness values of all N sparks in the K-1 sparkmaxIs the maximum of the fitness values of all N sparks in the K-1 spark;
Figure BDA0001564080670000084
is the maximum radius preset in the firework algorithm,
Figure BDA0001564080670000085
the maximum value of the number of sparks preset in the firework algorithm is obtained; ε is the minimum machine amount; the K-1 spark is the spark generated after the K-1 explosion. During the subsequent explosion, the explosion radius and the number of sparks are determined according to the fitness value of the spark of the current generation.
Because the fireworks with better performance have larger influence on the offspring,easily reduce the diversity of the population, and the number S of sparks in a certain radiusiAnd (4) limiting: using formulas
Figure BDA0001564080670000086
The explosion point X of the K-th explosioniIs corrected to Si', wherein a and b are arbitrary constants, and a < b < 1.
Step 600: and exploding by adopting a firework algorithm to obtain keywords related to the input information of the user. The fireworks algorithm (FWA) is a new optimization algorithm proposed based on the observation that fireworks explode in the air to produce a spark phenomenon. And the position vector of each firework represents each group of solutions, the offspring sparks are generated by explosion in the sky, and are continuously exploded, and repeated iteration is performed to complete the search of the solutions. Thus, each explosion requires the determination of the explosion point, the explosion radius and the number of sparks. And the spark generated by the last explosion is the final searched result, and the keyword corresponding to the position of the spark is the keyword related to the input information of the user. For example, the searched keywords may be: how much the child has fever, the age of the child, etc.
Specifically, when fireworks are exploded and exploded, an explosion operator generates explosion sparks, and a mutation operator generates Gaussian mutation sparks;
when the explosion spark is exploded, the explosion spark is exploded in multiple dimensions (directions) to an explosion point XiAcquiring a plurality of explosion directions for generating explosion sparks; for the k explosion direction, the formula is used
Figure BDA0001564080670000091
Determining the position of an explosion spark
Figure BDA0001564080670000092
Wherein R isiIs an explosion point XiThe radius of detonation of;
Figure BDA0001564080670000093
is the position before explosion;
explosion of Gaussian variant sparkWill explode in multiple dimensions (directions) for an explosion point XiAcquiring a plurality of explosion directions for generating Gaussian variant sparks; for the jth explosion direction, the formula is utilized
Figure BDA0001564080670000094
Determining the position of a Gaussian variant spark
Figure BDA0001564080670000095
Wherein e to N (1,1), N (1,1) represents a Gaussian distribution function with a mean value of 1 and a variance of 1; wherein e to N (1,1), N (1,1) represents a Gaussian distribution function with a mean value of 1 and a variance of 1.
In the process of generating explosion sparks and Gaussian variant sparks by the explosion operator and the variant operator respectively, sparks possibly generated exceed the boundary range of the feasible region omega. Firstly, whether the position of an explosion spark and/or the position of a Gaussian variation spark exceeds the boundary range of a feasible domain or not needs to be judged, and when the position exceeds the boundary on a dimension k, a new position is mapped by the following mapping rule:
when the position of the explosion spark exceeds the boundary range of the feasible region, the formula is used
Figure BDA0001564080670000096
Updating the location of the detonation spark in the k-th detonation direction to
Figure BDA0001564080670000097
Wherein XUB,kIs the upper boundary of the feasible region, XLB,kIs the lower boundary of the feasible region;
when the position of the Gaussian variant spark exceeds the boundary range of the feasible region, the formula is used
Figure BDA0001564080670000098
Updating the position of the Gaussian variation spark in the jth explosion direction to
Figure BDA0001564080670000099
Step 700: and recommending the keywords to a user through the search engine for the user to select. At this time, after the user inputs "children", the search engine recommends keywords such as "how children are getting fever", "children age", and the like to the user for selection. The process of step 100 is resumed when the user continues to enter or reenter new keywords.
The traditional process of searching keywords by a search engine is a multi-objective optimization algorithm based on particle swarm, so that a local optimal solution is easy to trap, and the local optimization capability is poor. The firework algorithm has a local searching capability and a global searching capability self-adjusting mechanism, and each firework in the firework population carries out resource distribution and information interaction according to the fitness value relative to other fireworks, so that the whole population can reach a balance between the global searching capability and the local searching capability. And the explosion searching mechanism of the fireworks enables a single firework to have strong local explosiveness, each individual firework in the population can generate a plurality of individuals in one iteration process in the iteration process of the fireworks algorithm, the particle swarm optimization algorithm generally only generates one individual, and the explosion searching mechanism of the fireworks algorithm enables the area near the fireworks to be searched more thoroughly.
