CN113590933A - Flight pushing method and system and electronic equipment - Google Patents
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Abstract
The invention discloses a flight pushing method, a flight pushing system and electronic equipment, and relates to the field of aviation. The method comprises the following steps: acquiring all user behavior data and all flight data of flights to be pushed; setting flight decision requirements according to flights to be pushed; mining association rules of user behavior data and flight decision requirements according to a preset mining algorithm; and acquiring the flight to be pushed associated with the behavior data of the target user according to the association rule, and pushing the flight to be pushed to the target user. The target user is pushed directionally according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirements, so that the mining precision is improved, and the pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
Description
Technical Field
The invention relates to the field of aviation, in particular to a flight pushing method, a flight pushing system and electronic equipment.
Background
With the rapid development of passenger transport aviation, airplanes gradually become the choice of many people when going out. To increase the availability of flights, airlines typically attract customers to purchase tickets for a flight in the form of discounts or the like. Although airline companies have discounted airline tickets for flights, there are still situations in most cases where stock tickets are not sold for the flight.
At present, when a user needs to go out, the ticket of each flight can be purchased on a ticket purchasing website according to a user travel plan, and when the flight has discounted stock tickets, the user also needs to actively search and then know the discounting condition of the ticket. Therefore, a user cannot know which flight has a discounted air ticket in time, and an airline company pushes the discount information of each flight to a client, but the pushing matching rate is poor, and the discount information is shielded by the client as advertisement marketing information, so that the airline company does not improve the attendance rate of the flight, and other popularization of the airline company is influenced.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a flight pushing method, a flight pushing system and electronic equipment.
The technical scheme for solving the technical problems is as follows:
a flight pushing method, comprising:
s1, acquiring all user behavior data and all flight data of flights to be pushed;
s2, setting flight decision requirements according to the flights to be pushed;
s3, mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm;
and S4, acquiring the flight to be pushed associated with the behavior data of the target user according to the association rule, and pushing the flight to be pushed to the target user.
The invention has the beneficial effects that: the association rule of the user behavior data and the flight decision requirement is obtained through a mining algorithm, the association rule is pushed to a target user in a directional mode according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirement, so that mining precision is improved, and pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
Further, still include:
s30, calculating influence factors related to the flight decision requirements from the user behavior data through a preset mining method;
the S3 specifically includes: and mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm and the influence factors.
The beneficial effect of adopting the further scheme is that: by obtaining important relevant factors, namely influence factors, when the client makes flight decisions, mining calculation of non-relevant data is reduced, the mining matching degree is high, and the mining precision and efficiency are improved.
Further, the user behavior data includes: multi-source data;
the S30 specifically includes:
performing fusion classification on the multi-source data;
establishing an incidence relation between the fused and classified multi-source data and the flight decision requirement, and quantifying the incidence relation through distance entropy;
performing fusion calculation according to the quantified incidence relation to obtain the distance entropy between the flight decision demand and the multi-source data;
and when the distance entropy meets a preset condition, the multi-source data corresponding to the current distance entropy is the influence factor.
The beneficial effect of adopting the further scheme is that: according to the scheme, the depth association and fusion are carried out on the multi-source data and the decision-making requirement according to the association degree and the trust degree; based on the process of continuously shortening the distance between multi-source data and decision requirements in the characteristic fusion process, the correlation quantification of the distance entropy is carried out, the stability of promoting fusion by the optimal distance is realized, the total entropy of the fusion system is minimized, and the information amount is maximized.
The traditional data combing mode adopts manual classification and other modes, and because the relevance and non-logical processing amount among various data sources exceeds the manual processing capacity, the optimal data fusion can be realized by fusing classification and deep relevance according to the scheme, and meanwhile, the most reasonable information amount is ensured.
