CN110333949B - Search engine processing method, device, terminal and storage medium - Google Patents

Search engine processing method, device, terminal and storage medium Download PDF

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CN110333949B
CN110333949B CN201910523572.0A CN201910523572A CN110333949B CN 110333949 B CN110333949 B CN 110333949B CN 201910523572 A CN201910523572 A CN 201910523572A CN 110333949 B CN110333949 B CN 110333949B
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search engine
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query request
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CN110333949A (en
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李俊良
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0607—Regulated
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0611—Request for offers or quotes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/78—Architectures of resource allocation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/80—Actions related to the user profile or the type of traffic

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Abstract

The invention discloses a search engine processing method, a search engine processing device, a search engine processing terminal and a storage medium. The method comprises the following steps: acquiring a query request; screening out search engines of which the flow rate does not reach a preset flow rate value corresponding to the corresponding search engine from the at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by utilizing the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine; the first parameter represents the estimated income brought by the corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine; the obtained query request is responded to by one of the at least one search engine.

Description

Search engine processing method, device, terminal and storage medium
Technical Field
The present invention relates to the field of computer applications, and in particular, to a search engine processing method, apparatus, terminal, and storage medium.
Background
Smart devices have evolved with increasing use. It has become a common phenomenon that users can query themselves for information of interest through smart devices.
However, how to reasonably distribute the traffic for each search engine during searching is a problem to be solved urgently.
Disclosure of Invention
In order to solve the existing technical problem, embodiments of the present invention provide a search engine processing method, apparatus, terminal, and storage medium.
The technical scheme of the embodiment of the invention is realized as follows:
the embodiment of the invention provides a search engine processing method, which comprises the following steps:
acquiring a query request;
screening out a search engine of which the corresponding flow rate does not reach a preset flow rate value from at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by utilizing the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine; the first parameter represents the maximum estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine;
the obtained query request is responded to by one of the at least one search engine.
In the above scheme, the method further comprises:
and aiming at each search engine in the at least two search engines, determining a preset flow value of the corresponding search engine by using the first parameter, the second parameter and the third parameter.
In the above scheme, the method further comprises:
for each of the at least two search engines, determining a corresponding first parameter, second parameter, and third parameter.
In the foregoing solution, the determining the corresponding second parameter includes:
and determining the corresponding second parameter by utilizing the historical income related information brought by the corresponding search engine and combining the historical total flow related information of the corresponding search engine.
In the foregoing solution, the determining the corresponding third parameter includes:
and identifying a result by using the intention of the historical query request, and determining the corresponding third parameter.
In the scheme, at least two search engines are screened out; the responding to the obtained query request by using one search engine in at least one search engine comprises:
obtaining the bid of each search engine in the screened at least two search engines to the obtained query request;
determining a search engine from the screened at least two search engines by using bidding results and combining a polling mechanism;
and responding to the acquired query request by utilizing the determined search engine.
An embodiment of the present invention further provides a search engine processing apparatus, including:
an acquisition unit configured to acquire an inquiry request;
the screening unit is used for screening the search engines of which the corresponding flow rate does not reach the preset flow rate value from the at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by utilizing the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine; the first parameter represents the maximum estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine;
and the processing unit is used for responding to the acquired query request by utilizing one search engine in at least one search engine.
In the above scheme, the apparatus further comprises:
and the determining unit is used for determining the preset flow value of the corresponding search engine by utilizing the first parameter, the second parameter and the third parameter aiming at each search engine in the at least two search engines.
An embodiment of the present invention further provides a terminal, including: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to perform the steps of any of the above methods when running the computer program.
An embodiment of the present invention further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the above methods.
