CN113946755A - Information pushing method, device and equipment based on association rule and storage medium - Google Patents
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Abstract
The method comprises the steps of determining combinations with the support degree exceeding a first threshold value in each combination corresponding to each historical product record as frequent item sets, and determining frequent item sets with the confidence degree exceeding a second threshold value in the frequent item sets as target frequent item sets; determining association rules between products and/or between user information and products; and completing the pushing of the target product information corresponding to the target user based on the association rule. According to the method and the device, the association rules among the products and/or between the user information and the products are mined according to the support degrees of each combination, namely the combinations among the products and the combinations between the user information and the products, the target product information which is correspondingly associated with the target user is pushed, the accuracy of the pushed information is improved, the matching degree of the pushed information and the user is improved, and the user experience is improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an information pushing method, an information pushing device, information pushing equipment, and a computer-readable storage medium based on association rules.
Background
The product information pushing refers to pushing product information suitable for the user to the user for selection. The current product recommendation method generally determines similar users based on the basic information of the users, and then carries out product recommendation based on products related to the similar users. Therefore, the relevance between products recommended by the current product recommendation method is low, and the accuracy of recommended product information is low. Therefore, how to solve the problem of low accuracy of the conventional information push becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The invention mainly aims to provide an information pushing method, an information pushing device, information pushing equipment and a computer readable storage medium based on association rules, and aims to solve the technical problem of low accuracy rate of the existing information pushing.
In order to achieve the above object, the present invention provides an information pushing method based on association rules, where the information pushing method based on association rules includes: respectively acquiring each historical product record corresponding to each user information, determining a combination with the support degree exceeding a first threshold value in each combination corresponding to each historical product record as a frequent item set, and determining a frequent item set with the confidence degree exceeding a second threshold value in the frequent item set as a target frequent item set; determining association rules among products and/or between user information and the products according to the target frequent item set; acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on a related rule between the products and/or a related rule between the user information and the products; and determining target product information in the associated product information, and pushing the target product information to the target user.
In addition, in order to achieve the above object, the present invention further provides an association rule-based information pushing apparatus, including: a frequent item set generating module, configured to obtain each historical product record corresponding to each user information, determine, in each combination corresponding to each historical product record, a combination with a support degree exceeding a first threshold as a frequent item set, and determine, in the frequent item set, a frequent item set with a confidence degree exceeding a second threshold as a target frequent item set; the association rule determining module is used for determining association rules among products and/or between user information and the products according to the target frequent item set; the related product determining module is used for acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on a related rule between the products and/or a related rule between the user information and the products; and the target information pushing module is used for determining target product information in the associated product information and pushing the target product information to the target user.
In addition, to achieve the above object, the present invention further provides an association rule based information pushing device, which includes a processor, a memory, and an association rule based information pushing program stored on the memory and executable by the processor, wherein when the association rule based information pushing program is executed by the processor, the steps of the association rule based information pushing method as described above are implemented.
In addition, to achieve the above object, the present invention further provides a computer readable storage medium, which stores an association rule based information pushing program, wherein when the association rule based information pushing program is executed by a processor, the steps of the association rule based information pushing method as described above are implemented.
The invention provides an information pushing method based on association rules, which respectively acquires each historical product record corresponding to each user information, determines a combination with the support degree exceeding a first threshold value in each combination corresponding to each historical product record as a frequent item set, and determines a frequent item set with the confidence degree exceeding a second threshold value in the frequent item set as a target frequent item set; determining association rules among products and/or between user information and the products according to the target frequent item set; acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on a related rule between the products and/or a related rule between the user information and the products; and determining target product information in the associated product information, and pushing the target product information to the target user. Through the mode, according to the support degrees of all the combinations, namely the combinations among the products and the combinations between the user information and the products, association rules among the products and/or between the user information and the products are mined, then the target product information which is correspondingly associated with the target user information or the related product information corresponding to the target user is determined based on the mined association rules, and the target product information is pushed, so that the information pushing accuracy is improved, the matching degree of the pushed information and the user is improved, the user experience is improved, and the technical problem of low information pushing accuracy in the prior art is solved.