Further, still include: the distance entropy is calculated by:
the formula for calculating the distance between the optimal set and other sets is as follows:
wherein the multi-source data and flight decision requirements are in m sets, each set having n knowledge elements, Oj *The optimal value of each set, i.e. the optimal value in the j-th set, j being 1, 2.., m; o isijInformation element values representing the jth and ith sets, i 1, 2.., n;
the formula for calculating the distance entropy of the ith set is as follows:
the beneficial effect of adopting the further scheme is that: according to the scheme, the depth association and fusion are carried out on the multi-source data and the decision-making requirements according to the association degree and the trust degree through the distance entropy.
Further, still include:
carrying out fine-grained division on the influence factors according to corresponding weight coefficients to obtain the influence factors after weight distribution;
and dividing the influence factors into multiple layers according to types to obtain the influence factors of multiple levels.
The beneficial effect of adopting the further scheme is that: in the existing scheme, multi-level influence factors are constructed from a large amount of non-granularity and non-level data, compared with the method of directly mining a large amount of data, the scheme can improve the support degree of an item set in association rule mining through a multi-level relation.
Further, still include: and calculating to obtain the weight coefficient according to the importance of the influence factor relative to the flight decision requirement.
The beneficial effect of adopting the further scheme is that: according to the scheme, the influence factors are divided through the weight coefficients, and the hierarchy and category division is more accurate.
Further, the S3 specifically includes:
and respectively carrying out association mining on the influence factors of multiple levels and the flight decision requirement through an Apriori algorithm to obtain an association rule of multiple levels.
The beneficial effect of adopting the further scheme is that: the existing user behavior data comprises a large amount of non-granularity and non-hierarchy data, a frequent item set is obtained through influence factors after multi-hierarchy and fine-granularity processing by using an Apriori algorithm in an iterative mode, the item set which does not meet the minimum support degree is filtered, and the potential association rule can be mined.
Further, still include: the user behavior data and the flight data of the flight to be pushed are obtained through a crawler tool,
the crawler tool comprises: concurrent crawler tools in master-slave mode for the master node and the slave nodes; the master node is used for maintaining a queue to be crawled of the whole crawler and task allocation work, and the slave node is used for receiving a master node delegation task;
each slave node maintains a task queue and a new link queue in real time, and after the slave nodes complete the task queues, the new link queues of the slave nodes are merged into the queue to be climbed of the master node;
and the master node continuously dispatches the links of the queue to be crawled to all the slave nodes, and the slave nodes continuously crawl all new user behavior data and all flight data of the flights to be pushed.
The beneficial effect of adopting the further scheme is that: due to the fact that the user behavior data and the flight data are large in quantity, the crawler of the single process is difficult to meet the requirement of fast crawling a large amount of data. By means of the concurrent data crawling function, user behavior data and flight data can be directly obtained from a large amount of data; the data crawling efficiency is improved.
Another technical solution of the present invention for solving the above technical problems is as follows:
a flight pushing system, comprising: the system comprises a multi-source data acquisition module, a configuration module, a mining module and a pushing module;
the multi-source data acquisition module is used for acquiring all user behavior data and all flight data of flights to be pushed;
the configuration module is used for setting flight decision requirements according to flights to be pushed;
the mining module is used for mining association rules of user behavior data and flight decision requirements according to a preset mining algorithm;
and the pushing module is used for acquiring the flight to be pushed associated with the behavior data of the target user according to the association rule and pushing the flight to be pushed to the target user.
The invention has the beneficial effects that: the association rule of the user behavior data and the flight decision requirement is obtained through a mining algorithm, the association rule is pushed to a target user in a directional mode according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirement, so that mining precision is improved, and pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
Another technical solution of the present invention for solving the above technical problems is as follows:
an electronic device comprising a memory, a processor and a program stored on the memory and running on the processor, wherein the processor executes the program to implement a flight pushing method according to any one of the above aspects.
