CN115982221B - Activity recommendation system, method, electronic equipment and medium based on historical data - Google Patents
Activity recommendation system, method, electronic equipment and medium based on historical data Download PDFInfo
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- CN115982221B CN115982221B CN202310012230.9A CN202310012230A CN115982221B CN 115982221 B CN115982221 B CN 115982221B CN 202310012230 A CN202310012230 A CN 202310012230A CN 115982221 B CN115982221 B CN 115982221B
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- 230000000694 effects Effects 0.000 title claims abstract description 297
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000001960 triggered effect Effects 0.000 claims abstract description 18
- 230000006978 adaptation Effects 0.000 claims abstract description 14
- 230000004044 response Effects 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000001376 precipitating effect Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 2
- 206010043417 Therapeutic response unexpected Diseases 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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Abstract
The embodiment of the invention discloses an activity recommendation system, method, electronic equipment and medium based on historical data, wherein the activity recommendation method based on the historical data comprises the following steps: inquiring an active form of traffic bit adaptation in response to the triggered traffic bit inquiry instruction; calculating the activity effect data of each activity form in a preset time at fixed time; traffic is configured for each of the activity forms based on the activity effect data. The activity recommendation method based on the historical data has the problem that the efficiency of manually switching the activity forms is low.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an activity recommendation system, an activity recommendation method, electronic equipment and a medium based on historical data.
Background
In the time when the merchant activity flies all the day, the participation will of the user in lottery drawing activities is gradually reduced, and even for some repeated activity forms, the user's dislike can be caused; the participation rate of the user on the activities is improved;
when the user participates in data through observing a period of time during the activity of putting on one traffic bit, the operation switches to another activity form when the data is found to be not ideal. The fatigue of the user is reduced and the participation rate of the user on the activity is increased by artificially modifying the activity form, such as changing a large turntable into a pounding Jin Danxing type activity.
However, by manually switching the active form, the efficiency is relatively low, and when the data is obviously reduced in the later period of the activity, the active form is switched again, so that the flow is wasted. And find out that activity form effect is longer than the cycle of good activity, for example: ten activity forms exist, and the operation is performed by observing the data switching activity, so that 10 times of operation are needed to find out which activity form has better effect.
Disclosure of Invention
The embodiment of the invention aims to provide an activity recommendation system, an activity recommendation method, electronic equipment and a medium based on historical data, which are used for solving the problem of low efficiency of manually switching an activity form in the prior art.
In order to achieve the above object, an embodiment of the present invention provides an activity recommendation method based on historical data, where the method specifically includes:
inquiring an active form of traffic bit adaptation in response to the triggered traffic bit inquiring instruction, and putting the active form on the traffic bit;
calculating the activity effect data of each activity form in a preset time at fixed time;
traffic is configured for each of the activity forms based on the activity effect data.
Based on the technical scheme, the invention can also be improved as follows:
further, the activity recommendation method based on the historical data further comprises the following steps:
receiving the activity effect data through a database, and counting and classifying the activity effect data;
summarizing the activity effect data, realizing digging and precipitating, and providing data analysis for the activity recommendation.
Further, the responding to the triggered traffic bit inquiring instruction inquires the active form of traffic bit adaptation, and puts the active form on the traffic bit, including:
and responding to the triggered traffic bit inquiry instruction to inquire the database, and acquiring an active form conforming to the traffic bit configuration based on the database.
Further, the timing calculation includes:
judging whether the active forms are put for the first time, and configuring the same flow for each active form when the active forms are put for the first time; and when the activity forms are not put in for the first time, calculating activity effect data of each activity form in a preset time.
Further, the timing calculation unit calculates activity effect data of each activity form within a preset time, and further includes:
sorting the activity forms based on the activity effect data to obtain a sorting result, and obtaining an optimal activity form based on the sorting result; wherein the activity effect data includes a participation rate index and a conversion rate index for each activity form.
Further, the timing calculation unit calculates activity effect data of each activity form within a preset time, and further includes:
calculating participation of each activity form based on the participation amount of each activity form and the activity exposure amount;
an overall activity engagement average is calculated based on the engagement of each activity form and the overall number of activity forms.
Further, the configuring traffic for each of the activity forms based on the activity effect data includes:
dividing the flow of the flow bit into a first partial flow and a second partial flow, dividing the flow of the first partial flow uniformly in an active mode with participation rate larger than the average value, dividing the flow of the second partial flow uniformly in an active mode with participation rate lower than the average value, and enabling the first partial flow to be larger than the second partial flow.
