WO2015096742A1 - Procédé, dispositif et système de traitement d'informations - Google Patents

Procédé, dispositif et système de traitement d'informations Download PDF

Info

Publication number
WO2015096742A1
WO2015096742A1 PCT/CN2014/094830 CN2014094830W WO2015096742A1 WO 2015096742 A1 WO2015096742 A1 WO 2015096742A1 CN 2014094830 W CN2014094830 W CN 2014094830W WO 2015096742 A1 WO2015096742 A1 WO 2015096742A1
Authority
WO
WIPO (PCT)
Prior art keywords
candidate information
information
click rate
candidate
pushed
Prior art date
Application number
PCT/CN2014/094830
Other languages
English (en)
Chinese (zh)
Inventor
习明昊
Original Assignee
腾讯科技（深圳）有限公司
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 腾讯科技（深圳）有限公司 filed Critical 腾讯科技（深圳）有限公司
Publication of WO2015096742A1 publication Critical patent/WO2015096742A1/fr

Links

Images

Classifications

    • 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates to Internet technologies, and in particular, to an information processing method, apparatus, and system.
  • the server set on the network side selects relevant candidate information from the candidate information and pushes it to the client at a specific time according to the feature information of the client user, such as gender, hobbies, geography, etc., and the client displays the location.
  • the selected alternative information is provided for the user's reference.
  • the server needs to calculate a large amount of computational information from the candidate information, which results in a long selection time and cannot meet the actual demand. If the selection time is shortened, the server needs to be upgraded, resulting in an increase in cost.
  • an embodiment of the present invention provides an information processing method, apparatus, and system.
  • the present invention provides an information processing method, the method comprising: receiving a plurality of candidate information, the plurality of candidate information being sent by one or more delivery terminals; and the server calculating offline according to the estimated click rate of the candidate information The weight of each candidate information is calculated.
  • the offline calculation refers to the calculation performed by the client where the user is located in an offline state; and the candidate letter whose weight meets the preset first rule is filtered out.
  • the pre-selected reference list includes at least two candidate information; acquiring feature information of the user who operates the client in an online state; and according to the feature information, Selecting candidate information that satisfies the preset second rule is selected in the pre-selected reference list; and the candidate information that satisfies the preset second rule in the pre-selected reference list is pushed to the client.
  • the weight of each candidate information is calculated offline: the weight of each candidate information is calculated offline according to the estimated click rate of the candidate information and the price information of the candidate information.
  • the method before the calculating the weight of each candidate information offline, the method further includes: calculating an estimated click rate of each candidate information.
  • the calculating an estimated click rate of each candidate information includes: when the candidate information is not pushed within a preset time, using an average click rate of the category to which the candidate information belongs The estimated clickthrough rate for this alternate information.
  • the calculating an estimated click rate of each candidate information includes: when the candidate information is pushed within a preset time, determining whether the number of times the candidate information is pushed is greater than or equal to a threshold value; when the pushed number of the candidate information is greater than or equal to the first threshold, the click rate of the candidate information in the preset time is used as the estimated click rate of the candidate information.
  • the calculating the estimated click rate of each candidate information further includes: when the pushed number of the candidate information is less than the first threshold, determining that the degree of association with the candidate information reaches the first Whether the number of times of pushing the first associated candidate information of a preset value is greater than or equal to the first threshold, and when the number of times of pushing the first associated candidate information is greater than or equal to the first threshold, the first associated candidate The click rate of the information within the preset time is used as the estimated click rate of the candidate information.
  • the calculating the estimated click rate of each candidate information further includes: when the pushed number of the first associated candidate information is less than the first threshold, determining the candidate letter Whether the number of times of pushing the second association candidate information of the second preset value is greater than or equal to the first threshold, and when the number of times the second association candidate information is pushed is greater than or equal to the first threshold, The click rate of the second association candidate information in the preset time is used as the estimated click rate of the candidate information; when the pushed number of the second association candidate information is less than the first threshold, the preparation The average click rate of the category to which the information belongs is used as the estimated click rate.
  • the feature information includes at least one of the following information: gender information, hobby information, and region information of a user who operates the client.
  • the present invention also provides a server, the server comprising: one or more processors; and a storage device for storing instructions executed by the one or more processors to implement an information processing method: receiving a plurality of candidate information, which is sent by one or more delivery terminals; the server calculates the weight of each candidate information offline according to the estimated click rate of the candidate information, where the offline calculation refers to the user