The invention has the beneficial effects that: the association rule of the user behavior data and the flight decision requirement is obtained through a mining algorithm, the association rule is pushed to a target user in a directional mode according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirement, so that mining precision is improved, and pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
Drawings
Fig. 1 is a schematic flow chart of a flight pushing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the correlation between flight decision requirements and the user behavior data according to another embodiment of the present invention;
FIG. 3 is a diagram illustrating a multi-level relationship of influencing factors according to another embodiment of the present invention;
fig. 4 is a schematic flow chart of a flight pushing method for increasing an influence factor according to another embodiment of the present invention;
FIG. 5 is a schematic flow chart of calculating an impact factor according to another embodiment of the present invention;
fig. 6 is a schematic structural diagram of a flight pushing system according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 1, a flight pushing method provided for an embodiment of the present invention includes:
s1, acquiring all user behavior data and all flight data of flights to be pushed; all user behavior data can be understood as mining the existing user behavior data as much as possible, and the more and more fully mined association rules are more accurate.
S2, setting flight decision requirements according to the flights to be pushed;
s3, mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm;
it should be noted that, in a certain embodiment, an influence factor related to the flight decision requirement may be calculated from the user behavior data by a preset mining method; wherein the influence factor is equivalent to an important relevant factor when obtaining flight decisions made by customers. The user behavior data includes: multi-source data; the association of flight decision requirements with the user behavior data is shown in fig. 2, where the multi-source data may include consumption data, web browsing data, query data, travel data, and the like.
The process of calculating the influence factor may include: performing fusion classification on the multi-source data; in one embodiment, a k-means spatial clustering analysis method may be employed to classify the multi-source data types.
Establishing an incidence relation between the fused and classified multi-source data and the flight decision requirement, and quantifying the incidence relation through distance entropy;
performing fusion calculation according to the quantified incidence relation to obtain the distance entropy between the flight decision demand and the multi-source data;
and when the distance entropy meets a preset condition, the multi-source data corresponding to the current distance entropy is the influence factor. Wherein the preset condition may include: the total entropy of the fusion system is minimal, and the information content is maximized.
And mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm and the influence factors. The preset mining algorithm may include: the Apriori classical association rule mining algorithm generates a candidate set based on Apriori properties, and greatly compresses the size of a frequent item set.
In a certain embodiment, before mining the association rule, the method further comprises:
carrying out fine-grained division on the influence factors according to corresponding weight coefficients to obtain the influence factors after weight distribution;
and dividing the influence factors into multiple layers according to types to obtain the influence factors of multiple levels.
In one embodiment, mining of travel plan behaviors built in angles of time sequences, behavior space maps and the like can be constructed in multi-source data type classification, fine-grained classification of user behaviors is carried out by introducing a travel plan behavior event sequence and weighted values of behavior space map parameters, and a mode of classifying the user behaviors in a first layer, a second layer and even a third layer is adopted during classification, so that different types of granularities are more accurate, and mapping among multiple parameter dimensions can be carried out during clustering. The schematic diagram of the multilayer relationship of the impact factors is shown in fig. 3, and the impact factors may include two-layer type division, as shown in fig. 3, or more than three layers; the first layer of impact factor-related user behavior data may include: consumption data, web browsing data, query data, travel data and the like; a second layer: the consumption data may include: travel group consumption and ticket consumption, etc.; the web browsing data may include: air ticket browsing information, scenic spot browsing information, travel strategy browsing information and the like; querying the data may include: location query information, scenery spot query information, geographical query information, and the like; travel data may include on-board vehicle travel, and the like.
The method can also comprise the following steps: and a third layer: for example, the location query information may include: the method comprises the following steps of short-distance place inquiry and long-distance place inquiry, wherein the near distance and the far distance can be defined by whether an air route passes through or not.
The method can also comprise the following steps: and respectively carrying out association mining on the influence factors of multiple levels and the flight decision requirement through an Apriori algorithm to obtain an association rule of multiple levels.
And S4, acquiring the flight to be pushed associated with the behavior data of the target user according to the association rule, and pushing the flight to be pushed to the target user.