The search engine processing method, the search engine processing device, the search engine processing terminal and the search engine processing storage medium provided by the embodiment of the invention are used for acquiring a query request; screening out a search engine of which the corresponding flow rate does not reach a preset flow rate value from at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by utilizing the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine; the first parameter represents the maximum estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine; and responding the acquired query request by utilizing one search engine in at least one search engine, and selecting the search engine with the flow rate not reaching the preset flow rate value when distributing the flow rate for the search engine, so that the flow rate conservation is considered, and the preset flow rate value is related to the maximum estimated income of the corresponding search engine, so that the distribution income of the flow rate is maximized, and the distribution of the flow rate is more reasonable.
Drawings
FIG. 1 is a schematic diagram of a real-time bidding mechanism;
FIG. 2 is a flow chart of a method of processing by a search engine according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a scenario of an embodiment of the present invention;
FIG. 4 is a schematic diagram of another scenario of an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an intelligent shunting device in an embodiment of the present invention;
FIG. 6 is a diagram illustrating a search engine processing apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
It should be noted that: in the present examples, "first", "second", etc. are used for distinguishing similar objects and are not necessarily used for describing a particular order or sequence.
In addition, the technical solutions described in the embodiments of the present invention may be arbitrarily combined without conflict.
In the present examples, a plurality means at least two, e.g., two, three, etc., unless specifically limited otherwise.
For the traffic distribution of the search engine (which can be understood as the distribution of the query request), it is mature to adopt the Real Time Bidding (RTB) mechanism to implement. The RTB mechanism is generally applied to an advertisement mode, that is, to a network advertisement delivery scenario, as shown in fig. 1, the specific implementation of the RTB mechanism is: the RTB engine sends a bid request for a query request of a user, a downstream Platform (DSP) for a Demand-Side Platform (generally, a Platform Side of an advertiser) bids the query request (which can be understood as a real-time bid through the downstream DSP), and the RTB engine selects the DSP with a high bid to obtain a complete bid for the traffic (i.e., the query request). For the implementation mode, the real-time performance is strong, and the maximum benefit of the current PV (the sum of the number of the browsed pages in a statistical period can be understood as a query request) is considered, and the overall maximum benefit is obtained through some greedy algorithms. However, this implementation does not consider the quantum mode (i.e. each DSP must have a certain amount of traffic), and then, in case of a small number of DSPs, it may cause an oligopolistic effect, thereby harming the other DSPs.
In addition, in some related technologies, although the guarantee mode is considered, a programmed guarantee mode is mainly performed from the perspective of media in implementation, that is, the guarantee mode is single or random, and a competition relationship of downstream ecpms (generally, in the advertisement field, advertisement revenue that can be obtained every thousand impressions, and here, a value brought by thousands of PV impressions brought to search engine drainage is not fully considered), that is, a competition relationship of values corresponding to each downstream search engine is not considered.
Based on this, in various embodiments of the invention, the distribution yield maximization of the whole flow is satisfied, and the flow guarantee of each search engine is considered.
An embodiment of the present invention provides a search engine processing method, which is applied to a terminal, and as shown in fig. 2, the method includes:
step 201: acquiring a query request;
step 202: screening out a search engine of which the corresponding flow rate does not reach a preset flow rate value from at least two search engines to obtain at least one search engine;
here, the preset flow value corresponding to each search engine is determined by using the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine.
The first parameter represents the pre-estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine;
step 203: the obtained query request is responded to by one of the at least one search engine.
In practical application, the terminal may be a mobile terminal, such as a mobile phone, a tablet computer (pad), and the like.
In step 201, the query obtaining request includes: and acquiring a query request corresponding to the keyword input by the user, and inputting the keyword through a search interface displayed on a display screen of the terminal by the user during actual application. The keywords input by the user are used for inquiring data required by the user.
In step 202, the preset flow value of the corresponding search engine may be determined using the following formula:
DPV＝C·Vmax+Δ(1)
where C represents the second parameter, Vmax represents the first parameter, and Δ represents the third parameter.
In practical application, the first parameter can be understood as the maximum estimated value brought by the corresponding search engine; the second parameter can be understood as a flow constant balance coefficient; the third parameter may be understood as a guarantee policy value. The preset flow value is expressed in terms of the PV number of the divided flows.