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Fig. 1 is a schematic hardware structure diagram of an association rule-based information pushing device according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first embodiment of an association rule-based information pushing method according to the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of an association rule-based information pushing method according to the present invention;
FIG. 4 is a flowchart illustrating a third embodiment of an association rule-based information pushing method according to the present invention;
fig. 5 is a functional block diagram of an information pushing apparatus based on association rules according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The information pushing method based on the association rule is mainly applied to information pushing equipment based on the association rule, and the information pushing equipment based on the association rule can be equipment with display and processing functions, such as a PC (personal computer), a portable computer, a mobile terminal and the like.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of an information push apparatus based on association rules according to an embodiment of the present invention. In the embodiment of the present invention, the information pushing apparatus based on the association rule may include a processor 1001 (e.g., a CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used for realizing connection communication among the components; the user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface); the memory 1005 may be a high-speed RAM memory, or may be a non-volatile memory (e.g., a magnetic disk memory), and optionally, the memory 1005 may be a storage device independent of the processor 1001.
Those skilled in the art will appreciate that the hardware configuration shown in fig. 1 does not constitute a limitation of the association rule based information pushing apparatus, and may include more or less components than those shown, or combine certain components, or arrange different components.
With continued reference to fig. 1, the memory 1005 of fig. 1, which is a computer-readable storage medium, may include an operating system, a network communication module, and an information push program based on association rules.
In fig. 1, the network communication module is mainly used for connecting to a server and performing data communication with the server; and the processor 1001 may call the association rule based information push program stored in the memory 1005 and execute the association rule based information push method provided by the embodiment of the present invention.
The embodiment of the invention provides an information pushing method based on association rules.
Referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of an information pushing method based on association rules according to the present invention.
In this embodiment, the information pushing method based on association rules includes the following steps:
step S10, respectively acquiring each historical product record corresponding to each user information, determining a combination with the support degree exceeding a first threshold value in each combination corresponding to each historical product record as a frequent item set, and determining a frequent item set with the confidence degree exceeding a second threshold value in the frequent item set as a target frequent item set;
in the data mining process, the mining purpose of a data Association Rule (Association Rule) is to mine that there is an Association relationship between a large number of data items. For example, in the financial industry, the business needs of users are predicted through data association rules between financial products. In the retail field, a user purchases a product by analyzing a data association rule between products, and determines whether or not a combination sale between the products is possible.
In this embodiment, in order to solve the problem of low accuracy of the existing information push, an information push method based on association rules is provided, that is, association rules between products or association rules between user information and products are mined based on historical product records of users, so that products associated with target users are determined, and the push of product information is completed. Therefore, the matching degree between the user and the product information is improved, the accuracy of information pushing is improved, and the user experience is improved.
Specifically, the user information includes, but is not limited to, the age, occupation, academic calendar, specialty, financial preference information, and the like of the user, and the historical product records corresponding to the user include, but are not limited to, each piece of user information and product information purchased, collected, clicked, and forwarded by the user corresponding to each piece of user information.
Illustratively, the information pushing method is applied to a distributed file system (HDFS), where the distributed file system includes at least two working nodes, and the step S10 specifically includes:
respectively acquiring each historical product record corresponding to each user information as information to be mined;
when the data volume corresponding to the information to be mined exceeds a preset threshold value, partitioning the information to be mined according to preset partition units;
and distributing the partitioned information to be mined to each working node in the distributed file system, so as to perform parallel frequent itemset mining on the information to be mined through each working node and generate a target frequent itemset.
The Apriori algorithm is used as a traditional data mining algorithm, and when massive historical product record data are faced, the Apriori algorithm in a single machine environment has low mining efficiency. In order to solve the above problems, the present proposal further combines the traditional Apriori algorithm with a big data platform, and utilizes MapReduce idea to process a large amount of data, thereby obtaining a processing result. Thereby addressing the short boards where conventional algorithms cannot handle large amounts of data. The MapReduce is a programming model and is used for parallel operation of large-scale data sets (larger than 1 TB). The MapReduce parallel computing framework includes a Map function (for mapping a set of key-value pairs into a new set of key-value pairs) and a Reduce function (for ensuring that each of all mapped key-value pairs share the same key-set).