Self-adjusting process: in a firework population, an algorithm determines an explosion radius and the number of explosion sparks only according to the difference between the fitness values of fireworks, the position of the fireworks with good fitness values is considered as a potential better area, more search resources can be obtained, the explosion radius is smaller, more sparks are generated in a smaller adjacent area to enable the fireworks to be finely searched in the area around the position, the fireworks with poor fitness values can only obtain relatively fewer resources, the explosion radius is larger, the search range is correspondingly increased, and the fireworks generate less sparks in a larger adjacent area to perform large-range global search around the position.
Moreover, the invention adopts a firework algorithm based on binary coding, the firework shell is a binary code with the length of m, and the explosion range of the firework shell Xi is controlled according to the explosion radius r, namely the binary bit is updated within 1-r. The radius r is non-linearly decreasing with the number of iterations. Each bit of Xi may be passed through a pair of variantsThe significance is obtained by the probability p, and the expression is
Figure BDA0001564080670000101
When the position is updated, a [0, 1 ] is randomly generated]If rand () < p, bkIf it is 0, the binary bit of the corresponding position takes the opposite state of the original one; otherwise, the holding state is unchanged.
FIG. 2 is a schematic structural diagram of a keyword recommendation system based on a search engine according to the present invention. As shown in fig. 2, the system includes:
an input information obtaining module 201, configured to obtain input information input by a user to a search engine;
a data information obtaining module 202, configured to obtain, according to the input information of the user, data information related to the input information from an engine database; the data information related to the input information comprises N pieces of data information;
the explosion times obtaining module 203 is used for obtaining the explosion times M of the firework algorithm;
an explosion point obtaining module 204, configured to obtain an explosion point of the kth explosion of the firework algorithm according to the N pieces of data information and the explosion frequency M, where K is greater than or equal to 1 and less than or equal to M; the explosion point obtaining module 204 specifically includes:
a first explosion point determining unit, configured to determine the N pieces of data information as an initial population of a firework algorithm when K is 1, and determine N positions of the N pieces of data information as N explosion points of a 1 st explosion;
the second explosion point determining unit is used for obtaining the fitness value of the K-1 spark when K is more than or equal to 2 and less than or equal to M-1; the K-1 spark is a spark generated after the K-1 explosion; determining the spark with the minimum fitness value in the K-1 spark as an explosion point of the K explosion; determining the rest N-1 explosion points of the Kth explosion by using the selected probability according to the distance between sparks in the Kth spark-1;
a third explosion point determination unit for obtaining a fitness value of an M-1 th spark when K is M; the M-1 spark is a spark generated after the M-1 explosion; and determining the spark with the minimum fitness value in the M-1 spark as an explosion point of the Mth explosion.
An explosion radius and spark number obtaining module 205, configured to obtain an explosion radius and a spark number of a kth explosion in the firework algorithm according to the N pieces of data information and the number of times of explosion M; the explosion radius and spark number obtaining module 205 specifically includes:
a first acquisition unit, configured to acquire a preset explosion radius and a preset number of sparks as an explosion radius and a preset number of sparks of a 1 st explosion when K is 1;
a second obtaining unit, configured to, when K is greater than or equal to 2 and less than or equal to M; using formulas
Figure BDA0001564080670000111
Determining the explosion point X of the Kth explosioniRadius of detonation Ri(ii) a Using formulas
Figure BDA0001564080670000112
Determining the explosion point X of the Kth explosioniNumber of sparks Si(ii) a Wherein, f (X)i) Is an explosion point XiFitness value of ymin＝min(f(Xj))，ymax＝max(f(Xj))，(j＝1,2,…N)，yminIs the minimum value of the fitness values of all n sparks in the K-1 spark, ymaxIs the maximum value of the fitness values of all n sparks in the K-1 spark;
Figure BDA0001564080670000121
is the maximum radius preset in the firework algorithm,
Figure BDA0001564080670000122
the maximum value of the number of sparks preset in the firework algorithm is obtained; ε is the minimum machine amount; the K-1 spark is the spark generated after the K-1 explosion.
The explosion module 206 is configured to explode with a firework algorithm to obtain keywords related to the input information of the user;
and the keyword recommending module 207 is used for recommending the keywords to the user through the search engine for the user to select.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (8)