In practice, in step 202, the at least two search engines are different types of search engines, for example, the type of search engine may be a search engine with value in eCPM, a search engine with value in click through rate (ctr) (pay for effect), a search engine with value in PV, or the like.
Before step 202 is performed, a preset flow value needs to be determined for each search engine.
Based on this, in an embodiment, the method may further include:
and aiming at each search engine in the at least two search engines, determining a preset flow value of the corresponding search engine by using the first parameter, the second parameter and the third parameter.
Of course, first, the first parameter, the second parameter, and the third parameter corresponding to the corresponding search engine need to be determined.
Based on this, in an embodiment, historical exposure, user click behavior and query requests corresponding to the corresponding search engine may be obtained, and these parameters are input into the established revenue model, so as to obtain the first parameter. The embodiment of the invention does not limit the process of establishing the profit model.
In actual application, the exposure, the user click behavior and the query request corresponding to the corresponding search engine can be obtained in real time or periodically, and the parameters are input into the established revenue model, so that the corresponding first parameters can be updated in real time or periodically, and the first parameters can reflect the actual situation.
In an embodiment, the corresponding second parameter is determined by using historical revenue related information brought by the corresponding search engine and combining historical total traffic related information of the corresponding search engine.
Wherein the historical revenue-related information may include: first information; the first information characterizes an average profit for N days.
The historical total traffic related information may include second information; the second information characterizes an average total flow over N days.
The value of N may be determined as needed, and may be dynamically adjusted in the process of implementing the scheme of the embodiment of the present invention, for example, the value of N may be dynamically updated through an established model.
Specifically, the second information is divided by the first information to obtain a second parameter.
Here, in actual use, the unit used for the statistical benefit may be different for different types of search engines, and therefore, when determining the average benefit of N, the unit conversion needs to be performed such that all the search engines use the same benefit unit.
In actual application, the updated historical income related information brought by the corresponding search engine can be utilized in real time or periodically, and the updated historical total flow related information is combined to re-determine the corresponding second parameter, so that the second parameter can reflect the actual situation.
In an embodiment, the corresponding third parameter is determined using the intent recognition result of the historical query request.
Here, the intent of the query request means: the query requests the intent of the corresponding user.
In practical application, the result may be identified according to the query request intention of each day, and the corresponding third parameter may be determined in combination with the established prediction model. The embodiment of the invention does not limit the process of establishing the prediction model.
When performing the intention identification, the intention identification needs to be performed in combination with the context corresponding to the query request.
Context may be understood as some information associated with a query request, such as: the geographic location of the user, the time of the query request, etc.
In actual application, the updated historical query request can be acquired in real time or periodically, intention identification is performed, and the corresponding third parameter is re-determined by using the intention identification result, so that the third parameter can reflect the actual situation.
In step 203, a search engine of the at least one search engine is used to respond to the obtained query request, obtain a query result, and output the query result.
And when the mobile terminal is actually applied, presenting the query result on the mobile terminal, and more specifically, displaying the query result on a display screen of the mobile terminal.
In practice, in step 202, when at least two search engines are screened out, that is, when multiple search engines hit simultaneously, a polling mechanism may be used for allocation.
Based on this, in an embodiment, the responding to the obtained query request by using one of the at least one search engine includes:
obtaining the bid of each search engine in the screened at least two search engines to the obtained query request;
determining a search engine from the screened at least two search engines by using bidding results and combining a polling mechanism;
and responding to the acquired query request by utilizing the determined search engine.
For example, suppose that three search engines, namely engine a, engine B and engine C, are screened out for the query request of this time, and the bid price of engine a is the highest, so that engine a can be used to respond to the query request of this time; the three search engines of the engine A, the engine B and the engine C are screened out similarly for the next query request, although the bidding price of the engine A is the highest, the polling mode is adopted at the moment, so the engine B is used for responding to the next query request, and the three search engines of the engine A, the engine B and the engine C are screened out similarly for the next query request, although the bidding price of the engine A is the highest, the polling mode is adopted at the moment, so the engine C is used for responding to the next query request.