In this embodiment, association rule mining is performed on each historical product record purchased by each user, that is, association relationships between products are mined according to products purchased by the users at the same time, so as to determine a product associated with the target user. The traditional Apriori algorithm is transplanted to a Hadoop (distributed system infrastructure) platform through a MapReduce parallel computing framework. The Hadoop platform comprises at least two working nodes, the Hadoop platform divides large-scale data, sends the divided data to different working nodes respectively, and collects results after the working nodes are mined in parallel.
After acquiring the historical product record data associated with each user, the control node divides the data according to the data size, respectively sends the tasks and the divided data to each working node, and performs frequent item set mining on the received historical product record through each node. The frequent item set mining comprises the steps of generating candidate item sets corresponding to the historical product records through a mapping function (Map function) and combining the candidate item sets through a reduction function (Reduce function) to generate frequent item sets.
The working nodes comprise at least two map nodes and at least two reduce nodes.
For example:
the first product record T1X 1, P1, P2, P4
The second product record T2X 2, P2, P3, P4
The third product record T3X 3, P1, P3, P5
The fourth product record T4X 4, P1, P3, P6
T1, T2, T3 and T4 are 4 historical product records corresponding to 4 users (X1, X2, X3 and X4), P1, P2, P3, P4, P5 and P6 are 6 different product information, 4 records from T1 to T4 are distributed to different nodes, for example, two nodes exist, a first product record T1 and a second product record T2 are processed on map1, and a third product record T3 and a fourth product record T4 are processed by map 2.
The Map function traverses each file on the Map node, splits each line of data in the file, and the split data form key-value pairs of key and value, such as < P1, (T1) > (P1 appears 1 time in T1 records) or < P1, (T3, T4) > (P1 appears 2 times in T3 and T4 records), that is, key-value pairs consisting of the product and the record in which the product appears.
And each map node sends the key value pair of each key and value to each reduce node, and the reduce nodes respectively count the times corresponding to each product and summarize the statistical results. And then taking the ratio of the corresponding times of each product to the number of the recorded products as the support degree of each product. And generating a first frequent item set according to the products exceeding the preset support threshold. That is, the map stage will send the data with the same key value in the processed result to the same reduce node. And combining the key value pairs with the same key value, then transmitting the combined key value pairs serving as the input of a Reduce stage into a Reduce function, and executing a Reduce program to summarize < key, value > to obtain a frequent item set. For example, there are two reduce nodes at this time, reduce1 and reduce2, map1 sends the generated key-value pair < P1, (T1) > to reduce1, map2 sends the generated key-value pair < P1, (T3, T4) > to reduce1, and at this time, reduce1 may obtain < P1, (T1, T3, T4) > key-value pairs. I.e., P1 appears 3 times in the entire recording. If the minimum support threshold is set to 0.3 at this time, P1 satisfies the minimum support (the minimum support of P1 is 3/4-0.75, and 0.75 is greater than the minimum support threshold), P2 (the minimum support of P2 is 2/4-0.5, and 0.5 is equal to the minimum support threshold), P3 (the minimum support of P3 is 3/4-0.75, and 0.75 is greater than the minimum support threshold), P4 (the minimum support of P4 is 2/4-0.5, and 0.5 is equal to the minimum support threshold), P5 (the minimum support of P5 is 1/4-0.25, and 0.25 is less than the minimum support threshold), P6 (the minimum support of P6 is 1/4-0.25, and 0.25 is less than the minimum support threshold), that is the first support of P1, P4, and P4624.
It can be understood that, in the present embodiment, the association rule is measured by the support degree and the confidence degree of the combination, the support degree is used to determine the frequency degree of the combination, and the calculation formula is:
s (Pi → Pj) ═ the number of times Pi and Pj products appear simultaneously in each product record/total number of product records, where S (Pi → Pj) is the support degree of the simultaneous appearance of Pi and Pj products;
the confidence is used to determine how frequently Pj occurs in the records containing Pi products, and is calculated by:
c (Pi → Pj) — the number of times Pi and Pj products appear simultaneously in each product record/the number of times Pi appears in each product record.