1. A keyword recommendation method based on a search engine is characterized by comprising the following steps:
acquiring input information input into a search engine by a user;
acquiring data information related to the input information from an engine database according to the input information of the user; the data information related to the input information comprises N pieces of data information;
acquiring the explosion times M of the firework algorithm;
acquiring an explosion point of the K-th explosion of the firework algorithm according to the N pieces of data information and the explosion times M, wherein K is more than or equal to 1 and less than or equal to M;
acquiring the explosion radius and the number of sparks of the K-th explosion in the firework algorithm according to the N pieces of data information and the explosion times M; the method specifically comprises the following steps: when K is 1, acquiring a preset explosion radius and a preset spark number as the explosion radius and the spark number of the 1 st explosion; when K is more than or equal to 2 and less than or equal to M; using formulas
Figure FDA0002406965870000011
Determining the Kth timeExplosion point X of an explosioniRadius of detonation Ri(ii) a Using formulas
Figure FDA0002406965870000012
Determining the explosion point X of the Kth explosioniNumber of sparks Si(ii) a Wherein, f (X)i) Is an explosion point XiFitness value of ymin＝min(f(Xj))，ymax＝max(f(Xj))，(j＝1,2,…N)，yminIs the minimum value of the fitness values of all n sparks in the K-1 spark, ymaxIs the maximum value of the fitness values of all n sparks in the K-1 spark;
Figure FDA0002406965870000013
is the maximum radius preset in the firework algorithm,
Figure FDA0002406965870000014
the maximum value of the number of sparks preset in the firework algorithm is obtained; ε is the minimum machine amount; the K-1 spark is a spark generated after the K-1 explosion;
carrying out explosion by adopting a firework algorithm to obtain keywords related to the input information of the user; the keyword corresponding to the position of the spark generated by the last explosion is the keyword related to the input information of the user;
and recommending the keywords to a user through the search engine for the user to select.
2. The method according to claim 1, wherein the obtaining of the explosion point of the kth explosion of the firework algorithm according to the N pieces of data information and the explosion number M specifically comprises:
when K is 1, determining the N pieces of data information as an initial population of a firework algorithm, and determining N positions of the N pieces of data information as N explosion points of the 1 st explosion;
when K is more than or equal to 2 and less than or equal to M-1, obtaining the fitness value of the K-1 spark; the K-1 spark is a spark generated after the K-1 explosion; determining the spark with the minimum fitness value in the K-1 spark as an explosion point of the K explosion; determining the rest N-1 explosion points of the Kth explosion by using the selected probability according to the distance between sparks in the Kth spark-1;
when K is M, obtaining the fitness value of the M-1 spark; the M-1 spark is a spark generated after the M-1 explosion; and determining the spark with the minimum fitness value in the M-1 spark as an explosion point of the Mth explosion.
3. The method of claim 2, wherein determining the remaining N-1 explosion points for the kth explosion using a selected probability based on the distance between sparks in the kth spark, comprises:
using formulas
Figure FDA0002406965870000021
Calculating a selected probability P (X) for each of the K-1 sparki) (ii) a Wherein R (X)i) For the X-th spark in the K-1 sparkiThe sum of the distances between the spark and the other sparks,
Figure FDA0002406965870000022
R(Xj) For the X-th spark in the K-1 sparkjThe sum of the distances between the spark and other sparks;
ranking the selected probabilities of all sparks in the K-1 spark;
and determining the first N-1 sparks with the highest probability to be the rest N-1 explosion points of the K explosion.
4. Method according to claim 1, characterized in that said determination of the explosion point X of the kth explosion is carried outiAfter the number of sparks, the method further comprises:
using formulas
Figure FDA0002406965870000023
The explosion point X of the K-th explosioniIs corrected to Si′，Wherein a and b are arbitrary constants, and a < b < 1.
5. The method according to claim 1, wherein the exploding with the fireworks algorithm specifically comprises:
fireworks explosion is adopted to generate explosion sparks and Gaussian variation sparks;
for explosion point XiAcquiring a plurality of explosion directions for generating explosion sparks; for the k explosion direction, the formula is used
Figure FDA0002406965870000031
Determining the position of an explosion spark
Figure FDA0002406965870000032
Wherein R isiIs an explosion point XiThe radius of detonation of;
Figure FDA0002406965870000033
is the position before explosion;
for explosion point XiAcquiring a plurality of explosion directions for generating Gaussian variant sparks; for the jth explosion direction, the formula is utilized
Figure FDA0002406965870000034
Determining the position of a Gaussian variant spark
Figure FDA0002406965870000035