  • the client is located in the offline state; the candidate information is filtered out to meet the preset first rule, and is recorded in a pre-selected reference list, where the pre-selected reference list includes at least two candidate information;
  • the client is in the online state, and operates the feature information of the user of the client; the candidate information that meets the preset second rule is selected from the pre-selected reference list according to the feature information; and the pre-selected reference list meets the preset
  • the alternative information of the second rule is pushed to the client.
  • the weight of each candidate information is calculated offline: the weight of each candidate information is calculated offline according to the estimated click rate of the candidate information and the price information of the candidate information.
  • the storage device of the server further includes an instruction executed by the one or more processors to implement the following steps: calculating each device before calculating the weight of each candidate information offline The estimated clickthrough rate of the selected information.
  • the calculating an estimated click rate of each candidate information includes: when the candidate information is not pushed within a preset time, an average point of a category to which the candidate information belongs The hit rate is used as the estimated click rate for this alternate information.
  • the calculating an estimated click rate of each candidate information includes: when the candidate information is pushed within a preset time, determining whether the number of times the candidate information is pushed is greater than or equal to a threshold value; when the pushed number of the candidate information is greater than or equal to the first threshold, the click rate of the candidate information in the preset time is used as the estimated click rate of the candidate information.
  • the calculating the estimated click rate of each candidate information further includes: when the pushed number of the candidate information is less than the first threshold, determining that the degree of association with the candidate information reaches the first Whether the number of times of pushing the first associated candidate information of a preset value is greater than or equal to the first threshold, and when the number of times of pushing the first associated candidate information is greater than or equal to the first threshold, the first associated candidate The click rate of the information within the preset time is used as the estimated click rate of the candidate information.
  • the calculating the estimated click rate of each candidate information further includes: determining, when the pushed number of the first associated candidate information is less than the first threshold, determining the association with the candidate information Whether the number of times of pushing the second association candidate information of the second preset value is greater than or equal to the first threshold, and when the number of times of pushing the second association candidate information is greater than or equal to the first threshold, the second Correlating the click rate of the candidate information in the preset time as the estimated click rate of the candidate information; when the pushed number of the second associated candidate information is less than the first threshold, the candidate information belongs to The average clickthrough rate for the category is used as the estimated clickthrough rate.
  • the feature information includes at least one of the following information: gender information, hobby information, and region information of a user who operates the client.
  • the present invention also provides a computer storage medium for information processing having stored thereon a set of instructions executed by one or more processors to perform the steps of: receiving a plurality of candidate information, the plurality of The candidate information is sent by one or more delivery terminals; the server calculates the weight of each candidate information offline according to the estimated click rate of the candidate information, where the offline calculation refers to the execution of the client where the client is offline.
  • the candidate information is recorded in a pre-selected reference list, where the pre-selected reference list includes at least two candidate information; acquiring feature information of the user who operates the client in the online state; according to the feature And selecting, from the pre-selected reference list, candidate information that meets the preset second rule; and pushing the candidate information that meets the preset second rule in the pre-selected reference list to the client.
  • the weight of each candidate information is calculated offline: the weight of each candidate information is calculated offline according to the estimated click rate of the candidate information and the price information of the candidate information.
  • the method before the calculating the weight of each candidate information offline, the method further includes: calculating an estimated click rate of each candidate information.
  • the calculating an estimated click rate of each candidate information includes: when the candidate information is pushed within a preset time, determining whether the number of times the candidate information is pushed is greater than or equal to a threshold value; when the pushed number of the candidate information is greater than or equal to the first threshold, the click rate of the candidate information in the preset time is used as the estimated click rate of the candidate information.
  • the candidate information is recorded in a pre-selected reference list, and when the user is online, the candidate information that meets the preset second rule is selected from the pre-selected reference list.
  • the user pushed to the client, because the amount of data of the candidate information in the pre-selected reference list is small, the amount of computation required by the server to select the relevant candidate information from the pre-selected reference list is greatly reduced, so that the server can be upgraded without upgrading. In case, reduce the time for selecting alternative information.
  • FIG. 1 is a schematic flow chart of a first embodiment of an information processing method according to the present invention.