Combining every item in the first frequent item set in pairs to obtain the minimum support degree after combining in pairs, such as < (P1, P2), T1>, (P1, P2) the minimum support degree is 1/4 which is 0.25 and is less than the minimum support degree threshold value,
< (P1, P3), (T3, T4) > (P1, P3) minimum support is 2/4-0.5, greater than the minimum support threshold, < (P2, P3), T2>, (P2, P3) minimum support is 1/4-0.25, less than the minimum support threshold, < (P3, P4), T2>, (P3, P4) minimum support is 1/4-0.25, less than the minimum support threshold, i.e. the second set of cross includes (P1, P3). The Reduce phase counts key-value pairs for the same key. And if the minimum support degree is met, retaining the result. Otherwise, deleting the item set to obtain a second frequent item set, and storing the result to the HDFS. If a plurality of items exist in the second frequent item set, repeating the operation (continuously iterating), namely when the frequent k +1 item set is generated, the support degree of the frequent k +1 item set is smaller than a preset support degree threshold value, and the iteration is not performed any more. And calculating the confidence coefficient of any one frequent item subset in the frequent k item sets, and taking the frequent item subset with the confidence coefficient not less than a preset confidence coefficient threshold value as a target frequent item set.
Step S20, determining association rules among products and/or between user information and products according to the target frequent item set;
in this embodiment, mining is performed in advance for an association relationship between user information (such as age, occupation, academic calendar, professional and/or financial management preference information) and a product, or mining is performed in advance for an association relationship between a product and a product, or mining is performed for an association relationship between user information and product information and a product associated with the user information, and a relationship between the user information and product information having an association relationship therewith, or a relationship between products having an association relationship therewith, or a relationship between the user information and product information having an association relationship therewith is recorded as an association rule. Determining association rules among products according to the frequent item sets among the products, such as determining P1- > P3 according to the frequent item sets (P1, P3), namely, when a user purchases, collects or shares P1, taking P3 as an associated product of P1; or determining association rules between the user information and the products according to the frequent item set between the user information and the products, such as (I1, P1), namely when the user belongs to the I1 industry, taking P1 as the associated product of the user; or determining association rules between the user information and the products, such as < (I1, P1) and P2>, according to the user information and the frequent item set between the product common and the products, namely, when the user belongs to the I1 industry and the user purchases, collects or shares P1, taking P2 as the associated product of the user. Thereby, association rules between products and/or association rules between user information and products are determined.
Step S30, acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on the association rules between the products and/or the association rules between the user information and the products;
in this embodiment, target user information of a target user and related product information associated with the target user are obtained, including but not limited to product information purchased, collected, shared, or complied by the target user. And then determining associated product information corresponding to the target user (namely the target user information and/or the related product information) according to an association rule between products and/or an association rule between user information and products.
And step S40, determining target product information in the associated product information, and pushing the target product information to the target user.
In this embodiment, the associated product information includes product information determined based on the target user information, product information determined based on the related product information, and/or product information determined based on both the target user information and the related product information. Any one of the product information and the combination thereof can be used as target product information and pushed to the target user to complete the pushing of the product information.
The embodiment provides an information pushing method based on association rules, which respectively acquires each historical product record corresponding to each user information, determines a combination with a support degree exceeding a first threshold value in each combination corresponding to each historical product record as a frequent item set, and determines a frequent item set with a confidence degree exceeding a second threshold value in the frequent item set as a target frequent item set; determining association rules among products and/or between user information and the products according to the target frequent item set; acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on a related rule between the products and/or a related rule between the user information and the products; and determining target product information in the associated product information, and pushing the target product information to the target user. Through the mode, according to the support degrees of all the combinations, namely the combinations among the products and the combinations between the user information and the products, association rules among the products and/or between the user information and the products are mined, then the target product information which is correspondingly associated with the target user information or the related product information corresponding to the target user is determined based on the mined association rules, and the target product information is pushed, so that the information pushing accuracy is improved, the matching degree of the pushed information and the user is improved, the user experience is improved, and the technical problem of low information pushing accuracy in the prior art is solved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a second embodiment of an information pushing method based on association rules according to the present invention.
Based on the foregoing embodiment shown in fig. 2, in this embodiment, the step S40 specifically includes:
step S41, determining, in the associated product information, target product information corresponding to the target user based on the target user representation corresponding to the target user, and pushing the target product information to the target user.
