CN114398558A - Information recommendation method and device, electronic equipment and storage medium - Google Patents

Information recommendation method and device, electronic equipment and storage medium Download PDF

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CN114398558A
CN114398558A CN202210063127.2A CN202210063127A CN114398558A CN 114398558 A CN114398558 A CN 114398558A CN 202210063127 A CN202210063127 A CN 202210063127A CN 114398558 A CN114398558 A CN 114398558A
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information
matching degree
recommended
determining
threshold
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CN114398558B (en
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周成瑜
曲巍
江兴何
黄庆亚
李晨阳
顾文婷
余飞
周天力
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G06Q30/0271—Personalized advertisement

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Abstract

The disclosure provides an information recommendation method, an information recommendation device, electronic equipment and a storage medium, and relates to the technical field of internet, in particular to the technical field of intelligent recommendation and user understanding. The specific implementation scheme of the information recommendation method is as follows: in response to the object information of the target object, determining a first matching degree between the information to be recommended and the target object according to the object information; and determining the information to be recommended as the alternative information for the target object in response to the fact that the first matching degree is larger than or equal to a matching degree threshold value for the information to be recommended, wherein an initial value of the matching degree threshold value is determined according to offline flow data of the information to be recommended, and the matching degree threshold value is adjusted by the online flow data of the information to be recommended.

Description

Information recommendation method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to the field of intelligent recommendation and the field of user understanding, and in particular, to an information recommendation method and apparatus, an electronic device, and a storage medium.
Background
With the development of internet technology, the requirement for conversion rate of the released information is gradually increased. And whether the information can be accurately put is a main factor influencing the conversion rate. The information conversion process may refer to a process in which a user selects a corresponding product by browsing information.
Disclosure of Invention
Based on this, the present disclosure provides an information recommendation method, apparatus, device, and storage medium aiming at improving information conversion rate.
According to an aspect of the present disclosure, there is provided an information recommendation method including: in response to the object information of the target object, determining a first matching degree between the information to be recommended and the target object according to the object information; and determining the information to be recommended as the alternative information for the target object in response to the fact that the first matching degree is larger than or equal to the matching degree threshold value for the information to be recommended. The initial value of the matching degree threshold is determined according to the offline flow data of the information to be recommended, and the matching degree threshold is adjusted by the online flow data of the information to be recommended.
According to another aspect of the present disclosure, there is provided an information recommendation apparatus including: the first matching determination module is used for responding to the acquired object information of the target object and determining a first matching degree between the information to be recommended and the target object according to the object information; and the alternative determining module is used for determining the information to be recommended as the alternative information for the target object in response to the fact that the first matching degree is larger than or equal to a matching degree threshold value for the information to be recommended, wherein an initial value of the matching degree threshold value is determined according to offline flow data of the information to be recommended, and the matching degree threshold value is adjusted by the online flow data of the information to be recommended.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the information recommendation methods provided by the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the information recommendation method provided by the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the information recommendation method provided by the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic view of an application scenario of an information recommendation method and apparatus according to an embodiment of the present disclosure;
FIG. 2 is a flow chart diagram of an information recommendation method according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram illustrating the principle of determining a threshold of a match from offline traffic data, according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating the principle of determining a threshold of a degree of match according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating the principle of adjusting the threshold of the matching degree according to the traffic data on the line according to an embodiment of the present disclosure;
fig. 6 is a block diagram of the structure of an information recommendation device according to an embodiment of the present disclosure; and
fig. 7 is a block diagram of an electronic device for implementing an information recommendation method according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The disclosure provides an information recommendation method, which comprises a matching determination stage and an alternative determination stage. In the matching determination stage, in response to the object information of the target object being obtained, a first matching degree between the information to be recommended and the target object is determined according to the object information. In the alternative determining stage, in response to the fact that the first matching degree is larger than or equal to the threshold value of the matching degree for the information to be recommended, the information to be recommended is determined to be alternative information for the target object. The initial value of the matching degree threshold is determined according to the offline flow data of the information to be recommended, and the matching degree threshold is adjusted by the online flow data of the information to be recommended.
An application scenario of the method and apparatus provided by the present disclosure will be described below with reference to fig. 1.
Fig. 1 is a schematic view of an application scenario of an information recommendation method and apparatus according to an embodiment of the present disclosure.
As shown in fig. 1, the scenario 100 of this embodiment includes a user 110, a terminal device 120, and a server 130.
Terminal device 120 may be communicatively coupled to server 130 via a network. The network may comprise a wired or wireless network, among others.
The terminal device 120 may be, for example, any electronic device having processing functionality, including but not limited to, a smart phone, a tablet computer, a laptop portable computer, a desktop computer, and the like. For example, the terminal device 120 may be installed with various client applications, such as a news browsing-type application, a game-type application, an instant messaging-type application, and so on (for example only).
The server 130 may be a server that provides various services, such as a background management server that may provide support for applications running on the terminal device 120. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
In an embodiment, the terminal device 120 may send the object information 140 of the target object to the server 130, with the user 110 as the target object, in response to the user 110 opening or refreshing the client application. The server 130, for example, in response to receiving the object information 140, may determine recommendation information 150 matching the user 110 according to the preference of the user 110, and send the recommendation information 150 to the terminal device 120 for presentation.
In an embodiment, the server 130 may maintain a matching degree threshold for each piece of information to be recommended in the database 160, and in a case that it is determined from the user representation of the user 110 that the matching degree of the user and the information to be recommended is greater than the matching degree threshold, the information to be recommended is taken as alternative recommendation information. It will be appreciated that the threshold of the degree of match maintained by the server 130 may be determined by the server 130, or may be determined by other servers communicatively coupled to the server 130.