  • FIG. 2 is a schematic flowchart of a second embodiment of an information processing method according to the present invention.
  • FIG. 3 is a schematic flowchart diagram of a third embodiment of an information processing method according to the present invention.
  • FIG. 4 is a schematic flowchart of an embodiment of pre-selection of candidate information in an information processing method according to the present invention.
  • FIG. 5 is a schematic structural diagram of an embodiment of a server provided by the present invention.
  • FIG. 6 is a schematic structural diagram of an embodiment of a client provided by the present invention.
  • FIG. 7 is a schematic structural diagram of an embodiment of an information processing system according to the present invention.
  • electronic devices such as web servers
  • web servers set on the network side select the most appropriate and most click-oriented tendency according to the online user's characteristic information (such as gender, hobbies, regions, etc.).
  • the small data is played to the user.
  • the prior art solution selects the most suitable and most click-oriented small data from the huge amount of alternative small data according to the characteristic information of the online user when the exposure occurs, and the calculation amount is huge.
  • a first embodiment of an information processing method provided by the present invention is applied to a server. As shown in FIG. 1, the method includes:
  • Step 101 Receive a plurality of candidate information, where the multiple candidate information is sent by one or more delivery terminals.
  • the candidate information may be an advertisement order placed by an advertiser.
  • CPC's advertising order has the characteristics of small budget and many data, and the same advertisement is displayed. There will be tens of thousands or even hundreds of thousands of orders bidding.
  • Step 102 The server calculates the weight of each candidate information offline according to the estimated click rate of the candidate information, where the offline calculation refers to the calculation performed by the server that the user is located in the offline state.
  • the weight of the alternative information refers to the degree to which the candidate information is filtered out to be pushed to the client in all the candidate information, and may be related to the historical click rate, price, and the like of the candidate information.
  • the higher the estimated click rate of the candidate information the higher the weight of the candidate information.
  • the higher the estimated click rate of the candidate information and the higher the price information of the candidate information the higher the weight of the candidate information.
  • Step 103 Filter out the candidate information that the weight meets the preset first rule, and record the information in a pre-selected reference list, where the pre-selected reference list includes at least two candidate information.
  • the weights may be sorted according to the weight of the weights, and a group of orders with the highest weight is selected as a pre-selected order, for example, an order of 1000 orders is selected from 10,000 orders.
  • Step 104 Acquire feature information of a user who operates the client in an online state.
  • the feature information of the user may include information such as a history of the user, and may also include classification of the gender, hobbies, regions, and the like of the user according to information such as the history of the user.
  • Step 105 Select, according to the feature information, candidate information that meets a preset second rule from the pre-selected reference list.
  • one or more orders with a large click preference may be selected from the pre-selected orders according to the feature information such as gender, hobbies, and regions, and pushed to the client, so that The client displays the received order, which can be clicked to play.
  • the second rule may be that the degree of fitting of the user preference with the candidate information in the pre-selected reference list is greater than a predetermined value, and therefore, the degree of fitting from the pre-selected reference list to the user's preference may be greater than the user feature.
  • Alternative information for the predetermined value may be used to be greater than the user feature.
  • Step 106 Push the candidate information in the pre-selected reference list that meets the preset second rule to the client.
  • the candidate information that the weights satisfy the preset first rule is recorded in a pre-selected reference list, and when the user is online, the device that meets the preset second rule is selected from the pre-selected reference list.
  • the information is selected and pushed. Since the amount of data of the candidate information in the pre-selected reference list is small, the amount of computation required by the server to select the relevant candidate information from the pre-selected reference list is greatly reduced, so that the server can be upgraded without upgrading the server. , reduce the time for selecting alternative information.
  • a second embodiment of an information processing method provided by the present invention is applied to a server. As shown in FIG. 2, the method includes:
  • Step 201 Receive multiple candidate information, where the multiple candidate information is sent by one or more delivery terminals.
  • Step 202 Calculate an estimated click rate of each candidate information.
  • a click rate can be estimated based on the size and the ad slot of the industry and the order to which the order belongs.
  • Step 203 Calculate the weight of each candidate information offline according to the estimated click rate of the candidate information, where the offline calculation refers to the calculation performed by the client where the user is located in an offline state.
  • the weight of each candidate information may also be calculated offline according to the estimated click rate and price information of each candidate information.
  • the price information may be an order bid of the CPC.
  • Step 204 Filter out the candidate information that the weight meets the preset first rule, and record in a pre- In the selected reference list, the pre-selected reference list includes at least two alternative information.