The association rule of the user behavior data and the flight decision requirement is obtained through a mining algorithm, the association rule is pushed to a target user in a directional mode according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirement, so that mining precision is improved, and pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
Preferably, in any of the above embodiments, as shown in fig. 4, the method further includes:
s30, calculating influence factors related to the flight decision requirements from the user behavior data through a preset mining method;
the S3 specifically includes: and mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm and the influence factors.
By obtaining important relevant factors, namely influence factors, when the client makes flight decisions, mining calculation of non-relevant data is reduced, the mining matching degree is high, and the mining precision and efficiency are improved.
Preferably, in any of the above embodiments, as shown in fig. 5, the user behavior data includes: multi-source data;
the S30 specifically includes:
performing fusion classification on the multi-source data;
establishing an incidence relation between the fused and classified multi-source data and the flight decision requirement, and quantifying the incidence relation through distance entropy;
performing fusion calculation according to the quantified incidence relation to obtain the distance entropy between the flight decision demand and the multi-source data;
and when the distance entropy meets a preset condition, the multi-source data corresponding to the current distance entropy is the influence factor.
According to the scheme, the depth association and fusion are carried out on the multi-source data and the decision-making requirement according to the association degree and the trust degree; based on the process of continuously shortening the distance between multi-source data and decision requirements in the characteristic fusion process, the correlation quantification of the distance entropy is carried out, the stability of promoting fusion by the optimal distance is realized, the total entropy of the fusion system is minimized, and the information amount is maximized.
The traditional data combing mode adopts manual classification and other modes, and because the relevance and non-logical processing amount among various data sources exceeds the manual processing capacity, the optimal data fusion can be realized by fusing classification and deep relevance according to the scheme, and meanwhile, the most reasonable information amount is ensured.
Preferably, in any of the above embodiments, further comprising: the distance entropy is calculated by:
the formula for calculating the distance between the optimal set and other sets is as follows:
wherein the multi-source data and flight decision requirements are in m sets, each set having n knowledge elements, Oj *The optimal value of each set, i.e. the optimal value in the j-th set, j being 1, 2.., m; o isijInformation element values representing the jth and ith sets, i 1, 2.., n;
the formula for calculating the distance entropy of the ith set is as follows:
in one embodiment, the clue data obtained according to the clustering algorithm is represented by a knowledge element, and the clue data is represented by fused multi-source knowledge data according to a granularity principle, and is described by associating the multi-source knowledge data with the concept, attribute and attribute of the decision-making demand object according to the granularity principle. Performing knowledge element representation Om ═ (Cm, Am, Rm, BFm) on the multi-source data object, wherein Cm is a concept and attribute set of the object; am is a keyword set after extraction; rm represents associations and mappings with other data sources and decision requirements; BFm, the confidence level of the data is represented, the optional multi-source data and the decision-making requirement are deeply associated and fused according to the association level and the confidence level, and the relationship among the keywords can be causal, sequential, following, concurrent, mutually exclusive and spatial.
Associative quantization based on distance entropy. The fusion process based on the characteristics is a process of continuously zooming in multi-source data and deciding the distance between demands, and the stability of the fusion is promoted through the optimal distance, so that the total entropy of the fusion system is minimum, and the information amount is maximized.
And (3) performing association between the multi-source data and the decision requirement according to the distance entropy, wherein the greater the distance entropy is, the lower the similarity between the representation sets is, optionally performing semantic association and fusion by combining the trust attribute of the multi-source data and the distance entropy to form a semantic association diagram of the multi-source data and the decision requirement.
Optionally, before executing fusion detection, a decision requirement set is constructed, and the decision requirement set is divided according to decision requirements.
According to the scheme, the depth association and fusion are carried out on the multi-source data and the decision-making requirements according to the association degree and the trust degree through the distance entropy.
Preferably, in any of the above embodiments, further comprising:
carrying out fine-grained division on the influence factors according to corresponding weight coefficients to obtain the influence factors after weight distribution;
and dividing the influence factors into multiple layers according to types to obtain the influence factors of multiple levels.