It should be noted that, the steps in the information recommendation method provided by the embodiment of the present disclosure may be generally executed by the server 130, or may be executed partially by the server 130 and partially by another server communicatively connected to the server 130. Accordingly, the modules in the information recommendation device provided by the embodiment of the present disclosure may be disposed in the server 130, or may be disposed partially in the server 130 and partially in other servers communicatively connected to the server 130.
It should be understood that the number and type of terminal devices, servers, and databases in fig. 1 are merely illustrative. There may be any number and type of terminal devices, servers, and databases, as the implementation requires.
The information recommendation method provided by the present disclosure will be described in detail below with reference to fig. 1 through fig. 2 to 5 below.
Fig. 2 is a flowchart illustrating an information recommendation method according to an embodiment of the present disclosure.
As shown in fig. 2, the information recommendation method 200 of this embodiment may include operations S210 to S220.
In operation S210, in response to acquiring the object information of the target object, a first matching degree between the information to be recommended and the target object is determined according to the object information.
According to an embodiment of the present disclosure, the target object may be, for example, a user of the terminal device, and the object information may include, for example, identification information of the target object, which may be an account identification of the client application.
Upon receiving the object information, the server may determine attribute information and/or portrait characteristics of the target object based on the object information. For example, the attribute information and/or portrait characteristics determined from the object information may include the amount of information viewed within a predetermined period of time, the number of times the information is clicked within a predetermined period of time, and the like. It will be appreciated that the attribute information and/or representation characteristics of the target object include information that is authorized, accessible, by the target object.
The embodiment can determine a first matching degree between each piece of information to be recommended and the target object according to the determined attribute information and/or portrait characteristics of the target object and the characteristics of each piece of information to be recommended aiming at each piece of information to be recommended in the database.
In one embodiment, a degree of match prediction model may be employed to determine the first degree of match. For example, the determined attribute information and/or portrait characteristics of the target object and the characteristics of each piece of information to be recommended may be spliced and used as the input of the matching degree estimation model, and the matching degree estimation model outputs the first matching degree. The matching degree estimation model can be a conversion rate estimation model or a click rate estimation model, and specifically can comprise a circulating neural network model, a deep neural network model and the like.
In one embodiment, the matching degree prediction model may include a double-tower deep neural network model composed of a conversion rate prediction model and a click rate prediction model. The embodiment can splice the determined attribute information and/or the portrait characteristics of the target object and the characteristics of each piece of information to be recommended and then simultaneously serve as the input of the conversion rate pre-estimation model and the click rate pre-estimation model, and the product of the conversion rate pre-estimated by the conversion rate pre-estimation model and the click rate pre-estimated by the click rate pre-estimation model serves as the first matching degree.
Illustratively, in the double-tower deep neural network model, the structural complexity of the click rate prediction model may be higher than that of the conversion rate prediction model, so as to improve the learning ability of the matching degree prediction model. The positive sample size of the click rate estimation is larger than that of the conversion rate under normal conditions, and the learning capacity of the click rate estimation model can be improved on the premise of ensuring the convergence of the model by designing a complex structure for the click rate estimation model.
In an embodiment, the matching degree pre-estimation model may be adjusted periodically, and when the method for recommending information is executed, the latest matching degree pre-estimation model may be obtained to perform the pre-estimation of the matching degree.
In an embodiment, in determining the first matching degree, in addition to the attribute information and/or portrait feature of the determined target object and the feature of each piece of information to be recommended, the cross feature between the target object and the information to be recommended may be considered. For example, the cross feature may include an association relationship between the type of the target object and the type of the information to be recommended. For example, if the target object of type a has a high preference for the information to be recommended of type a, the cross feature may include a feature pair consisting of type a and type a.
In operation S220, in response to that the first matching degree is greater than or equal to a matching degree threshold for the information to be recommended, the information to be recommended is determined to be alternative information for the target object.
According to the embodiment of the disclosure, each piece of information to be recommended may have a unique corresponding matching degree threshold value, which is used as a basis for determining whether each piece of information to be recommended is alternative information. The embodiment may determine that a certain piece of information to be recommended is alternative information for the target object when a first matching degree between the target object and the certain piece of information to be recommended is greater than or equal to a matching degree threshold value uniquely corresponding to the certain piece of information to be recommended. It can be understood that, for different information to be recommended, the values of the unique corresponding matching degree thresholds may be equal or unequal.
According to the embodiment of the disclosure, a matching degree threshold value uniquely corresponding to a certain piece of information to be recommended may have an initial value, for example, and the matching degree threshold value may be adjusted according to the online traffic data. The initial value may be determined according to offline traffic data of the certain information to be recommended. If the unit of the day is used, the offline traffic data may refer to traffic data in a predetermined time period before the current day, and the online traffic data may refer to traffic data before the current time in the current day. It is to be understood that the above-mentioned online traffic data, offline traffic data, and units (days) are merely examples to facilitate understanding of the present disclosure, and the present disclosure is not limited thereto.
For example, the traffic data may include presentation time information of the information to be recommended, object information of a presentation object (i.e., a recommended object) of the information to be recommended, operation information of the recommended object on the information to be recommended, and the like. The operation information may include, for example, a click operation, a conversion operation on a recommendation object in the information to be recommended, and the like. The conversion operation may be different for different types of recommendation objects. For example, if the information to be recommended is an APP advertisement, the recommendation object is an APP, and the conversion operation may include a download operation and/or a registration operation of the APP; if the information to be recommended is an article promotion advertisement, the recommendation object is an article, and the conversion operation may include ordering operation on the article and the like.