  • the weights can be sorted according to the weight of the weights, and a group of orders with the highest weight is selected, for example, the orders of the top 1000 are selected from 10,000 orders.
  • Step 205 Acquire feature information of a user who operates the client in an online state.
  • Step 206 Select, according to the feature information, candidate information that meets a preset second rule from the pre-selected reference list.
  • Step 207 Push the candidate information that meets the preset second rule in the pre-selected reference list to the client.
  • the weight of each candidate information can be calculated more accurately offline, thereby facilitating more accurate selection of relevant candidate information.
  • a third embodiment of an information processing method provided by the present invention is applied to a server. As shown in FIG. 3, the method includes:
  • Step 301 Receive multiple candidate information, where the multiple candidate information is sent by one or more delivery terminals.
  • Step 302 Determine whether the candidate information is pushed within a preset time. When the candidate information is pushed, the process proceeds to step 303; when the candidate information is not pushed, the process proceeds to step 309.
  • Step 303 Determine whether the number of times the candidate information is pushed is greater than or equal to the first threshold. When the number of times the candidate information is pushed is greater than or equal to the first threshold, proceed to step 304; when the candidate information is pushed When the number of times is less than the first threshold, the process proceeds to step 305.
  • the first threshold may be a sampling threshold
  • the sampling threshold 1/advertising average click rate* sampling coefficient
  • the average advertising click rate may be obtained by a server through statistics
  • the sampling The coefficient is a preset value, for example, a value of 5 or 10.
  • the ad clickthrough rate is the percentage of ads that are clicked in the display ad.
  • Step 304 The click rate of the candidate information in the preset time is used as the estimated click rate, and the process proceeds to step 310.
  • the preset time may be set according to actual conditions, for example, may be 7 days or 10 days, and the like.
  • Step 305 Determine whether the number of times of pushing the first association candidate information that is related to the candidate information reaches the first preset value is greater than or equal to the first threshold, when the number of times of the first association candidate information is pushed.
  • the process proceeds to step 306; when the number of times the first association candidate information is pushed is less than the first threshold, the process proceeds to step 307.
  • the first association candidate information whose degree of association with the candidate information reaches the first preset value may be a similar order of the same advertiser.
  • Step 306 The click rate of the first association candidate information in the preset time is used as the estimated click rate, and the process proceeds to step 310.
  • Step 307 Determine whether the pushed number of times of the second association candidate information that is related to the candidate information reaches the second preset value is greater than or equal to the first threshold, and when the second association candidate information is pushed.
  • the threshold is greater than or equal to the first threshold
  • the process proceeds to step 308; when the number of times the second association candidate information is pushed is less than the first threshold, the process proceeds to step 309.
  • the second association candidate information whose degree of association with the candidate information reaches the second preset value may be a similar order of the industry in which the advertiser is located.
  • Step 308 The click rate of the second associated candidate information in the preset time is used as the estimated click rate, and the process proceeds to step 310.
  • Step 309 The average click rate of the category to which the candidate information belongs is used as the estimated click rate.
  • the average click rate of the category to which the candidate information belongs may be the playback form of the order or the average click rate of the advertisement slot.
  • Step 310 Calculate each candidate offline according to the click rate and price information of each candidate information. The weight of the information.
  • Step 311 Filter out the candidate information that the weight meets the preset first rule, and record it in a pre-selected reference list, where the pre-selected reference list includes at least two candidate information.
  • Step 312 Acquire feature information of a user who operates the client in an online state.
  • Step 313 Select, according to the feature information, candidate information that meets a preset second rule from the pre-selected reference list.
  • Step 314 Push the candidate information that meets the preset second rule in the pre-selected reference list to the client.
  • the candidate information is pushed in the preset time, whether the number of times the candidate information is pushed is greater than or equal to the first threshold, and the degree of association with the candidate information reaches the first preset.
  • the number of times of pushing the first associated candidate information of the value is greater than or equal to the first threshold, and whether the number of times of pushing the second associated candidate information that has reached the second preset value with the candidate information is greater than or equal to the first Threshold and other information to estimate the click-through rate, you can get a more accurate estimated click-through rate, which is more conducive to more accurate selection of pre-selection related alternative information.
  • the flow of the candidate information pre-selection in an information processing method of the present invention will be described below by taking a specific application scenario as an example.
  • the alternative information in this embodiment is an advertisement order placed by an advertiser (customer).
  • Step 401 Determine whether the broadcasted order has exposure within 7 days, and when it is, go to step 402; if not, go to step 408.