In a certain embodiment, dividing the impact factors according to weight coefficients may include: the first layer of impact factor-related user behavior data may include: consumption data, web browsing data, query data, travel data and the like; a second layer: the consumption data may include: travel group consumption and ticket consumption, etc.; the web browsing data may include: air ticket browsing information, scenic spot browsing information, travel strategy browsing information and the like; querying the data may include: location query information, scenery spot query information, geographical query information, and the like; travel data may include on-board vehicle travel, and the like. The first layer data and the second layer data are respectively subjected to weight distribution, for example, the first layer consumption data, the web browsing data, the query data, the trip data, and the like are subjected to weight distribution, and the distribution result may be: consumption data: 0.1; and web page browsing data: 0.1; and (3) inquiring data: 0.1; and (3) trip data: 0.1; in the second layer of query data: and (3) inquiring the information of the place: 0.2; and (3) inquiring information of the scenic spots: 0.2; and geographic query information: 0.2; in the third layer of location query information: short-distance location query: 0.01; and remote location query: 0.4. a weight coefficient can be set according to the importance of the influence factor relative to the flight decision requirement;
in one embodiment, by combining different types of influence factors in multiple layers, and when the weight coefficient score meets a certain condition, it can be considered that the travel demand of the customer making the same type of behavior and the flight decision demand corresponding to the association rule have high wedging degree, and the corresponding flight can be pushed to the customer. The certain condition can be that which behavior operations the user will do before the flight are selected according to the historical consumption information of the user, the set of the behavior operations can be used as the necessary condition for the user to do when the user selects the flight, the weight information is set for each behavior operation according to the necessary degree, and the weight score is calculated. For example, each time the user makes a type a operation before selecting flight H, then a1, a2 operations under type a, B type operations, B1 operations under type B, C2 operations under type C, so that the customer selects flight H, the necessary actions include: A. b, C, a1, a2, b1 and c2, the final score is calculated for each operation according to the respective weight information.
In the existing scheme, multi-level influence factors are constructed from a large amount of non-granularity and non-level data, compared with the method of directly mining a large amount of data, the scheme can improve the support degree of an item set in association rule mining through a multi-level relation.
Preferably, in any of the above embodiments, further comprising: and calculating to obtain the weight coefficient according to the importance of the influence factor relative to the flight decision requirement.
According to the scheme, the influence factors are divided through the weight coefficients, and the hierarchy and category division is more accurate.
Preferably, in any of the above embodiments, the S3 specifically includes:
and respectively carrying out association mining on the influence factors of multiple levels and the flight decision requirement through an Apriori algorithm to obtain an association rule of multiple levels.
The existing user behavior data comprises a large amount of non-granularity and non-hierarchy data, a frequent item set is obtained through influence factors after multi-hierarchy and fine-granularity processing by using an Apriori algorithm in an iterative mode, the item set which does not meet the minimum support degree is filtered, and the potential association rule can be mined.
Preferably, in any of the above embodiments, further comprising: the user behavior data and the flight data of the flight to be pushed are obtained through a crawler tool,
the crawler tool comprises: concurrent crawler tools in master-slave mode for the master node and the slave nodes; the master node is used for maintaining a queue to be crawled of the whole crawler and task allocation work, and the slave node is used for receiving a master node delegation task;
each slave node maintains a task queue and a new link queue in real time, and after the slave nodes complete the task queues, the new link queues of the slave nodes are merged into the queue to be climbed of the master node;
and the master node continuously dispatches the links of the queue to be crawled to all the slave nodes, and the slave nodes continuously crawl all new user behavior data and all flight data of the flights to be pushed.
Due to the fact that the user behavior data and the flight data are large in quantity, the crawler of the single process is difficult to meet the requirement of fast crawling a large amount of data. By means of the concurrent data crawling function, user behavior data and flight data can be directly obtained from a large amount of data; the data crawling efficiency is improved.