For example, the matching degree threshold value for the information to be recommended may be determined with the target that the conversion rate of the information to be recommended reaches the target conversion rate. For example, the minimum value of the matching degree required for reaching the target conversion rate may be determined according to the offline flow data of the information to be recommended, and the minimum value may be used as the initial value of the matching degree threshold. In this way, the conversion rate of the information to be recommended can be made to reach the target conversion rate by recommending the information to be recommended to the object whose matching degree with the information to be recommended is greater than or equal to the initial value.
For example, the online conversion rate of the information to be recommended may be determined according to the online traffic data, and when the online conversion rate is lower than the target conversion rate, for example, the matching degree threshold may be appropriately increased, so that the information to be recommended may be recommended to a more matching object to improve the online conversion rate of the object to be recommended.
For example, when the online conversion rate is higher than the target conversion rate, the embodiment may appropriately decrease the matching degree threshold value, so that the information to be recommended is recommended to more subjects, and the exposure of the information to be recommended is increased.
In summary, the information recommendation method according to the embodiment of the disclosure determines whether to use the information to be recommended as the alternative information by setting the matching degree threshold for the information to be recommended, and can improve the conversion rate of the information to be recommended to a certain extent. Moreover, the matching degree threshold value not only determines an initial value according to the offline flow data, but also adjusts the value of the matching degree threshold value according to the online flow data, so that the situation that the conversion rate cannot be ensured due to the large difference between the offline flow and the online flow can be avoided, and the information to be recommended is further ensured to have a large conversion rate.
The principle of determining the initial value of the matching threshold from the offline traffic data will be described in detail below with reference to fig. 3 to 4.
Fig. 3 is a schematic diagram illustrating a principle of determining a threshold of a degree of match from offline traffic data according to an embodiment of the present disclosure.
As shown in fig. 3, in this embodiment 300, when determining the matching degree threshold according to offline traffic data, in response to acquiring offline traffic data 310 of any piece of information to be recommended, a second matching degree 320 between the offline traffic data 310 and a recommended object 311 may be determined first. Wherein, the recommended object 311 can be determined according to the offline traffic data. The second matching degree 320 is similar to the first matching degree determination method described above, and is not described herein again. Then, according to the second matching degree 320 and the operation information 312, a minimum matching degree that allows the actual conversion rate for the information to be recommended to reach the target conversion rate may be determined as the matching degree threshold 330. The offline traffic data 310 includes object information of the recommended object 311, operation information 312 of the recommended object 311 on the information to be recommended, and the like. The object information of the recommended object 311 is similar to the object information of the target object described above, and the operation information 312 may be the click operation, the conversion operation, or the like described above.
In this embodiment 300, the offline traffic data 310 may include accumulated m traffic data, and the embodiment may determine object information of a plurality of recommended objects included in the m traffic data. And then determining a second matching degree between the information to be recommended and each recommended object in the m recommended objects to obtain m second matching degrees corresponding to the m traffic data. And then sorting the m second matching degrees from high to low to form a matching degree sequence. For the matching degree sequence, the conversion rate of the information to be recommended can be determined according to i pieces of operation information included in the offline flow data corresponding to the first i pieces of second matching degrees, starting from the second matching degree arranged at the first position. And then determining that the conversion rate is greater than the target conversion rate, and if so, setting i as i + 1. If the conversion rate is smaller than the actual conversion rate, the conversion rate determined according to the offline flow data corresponding to the previous (i-1) second matching degree is used as the actual conversion rate, and the (i-1) th second matching degree is used as the matching degree threshold 330. Wherein m is an integer greater than 1, and the value range of i is greater than or equal to 1 and less than or equal to m.
In an embodiment, before determining the matching degree threshold according to the offline flow data, it may be further determined whether the offline accumulated flow data 340 meets a predetermined condition, and if so, the offline accumulated flow data 340 is taken as the offline flow data 310, so as to determine that the offline flow data is acquired. And if not, continuously accumulating the flow data under the line. The predetermined condition may be, among other things, limiting the amount of flow data accumulated offline and/or limiting the conversion rate determined by the offline accumulated flow data. In this way, the stability and accuracy of the determined matching degree threshold can be guaranteed.
Illustratively, the predetermined condition may include at least one of: the number of the offline accumulated traffic data is equal to or greater than a predetermined number threshold, the number of target traffic data in the offline accumulated traffic data is equal to or greater than a first threshold, a conversion rate determined from operation information included in the offline accumulated traffic data is equal to or greater than a target conversion rate, and the like. It is understood that the embodiment may accumulate the traffic data in real time without interruption, and determine the conversion number of the information to be recommended according to the accumulated traffic data. The conversion number is the number of the target flow data, that is, the number of the flow data of which the operation information indicates that the information to be recommended is converted. Wherein, in a case where the operation information includes a conversion operation, it may be determined that the operation information indicates that the information to be recommended is converted. Or, an identification bit may be added to the operation information by a developer of the information to be recommended, and the identification bit may be used to indicate whether the information to be recommended is converted. For example, the embodiment may also determine the historical total conversion number of the information to be recommended according to all the traffic data of the information to be recommended. The first threshold may be any value, such as 20 or 30, for example, which is not limited in this disclosure.