  • Step 402 Determine whether the number of exposures is greater than or equal to the sampling threshold. When the sampling threshold is greater than or equal to the sampling threshold, proceed to step 403. If the sampling threshold is less than the sampling threshold, proceed to step 404.
  • the sampling threshold 1 / advertising average click rate * sampling coefficient
  • the average advertising click rate can be obtained by the server through statistics
  • the sampling coefficient is a preset value, for example, 5 or 10 equal value.
  • the ad clickthrough rate is the percentage of ads that are clicked in the display ad.
  • Step 403 The 7-day historical click rate of the order is used as the estimated click rate, and the process proceeds to step 409.
  • Step 404 Determine whether the customer has a similar order within 7 days, the number of exposures is greater than or equal to the sampling threshold, and when not, go to step 405; if not, go to step 406.
  • step 405 the 7-day historical click rate of the customer similar order is used as the estimated click rate, and the process proceeds to step 409.
  • Step 406 Determine whether the customer's industry has a similar order, whether the number of exposures in the 7 days is greater than or equal to the sampling threshold, and when not, proceed to step 407; if not, proceed to step 408.
  • Step 407 The 7-day historical click rate of the similar order in the same industry is taken as the estimated click rate, and the process proceeds to step 409.
  • Step 408 The average click rate of the play form or the ad slot of the order is used as the estimated click rate.
  • Step 409 Calculate the weight of the order according to the order bid and the estimated click rate.
  • the orders can be sorted according to the calculated weights and entered or eliminated from the order pool to obtain a pre-selected order.
  • Another embodiment of the information processing method provided by the present invention is applied to a client, the method comprising: receiving push candidate information; and displaying the push candidate information.
  • the candidate information of the push is candidate information that is selected from the pre-selected reference list according to the feature information of the client user in the online state, and the selected second reference rule is recorded, and the filtered weight is recorded in the pre-selected reference list.
  • the method applied to the client corresponds to the above method applied to the server, and the specific details can be referred to the above method applied to the server.
  • the server includes:
  • One or more processors 501 and
  • the storage device 502 is configured to store an instruction executed by the one or more processors to implement:
  • the server calculates the weight of each candidate information offline according to the estimated click rate of the candidate information, where the offline calculation refers to the calculation performed by the client where the user is located in an offline state;
  • the candidate information in the pre-selected reference list that satisfies the preset second rule is pushed to the client.
  • the candidate information that the weights satisfy the preset first rule is recorded in a pre-selected reference list, and when the user is online, the device that meets the preset second rule is selected from the pre-selected reference list.
  • the information is selected and pushed. Since the amount of data of the candidate information in the pre-selected reference list is small, the amount of computation required by the server to select the relevant candidate information from the pre-selected reference list is greatly reduced, so that the server can be upgraded without upgrading the server. , reduce the time for selecting alternative information.
  • the weight of each candidate information is calculated offline: the weight of each candidate information is calculated offline according to the estimated click rate of the candidate information and the price information of the candidate information.
  • the storage device of the server further includes instructions executed by the one or more processors to implement the following steps:
  • the estimated click rate of each candidate information is calculated.
  • the calculating the estimated click rate of each candidate information comprises: determining an alternative Whether the information is pushed in a preset time, and when the candidate information is pushed, determining whether the number of times of pushing the candidate information is greater than or equal to a first threshold, when the number of times the candidate information is pushed is greater than or equal to a threshold value, the click rate of the candidate information in a preset time is used as an estimated click rate;
  • the average click rate of the category to which the candidate information belongs is used as the estimated click rate.
  • the calculating the estimated click rate of each candidate information comprises: when the pushed number of the candidate information is less than the first threshold, determining that the degree of association with the candidate information reaches the first pre- Whether the number of times the first associated candidate information is pushed is greater than or equal to the first threshold, and when the pushed number of the first associated candidate information is greater than or equal to the first threshold, the first associated candidate information is The click rate in the preset time is used as the estimated click rate.
  • the calculating the estimated click rate of each candidate information comprises: when the pushed number of the first associated candidate information is less than the first threshold, determining that the degree of association with the candidate information is reached Whether the number of times of the second association candidate information of the second preset value is greater than or equal to the first threshold, and when the number of times the second association candidate information is pushed is greater than or equal to the first threshold, the second association is prepared. Select the click rate of the information within the preset time as the estimated click rate;
  • the average click rate of the category to which the candidate information belongs is used as the estimated click rate.
  • the client includes:
  • the storage device 602 is configured to store an instruction executed by the one or more processors to implement:
  • the pushed candidate information is candidate information that is selected from the pre-selected reference list and meets the preset second rule according to the feature information of the client user in the online state, the pre-selected reference list.
  • the candidate information of the push is displayed.
  • An embodiment of an information processing system provided by the present invention includes a server 701 and a client 702.
  • the server 701 is configured to receive multiple candidate information, where the multiple candidate information is sent by one or more delivery terminals; and calculate the weight of each candidate information offline according to the estimated click rate of the candidate information.
  • the offline calculation refers to the calculation performed by the client where the user is located in an offline state; the candidate information whose weight meets the preset first rule is selected and recorded in a pre-selected reference list, and the pre-selected reference list includes at least two Optional information; acquiring feature information of the user who operates the client in the online state; selecting candidate information that meets the preset second rule from the pre-selected reference list according to the feature information; The candidate information in the pre-selected reference list that satisfies the preset second rule is pushed to the client.
  • the client 702 is configured to receive and display, by using the pre-selected reference list, candidate information that meets a preset second rule.
  • the candidate information whose weight is determined to meet the preset first rule is recorded in a pre-selected reference list, for example, hundreds or one thousand orders, when the client is online. Then, the candidate information that satisfies the preset second rule is selected from the pre-selected reference list. Because the data amount of the candidate information in the pre-selected reference list is small, the server needs to select the relevant candidate information from the pre-selected reference list. The amount of computation is greatly reduced, so that the candidate information selection time can be reduced without upgrading the server (the upgrade server refers to increasing the number of servers in the server cluster or improving the performance of a single server), for example, the control selection time is in the order of milliseconds.
  • the order is weighted according to the industry and similar orders to which the order belongs, and a certain number of orders with the highest weight are selected. Can effectively save server overhead and solve The problem of an unlimited number of orders.
  • the device embodiments in the present application correspond to the method embodiments, and thus the device embodiments are not described in detail. For specific details, refer to the description of the corresponding method embodiments.
  • the present invention also provides a computer storage medium for information processing having stored thereon a set of instructions executed by one or more processors to perform the following steps:
  • the server calculates the weight of each candidate information offline according to the estimated click rate of the candidate information, where the offline calculation refers to the calculation performed by the client where the user is located in an offline state;
  • the candidate information in the pre-selected reference list that satisfies the preset second rule is pushed to the client.
  • the weight of each candidate information is calculated offline: the weight of each candidate information is calculated offline according to the estimated click rate of the candidate information and the price information of the candidate information.
  • the method before the calculating the weight of each candidate information offline, the method further includes: calculating an estimated click rate of each candidate information.
  • the calculating the estimated click rate of each candidate information comprises: when the candidate information is not pushed within a preset time, using an average click rate of the category to which the candidate information belongs Estimated clickthrough rate for alternate information.
  • the calculating the estimated click rate of each candidate information comprises: determining, when the candidate information is pushed within a preset time, whether the number of times the candidate information is pushed is greater than or equal to the first Threshold; when the number of times the candidate information is pushed is greater than or equal to the first threshold, The click rate of the candidate information in the preset time is used as the estimated click rate of the candidate information.
  • the calculating the estimated click rate of each candidate information further includes: when the pushed number of the candidate information is less than the first threshold, determining that the degree of association with the candidate information reaches the first Whether the number of times of the first association candidate information of the preset value is greater than or equal to the first threshold, and when the number of times the first association candidate information is pushed is greater than or equal to the first threshold, the first association candidate information is used.
  • the click rate in the preset time is used as the estimated click rate of the candidate information.
  • the calculating the estimated click rate of each candidate information further includes: determining the degree of association with the candidate information when the pushed number of the first associated candidate information is less than the first threshold Whether the number of times of the second association candidate information that reaches the second preset value is greater than or equal to the first threshold, and when the number of times the second association candidate information is pushed is greater than or equal to the first threshold, the second association is The click rate of the candidate information in the preset time is used as the estimated click rate of the candidate information; when the pushed number of the second associated candidate information is less than the first threshold, the category of the candidate information belongs to The average clickthrough rate is used as the estimated clickthrough rate.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