In the embodiment, the user portrait is constructed by extracting historical behavior data of the user and marking a personal tag for the user by combining basic data of the user. User data can be classified into explicit feature data and implicit feature data according to data types. The display characteristic data type refers to personal information data actively submitted by a user, such as data of age, birthday, address, telephone, family composition, score, character evaluation and the like; the implicit characteristic data refers to user behavior information data recorded by the server, such as user browsing, searching, clicking, collecting and purchasing behaviors and the like. And performing relevant processing on the collected data to construct an interest model of the user. The user interest model is a commodity list which can reflect the interest preference of the customer and is calculated according to the historical behaviors of the user, such as sharing, clicking, collecting, purchasing and the like. The tags are normalized based on the basic attributes, social preferences, RFM analysis and the like of the user to abstract out the personal tags of the user, and the user tags are usually expressed in a multi-level tag mode. The user portrait refers to a labeling model of the user, and the user interest model refers to a calculated user preference product list. And determining products which conform to the user portrait label in the associated products or determining products which are matched with the user preference product list in the associated products as target product information.
Further, before the step S41, the method further includes:
acquiring target behavior data of the target user, and calculating the conformity of the target user and the product based on a first calculation formula, the similarity between products and the target behavior data, wherein the first calculation formula is as follows:
wherein N (u) is a scoring product set of the target user, S (j, k) is k products with the maximum similarity to the product j, i is a repeated product of the scoring product set and the k products, and W is a repeated product of the scoring product set and the k productsijIs the similarity of products i and j, ruiScoring a product i for the target user u;
and constructing a user portrait of the target user based on the target user information and the conformity of the target user and the product.
In this embodiment, the score of the product operated by the user in a behavior may be obtained from two channels: the method comprises the steps of directly scoring the product by the user, and calculating the interest score of the user on the product through the operation behavior of the user on the product. And (3) correspondingly weighting and scoring different user behaviors, and adding the weights of all the behaviors (namely, the behaviors of sharing, clicking, collecting, searching and the like of the user within a preset time period, such as one month or one week) of the product by the user to serve as the interest score of the user on the product. After the user purchases, the interest score of the user is subtracted from the purchase weight of the user, the interest score of the user on the product is reduced, and the product purchased by the user is not recommended. Therefore, the similarity between the products and the grade of the target user on the products are substituted into the formula, the conformity of the target user and the products is calculated, and the interest products of the target user are determined. The product j can be a product shared, clicked, collected and searched by the target user. And adding corresponding labels on the basis of the interest products in combination with basic information of the target user such as age, occupation, academic calendar, specialty and the like, thereby completing the user portrait construction of the target user. The similarity between the products can be preset, or the similarity between the products is calculated based on a second calculation formula, wherein the first calculation formula is as follows:
wherein, | N (i) | is the number of persons who purchase product i, | N (j) | is the number of persons who purchase product j, | N (i) # N (j) | is the number of persons who purchase product i and product j.
The embodiment combines the association rule with the user portrait, further excavates products associated with the user, and pushes product information according to the label of the user, so that the accuracy of information pushing is improved, and the user experience is improved.
Referring to fig. 4, fig. 4 is a flowchart illustrating a third embodiment of an information pushing method based on association rules according to the present invention.
Based on the foregoing embodiment shown in fig. 3, in this embodiment, the step S40 further includes:
step S42, when the associated product information is first product information associated with the target user information and the related product information, taking the first product information as the target product information, and pushing the target product information to the target user.
In this embodiment, when the associated product information is product information in which the user information and the related product information are associated together, that is, the associated product information is one type of product information, all of the associated product information may be used as the target product information. For example < (I1, P1), P2>, namely when the user belongs to the I1 industry and the user purchases, collects or shares P1, the P2 is taken as the associated product of the user.
Further, the step S40 further includes:
and when the associated product information comprises second product information associated with the target user information and third product information associated with the related product information, taking repeated product information between the second product information and the third product information as the target product information, and pushing the target product information to the target user. P1- > P3 is determined according to the frequent item set (P1, P3), namely P3 is taken as a related product of P1 when a user purchases, collects or shares P1; or determining an association rule between the user information and the product according to a frequent item set between the user information and the product, such as (I1, P1), namely, when the user belongs to the I1 industry, taking the P1 as the associated product of the user.
In this embodiment, the associated product information includes two kinds, i.e., second product information and third product information; in the case of the product information, a duplicate product between the second product information and the third product information may be used as the target product information. The first product information and the second product information may be all the target product information. Or setting a priority, and using the product information with the high priority as the target product information, for example, preferentially pushing the product information associated with the related product information, that is, using the third product information as the target product information.