Here, it should be noted that: the polling manner may be for each query request, as in the foregoing example, or for a plurality of query requests, for example, suppose that three search engines, namely engine a, engine B, and engine C, are screened out for the query request of this time, and the bid price of engine a is the highest, so that engine a may be used to respond to the query request of this time; the three search engines of engine a, engine B and engine C are similarly screened out for the next query request, and the bid price of engine a is the highest, so that engine a is used to respond to the next query request, and the three search engines of engine a, engine B and engine C are similarly screened out for the next query request, and although the bid price of engine a is the highest, the polling method is adopted at this time, so that engine B or engine C is used to respond to the next query request.
Of course, in practical applications, the polling mode may be other modes, and the embodiment of the present invention does not limit this mode.
According to the scheme provided by the embodiment of the invention, a query request is obtained; screening out a search engine of which the corresponding flow rate does not reach a preset flow rate value from at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by utilizing the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine; the first parameter represents the maximum estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine; and responding the acquired query request by utilizing one search engine in at least one search engine, and selecting the search engine with the flow rate not reaching the preset flow rate value when distributing the flow rate for the search engine, so that the flow rate conservation is considered, and the preset flow rate value is related to the maximum estimated income of the corresponding search engine, so that the distribution income of the flow rate is maximized, and the distribution of the flow rate is more reasonable.
In addition, the first parameter, the second parameter and the third parameter are updated in real time or periodically, so that the preset flow value can reflect the actual situation more accurately, and the flow distribution can be more reasonable.
The present invention will be described in further detail with reference to the following application examples.
In the embodiment of the application, the application scene is the browser search through.
Assuming that the user inputs "xxxx", when the store search engine and the advertisement search engine are accessed, a presentation result (which may be referred to as a sug result (which may be understood as an associativity result)) is shown in fig. 3. At this time, as shown in fig. 3, if the user clicks a search button or a keypad button, a general search page may be entered, as shown in fig. 4. It should be noted that: the content of the search page (which may also be referred to as stories) is related to the user's settings.
When the direct search result cannot meet the requirements of the user, the user can directly click the search button to enter a large search page.
In this case, if the direct search results are more and accurate, the traffic entering the search engine of the big search may be intercepted; if the direct search results are few, it is also a loss for other search engines. Therefore, in the embodiment of the application, the flow distribution among the search engines is mainly adjusted.
As shown in fig. 5, in the present embodiment, there are search engines (search engine 1 and search engine 2) for different ecpms, search engines (search engine 3) for PV premium, and search engines (search engine 4) for pay-per-effect (ctr) in the downstream.
When a user needs to retrieve, sending a query request to the intelligent distribution module, and distributing the query request of the user to a corresponding search engine by the intelligent distribution module to execute search; meanwhile, the intelligent distribution module generates a request log according to the query request of the user and stores the request log to a data log system; the query result can generate behaviors such as exposure, click and the like, and the click collector can collect the behaviors and store the behaviors in the data log system; the cost accounting module determines the maximum pre-estimated profit (which can also be understood as the maximum pre-estimated value) corresponding to each search engine by using the data stored in the data log system, and sends the maximum pre-estimated profit to the intelligent distribution module so as to determine the preset flow value corresponding to each search engine, thereby distributing the corresponding search engine to the query request.
The cost accounting module can periodically utilize data acquired from the data log system and combine with the profit model to determine the maximum predicted profit corresponding to each search engine.
The intelligent shunting module determines a preset flow value corresponding to each search engine by adopting a formula (1).
After receiving the query request of the user, the intelligent flow distribution module screens the search engines with the flow rate not reaching the preset flow rate value from all the search engines, and the screened search engines are utilized to respond to the acquired query request, namely the query request of the user is distributed to the search engines with the flow rate not reaching the preset flow rate value.