Landscapes

  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Information Transfer Between Computers (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé, un dispositif et un système de traitement d'informations, le procédé consistant : en la réception de multiples informations alternatives envoyées par au moins un terminal déployé ; en le calcul par un serveur hors-ligne du poids de chaque information alternative en fonction d'un taux de clic estimé de l'information alternative, le calcul hors-ligne faisant référence au calcul effectué quand un client utilisateur est dans un mode hors-ligne ; en le filtrage des informations alternatives au moyen d'un poids satisfaisant à une première règle prédéfinie, et en l'enregistrement dans une liste de référence présélectionnée comportant au moins deux informations alternatives ; en l'acquisition d'informations caractéristiques de l'utilisateur actionnant le client quand le client est dans un mode en ligne ; à sélectionner des informations alternatives satisfaisant à une seconde règle prédéfinie à partir d'une liste de référence présélectionnée suivant les informations caractéristiques ; et en la poussée des informations alternatives satisfaisant à la seconde règle prédéfinie dans la liste de référence présélectionnée au client.
PCT/CN2014/094830 2013-12-24 2014-12-24 Procédé, dispositif et système de traitement d'informations WO2015096742A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201310723793.5 2013-12-24
CN201310723793.5A CN104735117A (zh) 2013-12-24 2013-12-24 一种信息处理方法、装置和系统

Publications (1)

Publication Number Publication Date
WO2015096742A1 true WO2015096742A1 (fr) 2015-07-02

Family

ID=53458550

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2014/094830 WO2015096742A1 (fr) 2013-12-24 2014-12-24 Procédé, dispositif et système de traitement d'informations

Country Status (2)

Country Link
CN (1) CN104735117A (fr)
WO (1) WO2015096742A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109858942A (zh) * 2018-11-06 2019-06-07 北京奇虎科技有限公司 推广信息展示方法、装置、电子设备及可读存储介质

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105072204B (zh) * 2015-08-31 2019-03-01 北京金山安全软件有限公司 一种信息推送方法及装置
CN108415908B (zh) * 2017-02-09 2021-12-10 腾讯科技（北京）有限公司 一种多媒体数据的处理方法及服务器
CN106919692B (zh) * 2017-03-07 2021-02-19 阿里巴巴（中国）有限公司 一种推送消息的方法和装置
CN108875043B (zh) * 2018-06-27 2022-02-25 腾讯科技（北京）有限公司 用户数据处理方法、装置、计算机设备和存储介质