In one embodiment, as shown in fig. 6, a flight pushing system includes: the system comprises a multi-source data acquisition module 11, a configuration module 12, a mining module 13 and a pushing module 14;
the multi-source data acquisition module 11 is configured to acquire all user behavior data and all flight data of flights to be pushed;
the configuration module 12 is configured to set flight decision requirements according to flights to be pushed;
the mining module 13 is configured to mine association rules between the user behavior data and the flight decision requirements according to a preset mining algorithm;
the pushing module 14 is configured to obtain a flight to be pushed associated with the target user behavior data according to the association rule, and push the flight to be pushed to the target user.
The association rule of the user behavior data and the flight decision requirement is obtained through a mining algorithm, the association rule is pushed to a target user in a directional mode according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirement, so that mining precision is improved, and pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
Preferably, in any of the above embodiments, further comprising: the influence factor acquisition module is used for calculating influence factors related to the flight decision requirement from the user behavior data through a preset mining method;
the mining module 13 is specifically configured to mine association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm in combination with the influence factors.
Preferably, in any of the above embodiments, the user behavior data includes: multi-source data;
the influence factor acquisition module is specifically used for carrying out fusion classification on the multi-source data;
establishing an incidence relation between the fused and classified multi-source data and the flight decision requirement, and quantifying the incidence relation through distance entropy;
performing fusion calculation according to the quantified incidence relation to obtain the distance entropy between the flight decision demand and the multi-source data;
and when the distance entropy meets a preset condition, the multi-source data corresponding to the current distance entropy is the influence factor.
Preferably, in any of the above embodiments, the influence factor obtaining module is further specifically configured to calculate the distance entropy by:
the formula for calculating the distance between the optimal set and other sets is as follows:
wherein the multi-source data and flight decision requirements are in m sets, each set having n knowledge elements, Oj *The optimal value of each set, i.e. the optimal value in the j-th set, j being 1, 2.., m; o isijInformation element values representing the jth and ith sets, i 1, 2.., n;
the formula for calculating the distance entropy of the ith set is as follows:
preferably, in any of the above embodiments, further comprising: the multi-level division module is used for performing fine-grained division on the influence factors according to the corresponding weight coefficients to obtain the influence factors after weight distribution;
and dividing the influence factors into multiple layers according to types to obtain the influence factors of multiple levels.
Preferably, in any of the above embodiments, further comprising: and the weight coefficient calculation module is used for calculating and obtaining the weight coefficient according to the importance of the influence factor relative to the flight decision requirement.
Preferably, in any of the above embodiments, the mining module 13 is specifically configured to perform association mining on the influence factors of multiple levels and the flight decision requirement through an Apriori algorithm, so as to obtain multiple levels of association rules.
Preferably, in any of the above embodiments, further comprising: the data acquisition module is used for acquiring the user behavior data and the flight data of the flight to be pushed through the crawler tool,
the crawler tool comprises: concurrent crawler tools in master-slave mode for the master node and the slave nodes; the master node is used for maintaining a queue to be crawled of the whole crawler and task allocation work, and the slave node is used for receiving a master node delegation task;
each slave node maintains a task queue and a new link queue in real time, and after the slave nodes complete the task queues, the new link queues of the slave nodes are merged into the queue to be climbed of the master node;
and the master node continuously dispatches the links of the queue to be crawled to all the slave nodes, and the slave nodes continuously crawl all new user behavior data and all flight data of the flights to be pushed.
In an embodiment, an electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, and the processor executes the program to implement a flight pushing method as described in any one of the above embodiments.
The association rule of the user behavior data and the flight decision requirement is obtained through a mining algorithm, the association rule is pushed to a target user in a directional mode according to the association rule, and the potential requirements of the user can be associated through the user behavior data and the flight decision requirement, so that mining precision is improved, and pushing is more accurate. The preferential flight pushing is carried out according to the potential requirements in the user behavior data, the benign connection with the user is established, the pushing effect is good, and the customer dislike cannot be caused.