In addition, the embodiment of the invention also provides an information pushing device based on the association rule.
Referring to fig. 5, fig. 5 is a functional module diagram of a first embodiment of an information pushing apparatus based on association rules according to the present invention.
In this embodiment, the information pushing apparatus based on association rules includes:
a frequent item set generating module 10, configured to obtain each historical product record corresponding to each user information, determine, in each combination corresponding to each historical product record, a combination with a support degree exceeding a first threshold as a frequent item set, and determine, in the frequent item set, a frequent item set with a confidence degree exceeding a second threshold as a target frequent item set;
an association rule determining module 20, configured to determine association rules between products and/or between user information and products according to the target frequent item set;
the associated product determining module 30 is configured to obtain target user information and related product information of a target user, and determine associated product information corresponding to the target user information and/or the related product information based on an association rule between the products and/or an association rule between the user information and the products;
and the target information pushing module 40 is configured to determine target product information in the associated product information, and push the target product information to the target user.
Further, the target information pushing module 40 specifically includes:
and the first information pushing unit is used for determining target product information corresponding to the target user in the associated product information based on the target user portrait corresponding to the target user and pushing the target product information to the target user.
Further, the information pushing device based on the association rule further includes:
the conformity calculation module is used for acquiring target behavior data of the target user and calculating the conformity of the target user and the product based on a first calculation formula, the similarity between the products and the target behavior data, wherein the first calculation formula is as follows:
wherein N (u) is a scoring product set of the target user, S (j, k) is k products with the maximum similarity to the product j, i is a repeated product of the scoring product set and the k products, and W is a repeated product of the scoring product set and the k productsijIs the similarity of products i and j, ruiScoring a product i for the target user u;
and the user portrait construction module is used for constructing the user portrait of the target user based on the target user information and the conformity between the target user and the product.
Further, the information pushing device based on the association rule further includes:
the similarity calculation module is used for calculating the similarity between the products based on a second calculation formula, wherein the first calculation formula is as follows:
wherein, | N (i) | is the number of persons who purchase product i, | N (j) | is the number of persons who purchase product j, | N (i) # N (j) | is the number of persons who purchase product i and product j.
Further, the information pushing apparatus is applied to a distributed file system, the distributed file system includes at least two working nodes, and the frequent itemset generating module 10 specifically includes:
the information acquisition unit to be mined is used for respectively acquiring each historical product record corresponding to each user information as information to be mined;
the information to be mined blocking unit is used for blocking the information to be mined according to preset division units when the data volume corresponding to the information to be mined exceeds a preset threshold value;
and the frequent item set mining unit is used for distributing the partitioned information to be mined to each working node in the distributed file system, so that the information to be mined is subjected to parallel frequent item set mining through each working node to generate a target frequent item set.
Further, the target information pushing module 40 specifically includes:
and the second information pushing unit is used for taking the first product information as the target product information and pushing the target product information to the target user when the associated product information is the first product information associated with the target user information and the related product information.
Further, the target information pushing module 40 specifically includes:
and the third information pushing unit is used for taking repeated product information between the second product information and the third product information as the target product information and pushing the target product information to the target user when the associated product information comprises the second product information associated with the target user information and the third product information associated with the related product information.
Each module in the association rule-based information pushing apparatus corresponds to each step in the association rule-based information pushing method embodiment, and the functions and implementation processes thereof are not described in detail herein.
In addition, the embodiment of the invention also provides a computer readable storage medium.
The computer readable storage medium of the present invention stores an association rule based information pushing program, where the association rule based information pushing program, when executed by a processor, implements the steps of the association rule based information pushing method as described above.
The method implemented when the association rule-based information pushing program is executed may refer to each embodiment of the association rule-based information pushing method of the present invention, and details are not described here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An information pushing method based on association rules is characterized in that the information pushing method based on association rules comprises the following steps:
respectively acquiring each historical product record corresponding to each user information, determining a combination with the support degree exceeding a first threshold value in each combination corresponding to each historical product record as a frequent item set, and determining a frequent item set with the confidence degree exceeding a second threshold value in the frequent item set as a target frequent item set;
determining association rules among products and/or between user information and the products according to the target frequent item set;
acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on a related rule between the products and/or a related rule between the user information and the products;
and determining target product information in the associated product information, and pushing the target product information to the target user.