The start time of the offline accumulated flow data may be the start time of each day. Alternatively, for the current date, the offline flow data 310 may be obtained by acquiring flow data generated before the current date one by one from all the flow data accumulated offline according to the generation time from the back to the front until the acquired flow data satisfies a predetermined condition. The process of acquiring the flow data one by one may be understood as a process of accumulating the flow data offline.
In an embodiment, the first matching degree and the second matching degree may be determined by using the matching degree estimation model described above. The aforementioned predetermined condition may further include: when the flow data accumulated under the line is taken as the test data of the matching degree estimation model, the sequencing index of the matching degree estimation model is more than or equal to the index threshold value.
For example, the embodiment may obtain a sample data according to the traffic data accumulated under each line, where the sample data is similar to the data of the input matching degree prediction model described above, and is not described herein again. And inputting the obtained sample data into a matching degree pre-estimation model to obtain a predicted matching degree. If the predicted matching degree is greater than or equal to the predetermined matching degree, the predicted conversion result corresponding to the flow data accumulated under each line can be determined to be converted, otherwise, the predicted conversion result is determined to be not converted. And if the predicted conversion result and the conversion result indicated by the operation information included in the flow data accumulated under each line are converted, the flow data accumulated under each line is true positive data. If the predicted conversion result is converted, the conversion result indicated by the operation information included in the flow data accumulated under each line is not converted, and then the flow data accumulated under each line is false positive data. The embodiment can count the proportion of the false positive data and the proportion of the true positive data in the accumulated flow data under all lines as the false positive rate and the true positive rate respectively. A ranking indicator is determined based on the false positive rate and the true positive rate. The ranking index may be, for example, an auc (area Under current) index, which refers to an area Under a Receiver Operating Characteristic Curve (ROC) of a subject. The ROC curve is a curve drawn by taking the true positive rate as the ordinate and the false positive rate as the abscissa. When the ranking index is an AUC index, the index threshold value may be any value smaller than 1, such as 0.6 or 0.65. It is to be understood that the foregoing types of ranking indicators and values of the indicator threshold are merely examples to facilitate understanding of the present disclosure, and the present disclosure is not limited thereto.
The predetermined condition in this embodiment may make the acquired offline flow data more reliable by defining the ranking index of the matching degree prediction model, and thus, may facilitate improving the accuracy and stability of the determined matching degree threshold.
Fig. 4 is a schematic diagram of the principle of determining a threshold of degree of match according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, a bucket-splitting principle may be employed to determine a threshold of degree of match from offline traffic data. Thus, the efficiency of determining the threshold value of the matching degree and the robustness of the threshold value of the matching degree can be improved.
As shown in fig. 4, when determining the threshold of the matching degree, the m traffic data may be divided into n data groups ordered from large to small according to the second matching degrees according to the m second matching degrees. Wherein, each data set corresponds to a matching degree interval. Wherein n is an arbitrary value of 1 or more and m or less.
Illustratively, the embodiment 400 may set n buckets according to the matching degree, where each bucket corresponds to one matching degree interval and each bucket corresponds to one data group. For example, the bucket 401 arranged at the first position corresponds to a matching degree interval of [0.9, 1], the bucket 402 arranged at the second position corresponds to a matching degree interval of [0.8, 0.9 ], the bucket 403 arranged at the third last position corresponds to a matching degree interval of [0.2, 0.3 ], the bucket 404 arranged at the second last position corresponds to a matching degree interval of [0.1, 0.2 ], and the bucket 405 arranged at the last position corresponds to a matching degree interval of [0, 0.1 "). In this embodiment, the m pieces of traffic data may be divided into n buckets according to the matching degree interval in which the m pieces of second matching degrees are located. It can be understood that the matching degree intervals corresponding to the n buckets may be uniformly arranged or non-uniformly arranged, which is not limited in this disclosure. For example, a larger matching degree interval may be set for buckets with sparser data distribution, and a smaller matching degree interval may be set for buckets with denser data distribution.
Subsequently, a target data set of the n data sets and the sorting position p of the target data set can be determined. Wherein, the actual conversion rate determined according to the operation information included in the 1 st data group to the p th data group in the m data groups is greater than or equal to the target conversion rate, and the actual conversion rate determined according to the operation information included in the 1 st data group to the (p +1) th data group in the m data groups is less than the target conversion rate. The lower limit of the matching degree interval corresponding to the target data set may be used as the matching degree threshold. Or, the sum of the lower limit value and a predetermined value may be used as a matching degree threshold, so as to further ensure that the conversion rate of the information to be recommended can reach the target conversion rate. Wherein the predetermined value is a value greater than zero.
For example, in embodiment 400, if the optimization target is to increase the conversion by 10%, and the actual conversion determined from the operation information included in the m pieces of flow rate data is set to Q ═ b, the target conversion may be determined to be 1.1 b. If the data set corresponding to the bucket 403 of the n buckets is the target data set, the actual conversion rate Qr determined according to the operation information included in the buckets 401 to 403 is greater than or equal to 1.1b, and the actual conversion rate Qr2 determined according to the operation information included in the buckets 401 to 404 is less than 1.1 b. This embodiment may determine the matching degree threshold according to the lower limit value 0.2 of the matching degree interval [0.2, 0.3) corresponding to the bucket 403.