An activity recommendation system based on historical data, comprising:
the query module is used for responding to the triggered traffic bit query instruction to query the active form of traffic bit adaptation;
the delivery module is used for delivering the movable form on the flow position;
the calculation module is used for calculating the activity effect data of each activity form in a preset time at fixed time;
and the configuration module is used for configuring the flow for each activity form based on the activity effect data.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method when the computer program is executed.
A non-transitory computer readable medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method.
The embodiment of the invention has the following advantages:
according to the activity recommendation method based on the historical data, an activity form of flow bit adaptation is queried in response to a triggered flow bit query instruction, and the activity form is put in the flow bit; calculating the activity effect data of each activity form in a preset time at fixed time; configuring traffic for each of the activity forms based on the activity effect data; the problem of among the prior art through manual work switching activity form inefficiency is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present invention, should fall within the ambit of the technical disclosure.
FIG. 1 is a flow chart of an activity recommendation method based on historical data according to the present invention;
FIG. 2 is a block diagram of an activity recommendation system based on historical data according to the present invention;
fig. 3 is a schematic diagram of an entity structure of an electronic device according to the present invention.
Wherein the reference numerals are as follows:
query module 10, delivery module 20, calculation module 30, configuration module 40, electronic device 50, processor 501, memory 502, bus 503.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Fig. 1 is a flowchart of an embodiment of an activity recommendation method based on historical data, and as shown in fig. 1, the activity recommendation method based on historical data provided by the embodiment of the invention includes the following steps:
s101, inquiring an active form of traffic bit adaptation in response to a triggered traffic bit inquiring instruction, and throwing the active form on the traffic bit;
specifically, the active forms include: turntable, gold egg breaking, ferrule, license plate turning and the like;
and responding to the triggered traffic bit inquiry instruction to inquire the database, and acquiring an active form conforming to the traffic bit configuration based on the database.
S102, calculating activity effect data of each activity form in a preset time at regular time;
specifically, judging whether the active form is first put, and when the active form is first put, configuring the same flow for each active form; and when the activity forms are not put in for the first time, calculating activity effect data of each activity form in a preset time.
Sorting the activity forms based on the activity effect data to obtain a sorting result, and obtaining an optimal activity form based on the sorting result; wherein the activity effect data includes a participation rate index and a conversion rate index for each activity form.
Calculating participation of each activity form based on the participation amount of each activity form and the activity exposure amount;
an overall activity engagement average is calculated based on the engagement of each activity form and the overall number of activity forms.
In a specific embodiment, if the first delivery is performed, there is no history data, the traffic on the traffic bit is equally divided according to the number of the active forms, for example, there are 10 active forms, and then each active form allocates 10% of the traffic, and the traffic proportion calculation logic: taking the remainder of 100 according to the hash value of ip to obtain the percentage flow;
calculating the latest 1 hour activity effect data, participation in each activity form, and average participation in the overall participation rate over the traffic bit, participation in each activity form: participation pv/activity exposure uv;
overall activity participation average avg=sum (participation pv/activity exposure uv)/overall number of activity forms;
s103, configuring flow for each activity form based on the activity effect data;
specifically, the flow of the flow bit is divided into a first part of flow and a second part of flow, the flow of the first part of flow is divided into an active form with participation rate larger than the average value, the flow of the second part of flow is divided into an active form with participation rate lower than the average value, and the first part of flow is larger than the second part of flow.
In a specific embodiment, dividing the activity form into 2 parts, taking the surplus of the flow bit according to the hash value of ip by the activity form with the participation rate larger than the average value and the activity form with the participation rate lower than the average value to obtain the percentage flow, dividing the flow into 80% and 20%, and equally dividing the activity form with the participation rate larger than the average value into 80% of flows, so that the whole data is ensured; the active form with participation rate lower than average averages 20% of the flow.
The next hour, the timing task continues to count, the statistical result is updated to the storage medium, the logic above is circulated, most people can be matched with the activity with good effect, and therefore the participation rate of users is ensured.
Receiving the activity effect data through a database, and counting and classifying the activity effect data; summarizing the activity effect data, realizing digging and precipitating, and providing data analysis for the activity recommendation.
The activity recommendation method based on historical data provides a new electronic record consumption mode, and a triggered flow bit inquiry instruction is responded to inquire about an activity form of flow bit adaptation; calculating the activity effect data of each activity form in a preset time at fixed time; traffic is configured for each of the activity forms based on the activity effect data.