Wherein e to N (1,1), N (1,1) represents a Gaussian distribution function with a mean value of 1 and a variance of 1.
6. The method of claim 5, wherein after determining the location of the Gaussian variant spark, further comprising:
judging whether the position of the explosion spark exceeds the boundary range of a feasible region or not to obtain a first judgment result;
when the first judgment result shows that the position of the explosion spark exceeds the boundary range of the feasible regionAround the time, using the formula
Figure FDA0002406965870000036
Updating the position of the explosion spark in the K-th explosion direction to
Figure FDA0002406965870000037
Wherein XUB,kIs the upper boundary of the feasible region, XLB,kIs the lower boundary of the feasible region;
judging whether the position of the Gaussian variation spark exceeds the boundary range of a feasible region or not to obtain a second judgment result;
when the second judgment result shows that the position of the Gaussian variation spark exceeds the boundary range of a feasible region, a formula is used
Figure FDA0002406965870000038
Updating the position of the Gaussian variation spark in the jth explosion direction to
Figure FDA0002406965870000039
7. A keyword recommendation system based on a search engine, the system comprising:
the input information acquisition module is used for acquiring input information input into the search engine by a user;
the data information acquisition module is used for acquiring data information related to the input information from an engine database according to the input information of the user; the data information related to the input information comprises N pieces of data information;
the explosion times acquisition module is used for acquiring the explosion times M of the firework algorithm;
the explosion point acquisition module is used for acquiring the explosion point of the K-th explosion of the firework algorithm according to the N pieces of data information and the explosion times M, wherein K is more than or equal to 1 and less than or equal to M;
an explosion radius and spark number obtaining module for obtaining the explosion radius and spark number according to the N pieces of data informationThe explosion times M obtain the explosion radius and the number of sparks of the Kth explosion in the firework algorithm; the explosion radius and spark number acquisition module specifically comprises: a first acquisition unit, configured to acquire a preset explosion radius and a preset number of sparks as an explosion radius and a preset number of sparks of a 1 st explosion when K is 1; a second obtaining unit, configured to, when K is greater than or equal to 2 and less than or equal to M; using formulas
Figure FDA0002406965870000041
Determining the explosion point X of the first explosioniRadius of detonation Ri(ii) a Using formulas
Figure FDA0002406965870000042
Determining the explosion point X of the Kth explosioniNumber of sparks Si(ii) a Wherein, f (X)i) Is an explosion point XiFitness value of ymin＝min(f(Xj))，ymax＝max(f(Xj))，(j＝1,2,…N)，yminIs the minimum value of the fitness values of all n sparks in the K-1 spark, ymaxIs the maximum value of the fitness values of all n sparks in the K-1 spark;
Figure FDA0002406965870000043
is the maximum radius preset in the firework algorithm,
Figure FDA0002406965870000044
the maximum value of the number of sparks preset in the firework algorithm is obtained; ε is the minimum machine amount; the K-1 spark is a spark generated after the K-1 explosion;
the explosion module is used for carrying out explosion by adopting a firework algorithm to obtain keywords related to the input information of the user; the keyword corresponding to the position of the spark generated by the last explosion is the keyword related to the input information of the user;
and the keyword recommending module is used for recommending the keywords to the user through the search engine for the user to select.
8. The system according to claim 7, wherein the explosion point obtaining module specifically comprises:
a first explosion point determining unit, configured to determine the N pieces of data information as an initial population of a firework algorithm when K is 1, and determine N positions of the N pieces of data information as N explosion points of a 1 st explosion;
the second explosion point determining unit is used for obtaining the fitness value of the K-1 spark when K is more than or equal to 2 and less than or equal to M-1; the K-1 spark is a spark generated after the K-1 explosion; determining the spark with the minimum fitness value in the K-1 spark as an explosion point of the K explosion; determining the rest N-1 explosion points of the Kth explosion by using the selected probability according to the distance between sparks in the Kth spark-1;
a third explosion point determination unit for obtaining a fitness value of an M-1 th spark when K is M; the M-1 spark is a spark generated after the M-1 explosion; and determining the spark with the minimum fitness value in the M-1 spark as an explosion point of the Mth explosion.
CN201810092422.4A 2018-01-31 2018-01-31 Keyword recommendation method and system based on search engine Active CN108197308B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810092422.4A CN108197308B (en) 2018-01-31 2018-01-31 Keyword recommendation method and system based on search engine