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101828167A (zh) * 2007-10-04 2010-09-08 讯恩科技公司 推荐产生系统、设备和方法
CN102592235A (zh) * 2011-12-28 2012-07-18 北京品友互动信息技术有限公司 一种互联网广告投放系统
US20130166389A1 (en) * 2011-12-23 2013-06-27 Dennis Paul MICHALOPOULOS List-based advertisement serving

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102385601B (zh) * 2010-09-03 2015-11-25 阿里巴巴集团控股有限公司 一种产品信息的推荐方法及系统
CN102938121A (zh) * 2011-08-15 2013-02-20 阿里巴巴集团控股有限公司 一种商品信息投放方法和设备

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101828167A (zh) * 2007-10-04 2010-09-08 讯恩科技公司 推荐产生系统、设备和方法
US20130166389A1 (en) * 2011-12-23 2013-06-27 Dennis Paul MICHALOPOULOS List-based advertisement serving
CN102592235A (zh) * 2011-12-28 2012-07-18 北京品友互动信息技术有限公司 一种互联网广告投放系统

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109858942A (zh) * 2018-11-06 2019-06-07 北京奇虎科技有限公司 推广信息展示方法、装置、电子设备及可读存储介质
CN109858942B (zh) * 2018-11-06 2023-12-15 三六零科技集团有限公司 推广信息展示方法、装置、电子设备及可读存储介质

Also Published As

Publication number Publication date
CN104735117A (zh) 2015-06-24

Similar Documents

Publication Publication Date Title
US10417650B1 (en) Distributed and automated system for predicting customer lifetime value
WO2018121253A1 (fr) Procédé, dispositif et équipement d'ajustement d'un taux de publication de publicité
US12062070B2 (en) Ad serving with multiple goals using constraint error minimization
US20160132935A1 (en) Systems, methods, and apparatus for flexible extension of an audience segment
WO2019105226A1 (fr) Procédé et dispositif utilisés pour prédire l'effet d'une activité de mercatique, et dispositif électronique
US8620720B2 (en) Embedding calendar knowledge in event-driven inventory forecasting
WO2015096742A1 (fr) Procédé, dispositif et système de traitement d'informations
WO2017121314A1 (fr) Procédé et appareil de recommandation d'informations
US20110231239A1 (en) Method and system for attributing an online conversion to multiple influencers
WO2012048244A2 (fr) Système et procédé pour une adaptation de campagne publicitaire en temps réel
US20110173063A1 (en) Advertiser value-based bid management in online advertising
US20150235239A1 (en) Predicting demand of a newly introduced short lifecycle product within an assortment
CN110889725B (zh) 在线广告ctr预估方法、装置、设备及存储介质
CN101673385A (zh) 消费预估方法和装置
WO2015127864A1 (fr) Procédé et appareil pour commander des données multimédia à placer à vitesse constante
JP5094956B2 (ja) 広告配信サーバ及び広告配信方法
WO2018214503A1 (fr) Procédé et dispositif de réglage de poids d'échantillon et appareil électronique
US20160267519A1 (en) Allocating online advertising budget based on return on investment (roi)
US20170372352A1 (en) Real-time bidding through placebo-based experimentation
US20170091811A1 (en) Systems, methods, and devices for customized data event attribution and bid determination
US20110055004A1 (en) Method and system for selecting and optimizing bid recommendation algorithms
US20160350814A1 (en) Systems, methods, and apparatus for budget allocation
US10181130B2 (en) Real-time updates to digital marketing forecast models
CN104835067B (zh) 网络广告实时竞价系统
CN105469259A (zh) 发布信息展示方法及系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14873515

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC ( EPO FORM 1205A DATED 17/11/2016 )

122 Ep: pct application non-entry in european phase

Ref document number: 14873515

Country of ref document: EP

Kind code of ref document: A1