According to the embodiment of the disclosure, under the condition that the sum of the lower limit value and the preset value is used as the threshold of the matching degree, the information recommendation method of the embodiment can also count the online flow data and determine the number of conversions of the information to be recommended on the line. If the conversion number is greater than the predetermined conversion number, the threshold value of the matching degree may be adjusted to a predetermined threshold value. The predetermined threshold is less than the threshold of the degree of match before adjustment. I.e. if the number of conversions is greater than a predetermined number of conversions, the threshold of the degree of matching is lowered. Therefore, the exposure of the information to be recommended can be improved as much as possible while the conversion rate of the information to be recommended on line is ensured to reach the target conversion rate. Therefore, the processing platform side of the recommendation information can be guaranteed to have higher benefits to a certain extent.
The predetermined conversion number may be determined according to the average exposure of the information to be recommended in unit time and the target conversion rate. For example, the product of the average exposure amount and the target conversion rate may be taken as the predetermined conversion number. The predetermined threshold may be, for example, zero or any other value close to zero, which is not limited by the present disclosure.
The principle of adjusting the matching threshold value according to the on-line traffic data will be described in detail below with reference to fig. 5.
Fig. 5 is a schematic diagram illustrating a principle of adjusting a threshold of a matching degree according to traffic data on a line according to an embodiment of the present disclosure.
As shown in fig. 5, in this embodiment 500, after the online traffic data 510 of the information to be recommended is acquired, the online conversion rate 520 of the information to be recommended may be determined according to the online traffic data 510, where the online conversion rate is real-time. The match threshold is then adjusted based on the difference 540 between the on-line conversion 520 and the target conversion 530. It will be appreciated that the threshold of match may be adjusted periodically based on the on-line traffic data. For example, the matching degree threshold is adjusted once every unit period. The length of the unit period may be, for example, 1h, 0.5h, or the like set according to actual needs.
Illustratively, the online traffic data, similar to the offline traffic data, may include operational information. This embodiment may determine whether each inline traffic data is converted based on operational information included with the inline traffic data, thereby statistically deriving the inline conversion 520. The embodiment may decrease the threshold of degree of match when the difference between the target conversion 530 and the on-line conversion 520 is greater than a first value. The match threshold may be increased when the difference between the target conversion 530 and the on-line conversion 520 is less than a second value. Wherein the first value is a value greater than zero and the second value is a value less than zero. The adjustment amplitude of the threshold matching degree may be a predetermined amplitude, for example, 0.1, which is not limited by the present disclosure.
In an embodiment, the adjustment step size of the matching degree threshold may be determined according to the difference 540 between the target conversion rate 530 and the on-line conversion rate 520. And then adjusting the threshold of the matching degree according to the adjustment step size. For example, the absolute value of the difference between the target conversion 530 and the on-line conversion 520 can be positively correlated to the adjustment step size. The larger the absolute value of the difference, the larger the adjustment step size.
For example, the absolute value of the difference between the target conversion 530 and the on-line conversion 520 may be proportional to the adjustment step size, and the scaling factor may be any value, such as 0.1, which is not limited by the present disclosure.
By determining the adjustment step length according to the difference, the adjustment on the matching degree threshold value can be more accurate, the on-line conversion rate of the information to be recommended is closer to the target conversion rate, and therefore the conversion rate and the exposure of the information to be recommended can be well balanced.
In one embodiment, when determining the adjustment step length according to the difference 540, a base 550 of the adjustment step length may be determined according to the difference 540, and then a weight 570 of the adjustment step length may be determined according to a real-time exposure 560 of the information to be recommended in a unit time, i.e. a weight is given to the base. Finally, the adjustment step 580 is determined based on the weights and cardinality. For example, the product of weight 570 and radix 550 may be taken as adjustment step 580. In this way, the situation that the reference values of the on-line conversion rates determined according to the on-line flow data are different due to different exposure amounts of different sections of data can be fully considered, and therefore the adjustment accuracy of the matching degree threshold value is improved. It is to be understood that the aforementioned on-line traffic data may be traffic data generated at least one unit period before the current time, or in the case of information recommendation in units of days, the on-line traffic data may be all on-line traffic data that have been generated on the current day.
Illustratively, a Proportional Integral Derivative (PID) algorithm may be used to determine the base 550 for the adjustment step size.
Figure BDA0003478782840000121
Wherein, Delta SjBase number of adjustment steps determined for the j-th period in the periodic adjustment, ejThe difference between the target conversion determined for the jth cycle and the on-line conversion. e.g. of the typej-1The difference between the target conversion determined for the (j-1) th cycle and the on-line conversion. e.g. of the typekThe difference between the target conversion determined for the kth cycle and the on-line conversion. Alpha is alpha1、α2、α3Are empirical values.
Illustratively, the ratio between the real-time exposure amount and the predetermined exposure amount may be used as the weight for adjusting the step size. Alternatively, the root mean square of the ratio between the real-time exposure and the predetermined exposure is used as the weight of the adjustment step. For example, the ratio between the time exposure amount and the predetermined exposure amount is positively correlated with the weight 570. It is to be understood that the above method for determining the weight of the adjustment step is only an example to facilitate understanding of the present disclosure, and the present disclosure is not limited thereto.
In an embodiment, a larger adjustment step size is allocated for the case of increasing the matching degree threshold, and a smaller adjustment step size is allocated for the case of decreasing the matching degree threshold, so that excessive exposure caused by excessively large decrease of the matching degree threshold is avoided, and real-time adjustment of the matching degree threshold is facilitated.
For example, when determining the weight of the adjustment step size from the real-time exposure amount and the predetermined exposure amount, it may be determined whether the cardinality derived from the difference is less than zero. If the base number is greater than or equal to zero, a first ratio of the real-time exposure to the predetermined exposure may be valued as the weight 570. If the base number is less than zero, a second ratio of the real-time exposure to the predetermined exposure may be valued as the weight 570. Wherein the second ratio is less than the first ratio. In this way, different adjustment steps can be allocated for the case of down-and up-adjustments. Wherein, the first ratio may be 1, the second ratio may be 0.5, etc., which is not limited by the present disclosure.