When a plurality of search engines hit at the same time, namely a plurality of search engines with flow not reaching the preset flow value are available, distribution is carried out according to a polling strategy.
Wherein, for the parameters of the process, real-time data updating can be carried out, and feedback can be made in real time. That is, after the formula parameters are configured in the previous period, the parameters in the formula are automatically adjusted by using the closed loop of the whole device. That is, the rebalance shunting strategy is an intelligent shunting process through the feedback of the benefits brought by shunting.
It can be seen from the above description that, by adopting the scheme of the embodiment of the present invention, traffic distribution can be intelligently and reasonably scheduled to different types of search engines, and distribution yield of the whole traffic is maximized, so that not only the maximum value of the traffic is satisfied, but also a policy for traffic conservation can be considered (a policy for traffic conservation is considered), and the occurrence of traffic shortness damage is avoided.
In order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a search engine processing apparatus, which is disposed on a terminal, and as shown in fig. 6, the apparatus includes:
an obtaining unit 61, configured to obtain a query request;
the screening unit 62 is configured to screen a search engine, of which the flow rate corresponding to the corresponding search engine does not reach the preset flow rate value, from the at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by utilizing the first parameter, the second parameter and the third parameter corresponding to the corresponding search engine; the first parameter represents the estimated income brought by the corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine;
a processing unit 63, configured to respond to the obtained query request with one of the at least one search engine.
In practical application, the preset flow value of each search engine needs to be determined.
Based on this, in an embodiment, the apparatus may further include:
and the determining unit is used for determining the preset flow value of the corresponding search engine by utilizing the first parameter, the second parameter and the third parameter aiming at each search engine in the at least two search engines.
Wherein the determining unit is further configured to:
for each of the at least two search engines, determining a corresponding first parameter, second parameter, and third parameter.
In an embodiment, the determining unit determines the corresponding second parameter by using historical revenue related information brought by a corresponding search engine in combination with historical total traffic related information of the corresponding search engine.
In an embodiment, the determining unit determines the corresponding third parameter using an intention recognition result of the historical query request.
When at least two search engines are screened out, i.e., multiple search engines hit simultaneously, a polling mechanism may be employed for assignment.
Based on this, in an embodiment, the processing unit 63 is specifically configured to:
obtaining the bid of each search engine in the screened at least two search engines to the obtained query request;
determining a search engine from the screened at least two search engines by using bidding results and combining a polling mechanism;
and responding to the acquired query request by utilizing the determined search engine.
In practical applications, the obtaining unit 61, the filtering unit 62, the processing unit 63 and the determining unit may be implemented by a processor in a search engine processing device. Of course, the processor needs to run the program of the memory to realize the functions of the above-mentioned program modules.
It should be noted that: in the search engine processing apparatus provided in the above embodiment, when performing the search engine processing, only the division of each program module is exemplified, and in practical applications, the processing may be distributed to different program modules according to needs, that is, the internal structure of the apparatus may be divided into different program modules to complete all or part of the processing described above. In addition, the search engine processing apparatus and the search engine processing method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
Based on the hardware implementation of the program module, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides a terminal. Fig. 7 is a schematic diagram of a hardware composition structure of a terminal according to an embodiment of the present invention, and as shown in fig. 7, the terminal 70 includes:
a communication interface 71 capable of information interaction with other devices such as network devices and the like;
and the processor 72 is connected with the communication interface 71 to realize information interaction with other equipment, and is used for executing the method provided by one or more technical schemes of the terminal side when running a computer program. And the computer program is stored on the memory 73.
Of course, in practice, the various components of the terminal 70 are coupled together by a bus system 74. It will be appreciated that the bus system 74 is used to enable communications among the components of the connection. The bus system 74 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 74 in fig. 7.