It is understood that some or all of the alternative embodiments described above may be included in some embodiments.
It should be noted that the above embodiments are product embodiments corresponding to the previous method embodiments, and for the description of each optional implementation in the product embodiments, reference may be made to corresponding descriptions in the above method embodiments, and details are not described here again.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A flight pushing method, comprising:
s1, acquiring all user behavior data and all flight data of flights to be pushed;
s2, setting flight decision requirements according to the flights to be pushed;
s3, mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm;
and S4, acquiring the flight to be pushed associated with the behavior data of the target user according to the association rule, and pushing the flight to be pushed to the target user.
2. The flight pushing method according to claim 1, further comprising:
s30, calculating influence factors related to the flight decision requirements from the user behavior data through a preset mining method;
the S3 specifically includes: and mining association rules of the user behavior data and the flight decision requirements according to a preset mining algorithm and the influence factors.
3. The method of claim 2, wherein the user behavior data comprises: multi-source data;
the S30 specifically includes:
performing fusion classification on the multi-source data;
establishing an incidence relation between the fused and classified multi-source data and the flight decision requirement, and quantifying the incidence relation through distance entropy;
performing fusion calculation according to the quantified incidence relation to obtain the distance entropy between the flight decision demand and the multi-source data;
and when the distance entropy meets a preset condition, the multi-source data corresponding to the current distance entropy is the influence factor.
4. A flight pushing method according to claim 3, further comprising: the distance entropy is calculated by:
the formula for calculating the distance between the optimal set and other sets is as follows:
wherein the multi-source data and flight decision requirements are in m sets, each set having n knowledge elements, Oj *The optimal value of each set, i.e. the optimal value in the j-th set, j being 1, 2.., m; o isijInformation element values representing the jth and ith sets, i 1, 2.., n;
the formula for calculating the distance entropy of the ith set is as follows:
5. a flight pushing method according to claim 2 or 3, further comprising:
carrying out fine-grained division on the influence factors according to corresponding weight coefficients to obtain the influence factors after weight distribution;
and dividing the influence factors into multiple layers according to types to obtain the influence factors of multiple levels.
6. The flight pushing method according to claim 5, further comprising: and calculating to obtain the weight coefficient according to the importance of the influence factor relative to the flight decision requirement.
7. The flight pushing method according to claim 5 or 6, wherein the S3 specifically includes:
and respectively carrying out association mining on the influence factors of multiple levels and the flight decision requirement through an Apriori algorithm to obtain an association rule of multiple levels.
8. The flight pushing method according to claim 1, further comprising:
the user behavior data and the flight data of the flight to be pushed are obtained through a crawler tool,
the crawler tool comprises: concurrent crawler tools in master-slave mode for the master node and the slave nodes; the master node is used for maintaining a queue to be crawled of the whole crawler and task allocation work, and the slave node is used for receiving a master node delegation task;
each slave node maintains a task queue and a new link queue in real time, and after the slave nodes complete the task queues, the new link queues of the slave nodes are merged into the queue to be climbed of the master node;
and the master node continuously dispatches the links of the queue to be crawled to all the slave nodes, and the slave nodes continuously crawl all new user behavior data and all flight data of the flights to be pushed.
9. A flight pushing system, comprising: the system comprises a multi-source data acquisition module, a configuration module, a mining module and a pushing module;
the multi-source data acquisition module is used for acquiring all user behavior data and all flight data of flights to be pushed;
the configuration module is used for setting flight decision requirements according to flights to be pushed;
the mining module is used for mining association rules of user behavior data and flight decision requirements according to a preset mining algorithm;
and the pushing module is used for acquiring the flight to be pushed associated with the behavior data of the target user according to the association rule and pushing the flight to be pushed to the target user.
10. An electronic device comprising a memory, a processor and a program stored on the memory and running on the processor, wherein the processor implements a flight pushing method according to any one of claims 1 to 8 when executing the program.
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