In an embodiment, as shown in fig. 5, before adjusting the matching degree threshold according to the online traffic data, it may further be determined whether the number of the online accumulated traffic data 590 is greater than a second threshold, and if the number of the online accumulated traffic data 590 is greater than the second threshold, the online accumulated traffic data 590 is regarded as the online traffic data 510, so as to determine that the online traffic data is acquired. If not, continuing to accumulate the flow data on the line. Thus, the accuracy and effectiveness of the adjustment of the threshold value of the degree of matching can be improved. The second threshold may be set according to actual requirements, and may be any value such as 10 or 20, which is not limited in this disclosure.
For example, different second thresholds may be allocated to different types of information to be recommended. For example, the second threshold may be positively correlated with the conversion complexity of the information to be recommended. The second threshold value is higher if the conversion of the information to be recommended is more complex. In this way, the accuracy of the on-line conversion rate determined from the on-line flow data can be ensured, and thus the accuracy of the adjustment of the matching degree threshold value can be improved. The conversion complexity can be positively correlated with the number of process steps required for the conversion, for example. For example, the number of operation steps is more for the downloading operation and the registering operation of the APP, and the number of operation steps is less for the ordering operation.
Based on the information recommendation method provided by the disclosure, the disclosure also provides an information recommendation device. The apparatus will be described in detail below with reference to fig. 6.
Fig. 6 is a block diagram of the structure of an information recommendation device according to an embodiment of the present disclosure.
As shown in fig. 6, the information recommendation apparatus 600 of this embodiment may include a first match determination module 610 and an alternative determination module 620.
The first matching determining module 610 is configured to, in response to obtaining the object information of the target object, determine a first matching degree between the information to be recommended and the target object according to the object information. In an embodiment, the first matching determining module 610 may be configured to perform the operation S210 described above, and is not described herein again.
The alternative determining module 620 is configured to determine that the information to be recommended is alternative information for the target object in response to that the first matching degree is greater than or equal to a matching degree threshold for the information to be recommended. The initial value of the matching degree threshold is determined according to the offline flow data of the information to be recommended, and the matching degree threshold is adjusted by the online flow data of the information to be recommended. In an embodiment, the alternative determining module 620 may be configured to perform the operation S220 described above, which is not described herein again.
According to an embodiment of the present disclosure, the information recommendation device 600 may further include a conversion rate determination module and a first threshold adjustment module. The conversion rate determining module is used for responding to the acquired online traffic data of the information to be recommended and determining the online conversion rate of the information to be recommended according to the online traffic data. The first threshold adjusting module is used for adjusting the matching degree threshold according to the difference between the target conversion rate and the on-line conversion rate.
According to an embodiment of the present disclosure, the information recommendation apparatus 600 may further include a second matching determination module and a threshold determination module. And the second matching determination module is used for responding to the offline flow data of the information to be recommended, and determining a second matching degree between the offline flow data and the recommended object. The offline traffic data comprises object information of the recommended object and operation information of the recommended object on information to be recommended. And the threshold value determining module is used for determining the minimum matching degree which enables the actual conversion rate of the information to be recommended to reach the target conversion rate according to the second matching degree and the operation information, and the minimum matching degree is used as a matching degree threshold value.
According to an embodiment of the present disclosure, the first threshold adjustment module may include a step size determination sub-module and an adjustment sub-module. And the step size determining submodule is used for determining an adjusting step size aiming at the matching degree threshold according to the difference. And the adjusting submodule is used for adjusting the threshold value of the matching degree according to the adjusting step length.
According to an embodiment of the present disclosure, the step size determination sub-module may include a base determination unit, an exposure amount determination unit, a weight determination unit, and a step size determination unit. The base number determination unit is used for determining a base number aiming at the adjustment step size according to the difference. The exposure amount determining unit is used for determining the real-time exposure amount of the information to be recommended in the unit time interval according to the on-line flow data. The weight determination unit is used for determining the weight for the adjustment step according to the real-time exposure amount and the preset exposure amount. The step size determining unit is used for determining the adjustment step size according to the base number and the weight.
According to an embodiment of the present disclosure, the weight determination unit may include a first determination subunit and a second determination subunit. The first determining subunit is used for determining the value of a first proportion of the ratio of the real-time exposure to the preset exposure as the weight under the condition that the base number is larger than or equal to zero. The second determining subunit is used for determining the value of a second proportion of the ratio of the real-time exposure to the preset exposure as the weight under the condition that the base number is less than zero. Wherein the second ratio is less than the first ratio.
According to an embodiment of the present disclosure, the offline traffic data includes a plurality of traffic data. The threshold determination module may include a data partitioning sub-module, a target determination sub-module, and a threshold determination sub-module. The data dividing submodule is used for dividing the plurality of flow data into n data groups which are sorted from large to small according to the matching degree according to the second matching degree between the plurality of flow data and the recommended object; each of the n data sets corresponds to a matching degree interval. The target determining submodule is used for determining a target data group of the n data groups and the sequencing position p of the target data group; wherein, the actual conversion rate determined according to the operation information included in the 1 st data group to the p th data group in the m data groups is greater than or equal to the target conversion rate, and the actual conversion rate determined according to the operation information included in the 1 st data group to the (p +1) th data group in the m data groups is less than the target conversion rate. And the threshold value determining submodule is used for determining the threshold value of the matching degree according to the lower limit value of the matching degree interval corresponding to the target data group.