The memory 73 in the embodiment of the present invention is used to store various types of data to support the operation of the terminal 70. Examples of such data include: any computer program for operating on the terminal 70.
It will be appreciated that the memory 73 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 52 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
The method disclosed in the above embodiments of the present invention may be applied to the processor 72, or may be implemented by the processor 72. The processor 72 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by instructions in the form of hardware, integrated logic circuits, or software in the processor 72. The processor 72 described above may be a general purpose processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 72 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium located in the memory 73, and the processor 72 reads the program in the memory 73 and performs the steps of the aforementioned method in conjunction with its hardware.
Optionally, when the processor 72 executes the program, the corresponding process implemented by the terminal in each method according to the embodiment of the present invention is implemented, and for brevity, no further description is given here.
In an exemplary embodiment, the present invention further provides a storage medium, i.e. a computer storage medium, in particular a computer readable storage medium, for example comprising a memory 73 storing a computer program, which is executable by a processor 72 of the terminal to perform the steps of the aforementioned method. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, terminal and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (8)

1. A search engine processing method, comprising:
acquiring a query request;
screening out a search engine of which the corresponding flow rate does not reach a preset flow rate value from at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by adding the product of the first parameter and the second parameter corresponding to the corresponding search engine and the third parameter; the first parameter represents the maximum estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine;
the obtained query request is responded to by one of the at least one search engine.
2. The method of claim 1, further comprising:
for each of the at least two search engines, determining a corresponding first parameter, second parameter, and third parameter.
3. The method of claim 2, wherein determining the corresponding second parameter comprises:
and determining the corresponding second parameter by utilizing the historical income related information brought by the corresponding search engine and combining the historical total flow related information of the corresponding search engine.
4. The method of claim 2, wherein determining the corresponding third parameter comprises:
and identifying a result by using the intention of the historical query request, and determining the corresponding third parameter.
5. The method of claim 1, wherein at least two search engines are screened; the responding to the obtained query request by using one search engine in at least one search engine comprises:
obtaining the bid of each search engine in the screened at least two search engines to the obtained query request;
determining a search engine from the screened at least two search engines by using bidding results and combining a polling mechanism;
and responding to the acquired query request by utilizing the determined search engine.
6. A search engine processing apparatus, comprising:
an acquisition unit configured to acquire an inquiry request;
the screening unit is used for screening the search engines of which the corresponding flow rate does not reach the preset flow rate value from the at least two search engines to obtain at least one search engine; the preset flow value corresponding to each search engine is determined by adding the product of the first parameter and the second parameter corresponding to the corresponding search engine and the third parameter; the first parameter represents the maximum estimated income brought by a corresponding search engine; the second parameter represents a first weight coefficient of the predicted income brought by the corresponding search engine; the third parameter represents a second weight coefficient of the predicted income brought by the corresponding search engine;
and the processing unit is used for responding to the acquired query request by utilizing one search engine in at least one search engine.
7. A terminal, comprising: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is adapted to perform the steps of the method of any one of claims 1 to 5 when running the computer program.
8. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, implementing the steps of the method of any one of claims 1 to 5.