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810092422.4A CN108197308B (en) 2018-01-31 2018-01-31 Keyword recommendation method and system based on search engine

Publications (2)

Publication Number Publication Date
CN108197308A CN108197308A (en) 2018-06-22
CN108197308B true CN108197308B (en) 2020-06-05

Family

ID=62591268

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810092422.4A Active CN108197308B (en) 2018-01-31 2018-01-31 Keyword recommendation method and system based on search engine

Country Status (1)

Country Link
CN (1) CN108197308B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110610017B (en) * 2019-07-16 2021-10-29 安徽理工大学 Mining subsidence prediction parameter solving method based on improved firework algorithm

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005091825A2 (en) * 2004-03-04 2005-10-06 Yahoo! Inc. Keyword recommendation for internet search engines
CN102902753A (en) * 2012-09-20 2013-01-30 北京奇虎科技有限公司 Method and device for complementing search terms and establishing individual interest models
CN102929924A (en) * 2012-09-20 2013-02-13 百度在线网络技术（北京）有限公司 Method and device for generating word selecting searching result based on browsing content
US8874541B1 (en) * 2012-01-31 2014-10-28 Intuit Inc. Social search engine optimizer enhancer for online information resources
CN106503661A (en) * 2016-10-25 2017-03-15 陕西师范大学 Face gender identification method based on fireworks depth belief network

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005091825A2 (en) * 2004-03-04 2005-10-06 Yahoo! Inc. Keyword recommendation for internet search engines
US8874541B1 (en) * 2012-01-31 2014-10-28 Intuit Inc. Social search engine optimizer enhancer for online information resources
CN102902753A (en) * 2012-09-20 2013-01-30 北京奇虎科技有限公司 Method and device for complementing search terms and establishing individual interest models
CN102929924A (en) * 2012-09-20 2013-02-13 百度在线网络技术（北京）有限公司 Method and device for generating word selecting searching result based on browsing content
CN106503661A (en) * 2016-10-25 2017-03-15 陕西师范大学 Face gender identification method based on fireworks depth belief network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于二进制烟花算法的特征选择方法;路永和等;《情报学报》;20170331;第36卷(第3期);论文正文第249-253页 *
烟花算法研究进展;谭营等;《智能系统学报》;20141031;第9卷(第5期);论文正文第515-525页 *

Also Published As

Publication number Publication date
CN108197308A (en) 2018-06-22

Similar Documents

Publication Publication Date Title
US8484181B2 (en) Cloud matching of a question and an expert
US5317507A (en) Method for document retrieval and for word sense disambiguation using neural networks
CN108829865B (en) Information retrieval method and device
US20120095978A1 (en) Related item usage for matching questions to experts
Nabli et al. Efficient cloud service discovery approach based on LDA topic modeling
De Vocht et al. Discovering meaningful connections between resources in the web of data
CN111159361B (en) Method and device for acquiring article and electronic equipment
CN113011471A (en) Social group dividing method, social group dividing system and related devices
JP6973255B2 (en) Word vector changing device, method, and program
CN114896377A (en) Knowledge graph-based answer acquisition method
CN107239554B (en) Method for retrieving English text based on matching degree
Güran et al. Efficient feature integration with Wikipedia-based semantic feature extraction for Turkish text summarization
CN108197308B (en) Keyword recommendation method and system based on search engine
CN114490926A (en) Method and device for determining similar problems, storage medium and terminal
CN111723179A (en) Feedback model information retrieval method, system and medium based on concept map
CN113312523B (en) Dictionary generation and search keyword recommendation method and device and server
CN112507097B (en) Method for improving generalization capability of question-answering system
CN114328820A (en) Information searching method and related equipment
Nio et al. Improving the robustness of example-based dialog retrieval using recursive neural network paraphrase identification
CN113128210B (en) Webpage form information analysis method based on synonym discovery
Peña et al. Siambert: Siamese bert-based code search
Mishra et al. Review of Web Page Clustering
Martellozzo Integrating semantic and keyword search: a transformer-based approach for content discovery
Banerjee Detecting ambiguity in conversational systems
Setia et al. Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20231008

Address after: D13, B1/F, Ouyada Hanyang International Plaza, 123 Longyang Avenue, Hanyang District, Wuhan City, Hubei Province, 430000

Patentee after: Jiurun (Hubei) New Energy Technology Co.,Ltd.

Address before: 430000 South Eru 28, Hongshan District, Wuhan, Hubei

Patentee before: HUBEI University OF TECHNOLOGY