According to an embodiment of the present disclosure, the threshold determination submodule may be configured to determine that a sum of the lower limit value and the predetermined value is the matching degree threshold. The information recommendation apparatus 600 may further include a second threshold adjustment module, configured to adjust the matching degree threshold to a predetermined threshold in response to determining that the conversion number for the information to be recommended is greater than the predetermined conversion number according to the acquired online traffic data. The preset value is a value larger than zero, and the preset threshold is smaller than the matching degree threshold before adjustment.
According to the embodiment of the disclosure, the first matching degree and the second matching degree are determined by adopting a matching degree pre-estimation model. The information recommendation device 600 may further include a first obtaining determination module, configured to determine that offline traffic data is obtained in response to at least one of the following conditions being satisfied: the method comprises the steps that the number of target flow data in flow data accumulated under a line of information to be recommended is larger than or equal to a first threshold, and operation information included in the target flow data indicates that the information to be recommended is converted; when the flow data accumulated under the line is taken as the test data of the matching degree estimation model, the sequencing index of the matching degree estimation model is more than or equal to the index threshold value.
According to an embodiment of the present disclosure, the information recommendation apparatus 600 may further include a second obtaining determination module, configured to determine that the online traffic data is obtained in response to that the number of the online traffic data of the information to be recommended is greater than a second threshold.
According to the embodiment of the disclosure, the second threshold is positively correlated with the conversion complexity of the information to be recommended.
In the technical scheme of the present disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the related users all conform to the regulations of the related laws and regulations, and do not violate the good custom of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 7 shows a schematic block diagram of an example electronic device 700 that may be used to implement the information recommendation method of embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 701 executes the respective methods and processes described above, such as the information recommendation method. For example, in some embodiments, the information recommendation method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the information recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the information recommendation method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a traditional physical host and a VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (25)

1. An information recommendation method, comprising:
in response to the object information of the target object, determining a first matching degree between the information to be recommended and the target object according to the object information; and
determining the information to be recommended as alternative information for the target object in response to the first matching degree being greater than or equal to a matching degree threshold value for the information to be recommended,
the initial value of the matching degree threshold is determined according to the offline flow data of the information to be recommended, and the matching degree threshold is adjusted by adopting the online flow data of the information to be recommended.
2. The method of claim 1, further comprising:
responding to the acquired online flow data of the information to be recommended, and determining the online conversion rate of the information to be recommended according to the online flow data; and
adjusting the threshold of degree of match according to a difference between a target conversion rate and the on-line conversion rate.
3. The method of claim 1, further comprising:
responding to the offline flow data of the information to be recommended, and determining a second matching degree between the offline flow data and the recommended object; the offline flow data comprises object information of the recommended object and operation information of the recommended object on the information to be recommended; and
and determining the minimum matching degree which enables the actual conversion rate of the information to be recommended to reach the target conversion rate according to the second matching degree and the operation information, and using the minimum matching degree as the threshold value of the matching degree.
4. The method of claim 2, wherein said adjusting the threshold of the degree of match according to the difference between a target conversion rate and the on-line conversion rate comprises:
determining an adjustment step length aiming at the matching degree threshold according to the difference; and
and adjusting the threshold value of the matching degree according to the adjustment step length.
5. The method of claim 4, wherein the determining, according to the difference, an adjustment step size for the threshold of degree of match comprises:
determining a base number for the adjustment step size according to the difference;
determining the real-time exposure of the information to be recommended in a unit time interval according to the on-line flow data;
determining the weight of the adjustment step according to the real-time exposure and the preset exposure; and
and determining the adjustment step size according to the base number and the weight.
6. The method of claim 5, wherein the determining a weight for the adjustment step size based on the real-time exposure amount and a predetermined exposure amount comprises:
under the condition that the base number is larger than or equal to zero, determining the value of a first proportion of the ratio of the real-time exposure to the preset exposure as the weight; and
determining a value of a second ratio of the real-time exposure to the predetermined exposure to be the weight in case the base number is less than zero,
wherein the second ratio is less than the first ratio.
7. The method of claim 3, wherein the offline traffic data comprises a plurality of traffic data; the determining, according to the second matching degree and the operation information, a minimum matching degree that enables an actual conversion rate of the information to be recommended to reach a target conversion rate includes:
dividing the plurality of flow data into n data groups which are sorted from large to small according to the matching degree according to a second matching degree between the plurality of flow data and the recommended object; each data group in the n data groups corresponds to a matching degree interval;
determining a target data group of the n data groups and a sequencing position p of the target data group; wherein the actual conversion rate determined according to the operation information included in the 1 st data group to the p th data group in the m data groups is greater than or equal to the target conversion rate, and the actual conversion rate determined according to the operation information included in the 1 st data group to the (p +1) th data group in the m data groups is less than the target conversion rate; and
and determining the threshold value of the matching degree according to the lower limit value of the matching degree interval corresponding to the target data set.
8. The method of claim 7, wherein:
determining the threshold value of the matching degree according to the lower limit value of the matching degree interval corresponding to the target data set comprises: determining the sum of the lower limit value and a preset value as the matching degree threshold value;
the method further comprises the following steps: adjusting the matching degree threshold to a predetermined threshold in response to determining that a conversion number for the information to be recommended is greater than a predetermined conversion number from the acquired on-line traffic data,
the preset value is a value larger than zero, and the preset threshold is smaller than the matching degree threshold before adjustment.