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Families Citing this family (1)

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Publication number Priority date Publication date Assignee Title
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Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1677394A (en) * 2004-03-22 2005-10-05 微软公司 System and method for optimizing paid listing yield
CN102043834A (en) * 2010-11-25 2011-05-04 北京搜狗科技发展有限公司 Method for realizing searching by utilizing client and search client
CN103246985A (en) * 2013-04-26 2013-08-14 北京亿赞普网络技术有限公司 Advertisement click rate predicting method and device
CN104992348A (en) * 2015-06-24 2015-10-21 深圳市腾讯计算机系统有限公司 Method and device for displaying information
CN105095210A (en) * 2014-04-22 2015-11-25 阿里巴巴集团控股有限公司 Method and apparatus for screening promotional keywords
CN105431875A (en) * 2013-08-05 2016-03-23 雅虎公司 Search engine marketing optimizer
CN105678593A (en) * 2016-03-11 2016-06-15 合一网络技术(北京)有限公司 Video advertisement combined bidding and serving method and system
CN105912680A (en) * 2016-04-12 2016-08-31 广东欧珀移动通信有限公司 Search-related recommendation method, device and mobile terminal
CN105988836A (en) * 2015-02-12 2016-10-05 广东欧珀移动通信有限公司 Application recommendation method and device
CN106296287A (en) * 2016-08-09 2017-01-04 百度在线网络技术（北京）有限公司 For the method and apparatus adjusting the bid of promotion message
CN106682937A (en) * 2016-12-16 2017-05-17 合网络技术（北京）有限公司 Advertisement traffic pricing method and advertisement traffic pricing device in online real-time bidding environment
CN106844615A (en) * 2017-01-18 2017-06-13 广州优视网络科技有限公司 A kind of method and apparatus for recommending to increase exposure coverage rate while application
CN107122990A (en) * 2017-03-22 2017-09-01 广州优视网络科技有限公司 Using recommendation method, client, server and system
CN109145183A (en) * 2017-06-19 2019-01-04 腾讯科技（深圳）有限公司 Push the method and device of information to be presented
CN109360017A (en) * 2018-09-11 2019-02-19 广东神马搜索科技有限公司 The method and apparatus for determining advertisement reserve price for query statement

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9853876B1 (en) * 2014-06-13 2017-12-26 Narus, Inc. Mobile application identification in network traffic via a search engine approach

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1677394A (en) * 2004-03-22 2005-10-05 微软公司 System and method for optimizing paid listing yield
CN102043834A (en) * 2010-11-25 2011-05-04 北京搜狗科技发展有限公司 Method for realizing searching by utilizing client and search client
CN103246985A (en) * 2013-04-26 2013-08-14 北京亿赞普网络技术有限公司 Advertisement click rate predicting method and device
CN105431875A (en) * 2013-08-05 2016-03-23 雅虎公司 Search engine marketing optimizer
CN105095210A (en) * 2014-04-22 2015-11-25 阿里巴巴集团控股有限公司 Method and apparatus for screening promotional keywords
CN105988836A (en) * 2015-02-12 2016-10-05 广东欧珀移动通信有限公司 Application recommendation method and device
CN104992348A (en) * 2015-06-24 2015-10-21 深圳市腾讯计算机系统有限公司 Method and device for displaying information
CN105678593A (en) * 2016-03-11 2016-06-15 合一网络技术(北京)有限公司 Video advertisement combined bidding and serving method and system
CN105912680A (en) * 2016-04-12 2016-08-31 广东欧珀移动通信有限公司 Search-related recommendation method, device and mobile terminal
CN106296287A (en) * 2016-08-09 2017-01-04 百度在线网络技术（北京）有限公司 For the method and apparatus adjusting the bid of promotion message
CN106682937A (en) * 2016-12-16 2017-05-17 合网络技术（北京）有限公司 Advertisement traffic pricing method and advertisement traffic pricing device in online real-time bidding environment
CN106844615A (en) * 2017-01-18 2017-06-13 广州优视网络科技有限公司 A kind of method and apparatus for recommending to increase exposure coverage rate while application
CN107122990A (en) * 2017-03-22 2017-09-01 广州优视网络科技有限公司 Using recommendation method, client, server and system
CN109145183A (en) * 2017-06-19 2019-01-04 腾讯科技（深圳）有限公司 Push the method and device of information to be presented
CN109360017A (en) * 2018-09-11 2019-02-19 广东神马搜索科技有限公司 The method and apparatus for determining advertisement reserve price for query statement

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
User behavior analysis and commodity recommendation for point-earning apps;Y. Chen;《2016 Conference on Technologies and Applications of Artificial Intelligence》;20161127;全文 *
基于机器学习的移动应用推荐系统的研究与实现;符永顺;《信息科技辑》;20150115(第1期);全文 *

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