2. The association rule-based information pushing method according to claim 1, wherein the step of determining target product information in the associated product information and pushing the target product information to a target user specifically comprises:
and determining target product information corresponding to the target user in the associated product information based on the target user portrait corresponding to the target user, and pushing the target product information to the target user.
3. The association rule-based information pushing method as claimed in claim 2, wherein before the step of determining the target product information corresponding to the target user in the associated product information based on the target user representation corresponding to the target user and pushing the target product information to the target user, the method further comprises:
acquiring target behavior data of the target user, and calculating the conformity of the target user and the product based on a first calculation formula, the similarity between products and the target behavior data, wherein the first calculation formula is as follows:
wherein N (u) is a scoring product set of the target user, S (j, k) is k products with the maximum similarity to the product j, i is a repeated product of the scoring product set and the k products, and W is a repeated product of the scoring product set and the k productsijIs the similarity of products i and j, ruiScoring a product i for the target user u;
and constructing a user portrait of the target user based on the target user information and the conformity of the target user and the product.
4. The association rule-based information pushing method according to claim 3, wherein the step of obtaining the target behavior data of the target user and calculating the conformity of the target user with the product based on the first calculation formula, the similarity between the products and the target behavior data further comprises:
calculating the similarity between the products based on a second calculation formula, wherein the first calculation formula is as follows:
wherein, | N (i) | is the number of persons who purchase product i, | N (j) | is the number of persons who purchase product j, | N (i) # N (j) | is the number of persons who purchase product i and product j.
5. The information pushing method based on association rules according to claim 1, wherein the information pushing method is applied to a distributed file system, the distributed file system includes at least two working nodes, the steps of respectively obtaining each historical product record corresponding to each user information, determining a combination with a support degree exceeding a first threshold value in each combination corresponding to each historical product record as a frequent item set, and determining a frequent item set with a confidence degree exceeding a second threshold value in the frequent item set, and the step of determining the frequent item set as a target frequent item set includes:
respectively acquiring each historical product record corresponding to each user information as information to be mined;
when the data volume corresponding to the information to be mined exceeds a preset threshold value, partitioning the information to be mined according to preset partition units;
and distributing the partitioned information to be mined to each working node in the distributed file system, so as to perform parallel frequent itemset mining on the information to be mined through each working node and generate a target frequent itemset.
6. The association rule-based information pushing method as claimed in claim 1, wherein the step of determining target product information in the association product information and pushing the target product information to the target user comprises:
and when the associated product information is first product information associated with the target user information and the related product information, taking the first product information as the target product information, and pushing the target product information to the target user.
7. The association rule-based information pushing method according to any one of claims 1-6, wherein the step of determining target product information in the association product information and pushing the target product information to the target user comprises:
and when the associated product information comprises second product information associated with the target user information and third product information associated with the related product information, taking repeated product information between the second product information and the third product information as the target product information, and pushing the target product information to the target user.
8. An association rule-based information pushing apparatus, characterized in that the association rule-based information pushing apparatus comprises:
a frequent item set generating module, configured to obtain each historical product record corresponding to each user information, determine, in each combination corresponding to each historical product record, a combination with a support degree exceeding a first threshold as a frequent item set, and determine, in the frequent item set, a frequent item set with a confidence degree exceeding a second threshold as a target frequent item set;
the association rule determining module is used for determining association rules among products and/or between user information and the products according to the target frequent item set;
the related product determining module is used for acquiring target user information and related product information of a target user, and determining related product information corresponding to the target user information and/or the related product information based on a related rule between the products and/or a related rule between the user information and the products;
and the target information pushing module is used for determining target product information in the associated product information and pushing the target product information to the target user.
9. An association rule based information pushing device, characterized in that the association rule based information pushing device comprises a processor, a memory, and an association rule based information pushing program stored on the memory and executable by the processor, wherein when the association rule based information pushing program is executed by the processor, the steps of the association rule based information pushing method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, on which an association rule-based information pushing program is stored, wherein the association rule-based information pushing program, when executed by a processor, implements the steps of the association rule-based information pushing method according to any one of claims 1 to 7.
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