9. The method of claim 3, wherein the first and second degrees of match are determined using a degree of match prediction model; the method further comprises the following steps: determining that the offline traffic data is acquired in response to at least one of the following conditions being satisfied:
the quantity of target flow data in the flow data accumulated under the line of the information to be recommended is greater than or equal to a first threshold, and the operation information included in the target flow data indicates that the information to be recommended is converted;
and when the flow data accumulated off-line is taken as the test data of the matching degree estimation model, the sequencing index of the matching degree estimation model is greater than or equal to the index threshold.
10. The method of claim 2, further comprising:
and determining to acquire the online traffic data in response to the fact that the quantity of the online traffic data of the information to be recommended is larger than a second threshold value.
11. The method of claim 10, wherein the second threshold is positively correlated with a conversion complexity of the information to be recommended.
12. An information recommendation apparatus comprising:
the first matching determination module is used for responding to the object information of the obtained target object and determining a first matching degree between the information to be recommended and the target object according to the object information; and
an alternative determining module, configured to determine that the information to be recommended is alternative information for the target object in response to a matching degree threshold for the information to be recommended being greater than or equal to the first matching degree,
the initial value of the matching degree threshold is determined according to the offline flow data of the information to be recommended, and the matching degree threshold is adjusted by adopting the online flow data of the information to be recommended.
13. The apparatus of claim 12, further comprising:
the conversion rate determining module is used for responding to the acquired online flow data of the information to be recommended and determining the online conversion rate of the information to be recommended according to the online flow data; and
a first threshold adjustment module to adjust the matching degree threshold according to a difference between a target conversion rate and the on-line conversion rate.
14. The apparatus of claim 12, further comprising:
the second matching determination module is used for responding to the offline flow data of the information to be recommended, and determining a second matching degree between the offline flow data and the recommended object; the offline flow data comprises object information of the recommended object and operation information of the recommended object on the information to be recommended; and
and the threshold value determining module is used for determining the minimum matching degree which enables the actual conversion rate of the information to be recommended to reach the target conversion rate according to the second matching degree and the operation information, and the minimum matching degree is used as the threshold value of the matching degree.
15. The apparatus of claim 13, wherein the first threshold adjustment module comprises:
the step length determining submodule is used for determining an adjusting step length aiming at the matching degree threshold value according to the difference; and
and the adjusting submodule is used for adjusting the matching degree threshold according to the adjusting step length.
16. The apparatus of claim 15, wherein the step size determination submodule comprises:
a radix determining unit for determining a radix for the adjustment step size according to the difference;
the exposure determining unit is used for determining the real-time exposure of the information to be recommended in a unit time interval according to the online flow data;
a weight determination unit for determining a weight for the adjustment step according to the real-time exposure amount and a predetermined exposure amount; and
and the step size determining unit is used for determining the adjustment step size according to the base number and the weight.
17. The apparatus of claim 16, wherein the weight determination unit comprises:
the first determining subunit is used for determining the value of a first proportion of the ratio of the real-time exposure to the preset exposure as the weight under the condition that the base number is greater than or equal to zero; and
a second determining subunit, configured to determine, as the weight, a value of a second ratio of the real-time exposure amount to the predetermined exposure amount when the base number is smaller than zero,
wherein the second ratio is less than the first ratio.
18. The apparatus of claim 14, wherein the offline traffic data comprises a plurality of traffic data; the threshold determination module comprises:
the data dividing submodule is used for dividing the plurality of flow data into n data groups which are sorted from large to small according to the matching degree according to a second matching degree between the plurality of flow data and the recommended object; each data group in the n data groups corresponds to a matching degree interval;
the target determining submodule is used for determining a target data group of the n data groups and the sequencing position p of the target data group; wherein the actual conversion rate determined according to the operation information included in the 1 st data group to the p th data group in the m data groups is greater than or equal to the target conversion rate, and the actual conversion rate determined according to the operation information included in the 1 st data group to the (p +1) th data group in the m data groups is less than the target conversion rate; and
and the threshold value determining submodule is used for determining the threshold value of the matching degree according to the lower limit value of the matching degree interval corresponding to the target data group.
19. The apparatus of claim 18, wherein:
the threshold determination submodule is configured to: determining the sum of the lower limit value and a preset value as the matching degree threshold value;
the device further comprises a second threshold adjusting module for adjusting the threshold of the matching degree to a predetermined threshold in response to determining that the conversion number for the information to be recommended is greater than a predetermined conversion number according to the acquired online traffic data,
the preset value is a value larger than zero, and the preset threshold is smaller than the matching degree threshold before adjustment.
20. The apparatus of claim 14, wherein the first and second degrees of match are determined using a degree of match prediction model; the apparatus further includes a first acquisition determination module configured to determine to acquire the offline traffic data in response to at least one of the following conditions being satisfied:
the quantity of target flow data in the flow data accumulated under the line of the information to be recommended is greater than or equal to a first threshold, and the operation information included in the target flow data indicates that the information to be recommended is converted;
and when the flow data accumulated off-line is taken as the test data of the matching degree estimation model, the sequencing index of the matching degree estimation model is greater than or equal to the index threshold.
21. The apparatus of claim 13, further comprising:
and the second obtaining and determining module is used for determining to obtain the online flow data in response to the fact that the quantity of the online accumulated flow data of the information to be recommended is larger than a second threshold value.
22. The apparatus of claim 21, wherein the second threshold positively correlates to a conversion complexity of the information to be recommended.
23. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-11.
24. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of claims 1-11.
25. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 11.
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