The activity recommendation method based on the historical data solves the problems that when the activity is put in for the first time, the activity effect of the mode of putting the activity is unknown, the activity effect of which activity modes is bad, the flow is equally divided, the activity effect data is uniformly observed, and the activity effect data is obtained in the least time; the activity forms are divided into 2 parts by the participation rate index and the conversion rate index of each activity form, namely activity forms higher than the average value and activity forms lower than the average value. The flow distribution effect of 80% is larger than that of the average value active form, so that the whole data is ensured, and meanwhile, 20% of flow is distributed to the active form lower than the average value; most people can see the activities which are interested, meanwhile, other activity forms can have opportunities, 80% of the whole data are guaranteed, 20% of flow is given to the activities which are bad in the current period of effect, and the activities can have potential to become good-effect activities.
The method has the advantages that through timely switching the activity form with the optimal effect of the activity data in the last 1 hour, different activities are ordered in time directly based on the activity effect data calculated in the last 1 hour, the activities with good effect can be timely pushed to the user, the interests of the user are aroused, the participation rate of the user on the activities can be effectively increased, the better activity form is timely pushed based on the generated activity data, the interests of the user are aroused, and the participation rate of the user on the activities is effectively increased.
FIG. 2 is a flow chart of an activity recommendation system based on historical data according to an embodiment of the present invention; as shown in fig. 2, the activity recommendation system based on historical data provided by the embodiment of the invention includes the following steps:
a query module 10 for querying an active form of traffic bit adaptation in response to the triggered traffic bit query instruction; querying the database in response to the triggered traffic bit query instruction, and acquiring an active form conforming to the traffic bit configuration based on the database;
a delivery module 20 for delivering the active form on the traffic level;
a calculating module 30, configured to calculate activity effect data of each of the activity forms in a preset time at regular time; judging whether the active forms are put for the first time, and configuring the same flow for each active form when the active forms are put for the first time; when the activity forms are not put in for the first time, calculating activity effect data of each activity form in a preset time; sorting the activity forms based on the activity effect data to obtain a sorting result, and obtaining an optimal activity form based on the sorting result; wherein the activity effect data includes a participation rate index and a conversion rate index for each activity form. Calculating participation of each activity form based on the participation amount of each activity form and the activity exposure amount; an overall activity engagement average is calculated based on the engagement of each activity form and the overall number of activity forms.
A configuration module 40 for configuring traffic for each of the activity forms based on the activity effect data. Dividing the flow of the flow bit into a first partial flow and a second partial flow, dividing the flow of the first partial flow uniformly in an active mode with participation rate larger than the average value, dividing the flow of the second partial flow uniformly in an active mode with participation rate lower than the average value, and enabling the first partial flow to be larger than the second partial flow.
According to the activity recommendation system based on historical data, an activity form of traffic bit adaptation is inquired through an inquiry module 10 in response to a triggered traffic bit inquiry command; delivering the active form on the traffic location by a delivery module 20; calculating the activity effect data of each activity form in a preset time in a timing way through a calculating module 30; traffic is configured for each of the activity forms based on the activity effect data by a configuration module 40.
Fig. 3 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention, as shown in fig. 3, an electronic device 50 includes: a processor 501 (processor), a memory 502 (memory), and a bus 503;
wherein, the processor 501 and the memory 502 complete the communication with each other through the bus 503;
the processor 501 is configured to invoke program instructions in the memory 502 to perform the methods provided by the above-described method embodiments, for example, including: inquiring an active form of traffic bit adaptation in response to the triggered traffic bit inquiring instruction, and putting the active form on the traffic bit; calculating the activity effect data of each activity form in a preset time at fixed time; traffic is configured for each of the activity forms based on the activity effect data.
The present embodiment provides a non-transitory computer readable medium storing computer instructions that cause a computer to perform the methods provided by the above-described method embodiments, for example, including: inquiring an active form of traffic bit adaptation in response to the triggered traffic bit inquiring instruction, and putting the active form on the traffic bit; calculating the activity effect data of each activity form in a preset time at fixed time; traffic is configured for each of the activity forms based on the activity effect data.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable medium such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of the respective embodiments or parts of the embodiments.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (6)
1. An activity recommendation method based on historical data, which is characterized by comprising the following steps:
inquiring an active form of traffic bit adaptation in response to the triggered traffic bit inquiring instruction, and putting the active form on the traffic bit;
calculating the activity effect data of each activity form in a preset time at fixed time;
calculating participation of each activity form based on the participation amount of each activity form and the activity exposure amount; participation of each activity form = participation amount pv/activity exposure uv;
calculating an overall activity participation average based on the participation of each activity form and the overall number of activity forms; activity global participation average=sum (participation pv/activity exposure uv)/global number of activity forms;
sorting the activity forms based on the activity effect data to obtain a sorting result, and obtaining an optimal activity form based on the sorting result; wherein the activity effect data comprises a participation rate index and a conversion rate index of each activity form;
judging whether the active forms are put for the first time, and configuring the same flow for each active form when the active forms are put for the first time; when the activity forms are not put in for the first time, calculating activity effect data of each activity form in a preset time; equally dividing the flow on the flow position according to the number of the movable forms;
configuring traffic for each of the activity forms based on the activity effect data;
taking the surplus of 100 according to the hash value of ip of the flow bit to obtain the percentage flow; dividing the flow of the flow bit into a first partial flow and a second partial flow, dividing the flow of the first partial flow uniformly in an active mode with participation rate larger than the average value, dividing the flow of the second partial flow uniformly in an active mode with participation rate lower than the average value, and enabling the first partial flow to be larger than the second partial flow.
2. The activity recommendation method based on historical data according to claim 1, further comprising:
receiving the activity effect data through a database, and counting and classifying the activity effect data;
summarizing the activity effect data, realizing digging and precipitating, and providing data analysis for the activity recommendation.
3. The activity recommendation method based on historical data according to claim 2, wherein said querying the activity form of the traffic bit adaptation in response to the triggered traffic bit querying instruction and putting the activity form on the traffic bit comprises:
and responding to the triggered traffic bit inquiry instruction to inquire the database, and acquiring an active form conforming to the traffic bit configuration based on the database.
4. An activity recommendation system based on historical data, comprising:
the query module is used for responding to the triggered traffic bit query instruction to query the active form of traffic bit adaptation;
the delivery module is used for delivering the movable form on the flow position;
the calculation module is used for calculating the activity effect data of each activity form in a preset time at fixed time;
the computing module is further for:
judging whether the active forms are put for the first time, and configuring the same flow for each active form when the active forms are put for the first time; when the activity forms are not put in for the first time, calculating activity effect data of each activity form in a preset time; equally dividing the flow on the flow position according to the number of the movable forms;
calculating participation of each activity form based on the participation amount of each activity form and the activity exposure amount; participation of each activity form = participation amount pv/activity exposure uv;
calculating an overall activity participation average based on the participation of each activity form and the overall number of activity forms; activity global participation average=sum (participation pv/activity exposure uv)/global number of activity forms;
sorting the activity forms based on the activity effect data to obtain a sorting result, and obtaining an optimal activity form based on the sorting result; wherein the activity effect data comprises a participation rate index and a conversion rate index of each activity form;
a configuration module for configuring traffic for each of the activity forms based on the activity effect data;
the configuration module is further configured to:
taking the surplus of 100 according to the hash value of ip of the flow bit to obtain the percentage flow; dividing the flow of the flow bit into a first partial flow and a second partial flow, dividing the flow of the first partial flow uniformly in an active mode with participation rate larger than the average value, dividing the flow of the second partial flow uniformly in an active mode with participation rate lower than the average value, and enabling the first partial flow to be larger than the second partial flow.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 3 when the computer program is executed.
6. A non-transitory computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1 to 3.
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| CN109299356A (en) * | 2018-08-22 | 2019-02-01 | 中国平安人寿保险股份有限公司 | Activity recommendation method, apparatus, electronic equipment and storage medium based on big data |
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| CN112822281A (en) * | 2021-01-21 | 2021-05-18 | 中国平安人寿保险股份有限公司 | Flow distribution method and device, terminal equipment and computer readable storage medium |
| CN113765811A (en) * | 2020-06-05 | 2021-12-07 | 腾讯科技（深圳）有限公司 | Flow control method, device, equipment and storage medium |
| CN114286135A (en) * | 2021-12-22 | 2022-04-05 | 天翼爱音乐文化科技有限公司 | New video recommendation traffic distribution method, system, device and medium |
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Patent Citations (5)
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| CN109299356A (en) * | 2018-08-22 | 2019-02-01 | 中国平安人寿保险股份有限公司 | Activity recommendation method, apparatus, electronic equipment and storage medium based on big data |
| CN113765811A (en) * | 2020-06-05 | 2021-12-07 | 腾讯科技（深圳）有限公司 | Flow control method, device, equipment and storage medium |
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