Game prop package setting method and system based on frequency association rules

Abstract

The present application relates to a method and system for setting a game prop package based on frequency association rules, wherein the method includes: obtaining a preset data set for each level by preprocessing the original data records in the user's level-by-level purchase statistics table, generating a corresponding item header table based on the preset data set, and then obtaining a frequent data set based on the item header table and the preset data set, further generating a corresponding frequent pattern tree based on the frequent data set, mining the frequent pattern tree to obtain the maximum frequent item set, outputting the maximum frequent item set to the game prop frequent item set table of the game server database, and setting the game prop package based on the data in the game prop frequent item set table. Through this application, the problem of inaccurate prop package push for different game user groups is solved, and the corresponding game prop package recommendation is dynamically generated based on the purchase records of historical game users at different levels, which effectively improves the gaming experience of different game user groups.

Description

Technical Field

[0001] The present application relates to the field of intelligent recommendation technology, and in particular to a method and system for setting up a game prop package based on frequency association rules. Background Art

[0002] With the continuous development of the game industry, electronic games as a form of leisure and entertainment have gradually been accepted by people. The number of game players has increased rapidly, especially the base of paying players has continued to expand. Game developers hope to be able to develop appropriate prop packages for different user groups in a targeted manner to further enhance users' gaming experience.

[0003] At present, with the increase in game complexity, the amount of in-game data has surged. When faced with large amounts of data, traditional prop recommendation methods cannot accurately recommend appropriate game props for different user groups due to factors such as high data noise and bloated data code.

[0004] Currently, no effective solution has been proposed for the problem of inaccurate push of prop packages to different game user groups in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a method and system for setting a game prop package based on frequency association rules, so as to at least solve the problem of inaccurate prop package push for different game user groups in the related art.

[0006] In a first aspect, an embodiment of the present application provides a method for setting a game prop package based on a frequency association rule, the method comprising:

[0007] Preprocess the original data records in the user's level-by-level purchase statistics table to obtain a preset data set for each level;

[0008] Generate a corresponding header table according to the preset data set;

[0009] Obtain a frequent data set according to the header table and the preset data set, and generate a corresponding frequent pattern tree according to the frequent data set;

[0010] Mining the frequent pattern tree to obtain a maximum frequent item set;

[0011] Outputting the maximum frequent item set to the game props frequent item set table of the game server database;

[0012] The game props package is set according to the data in the game props frequent item set table.

[0013] In some embodiments, generating a corresponding header table according to the preset data set includes:

[0014] Scan the preset data set to obtain the support of all item sets in the preset data set;

[0015] Deleting a non-frequent item set whose support is less than a preset threshold in the item set to obtain a frequent item set;

[0016] An item header table is generated by arranging the frequent itemsets from large to small according to their support.

[0017] In some embodiments, obtaining a frequent data set according to the header table and the preset data set includes:

[0018] Scanning a preset data set and deleting a non-frequent item set in the preset data set that does not exist in the item header table;

[0019] The item data in the preset data set is reordered according to the arrangement order of the frequent item set in the item header table to obtain a frequent data set.

[0020] In some embodiments, mining the frequent pattern tree to obtain the maximum frequent item set includes:

[0021] According to the arrangement order of the frequent item sets in the item header table, the conditional pattern bases of the frequent item sets are calculated in sequence;

[0022] The maximum frequent itemset corresponding to the frequent itemset is obtained according to the conditional pattern base.

[0023] In some embodiments, before preprocessing the original data records in the user-level purchase statistics table, the method further includes:

[0024] Set the preset time according to the date of reaching the current level in the user's level-by-level purchase statistics table;

[0025] The original data records of the user's level-based purchase statistics table within a preset time are read from the game server database.

[0026] In a second aspect, an embodiment of the present application provides a game prop package setting system based on frequency association rules, the system comprising a preprocessing module, a generation module, a mining module, a database operation module and a setting module;

[0027] The preprocessing module preprocesses the original data records in the user level purchase statistics table to obtain a preset data set for each level.

[0028] The generating module generates a corresponding header table according to the preset data set;

[0029] The generating module obtains a frequent data set according to the header table and the preset data set, and generates a corresponding frequent pattern tree according to the frequent data set.

[0030] The mining module mines the frequent pattern tree to obtain the maximum frequent item set;

[0031] The database operation module outputs the maximum frequent item set to the game prop frequent item set table of the game server database;

[0032] The setting module sets the game prop package according to the data in the game prop frequent item set table.

[0033] In some embodiments, the generating module generates a corresponding header table according to the preset data set, including:

[0034] The generating module scans the preset data set to obtain the support of all item sets in the preset data set;

[0035] The generating module deletes a non-frequent item set whose support is less than a preset threshold in the item set to obtain a frequent item set;

[0036] The generating module generates an item header table by arranging the frequent itemsets from large to small according to their support.

[0037] In some embodiments, the generating module obtains the frequent data set according to the item header table and the preset data set, including:

[0038] The generating module scans a preset data set and deletes a non-frequent item set in the preset data set that does not exist in the item header table;

[0039] The generating module reorders the item data in the preset data set according to the arrangement order of the frequent item set in the item header table to obtain a frequent data set.

[0040] In some embodiments, the mining module mines the frequent pattern tree to obtain the maximum frequent item set, including:

[0041] The mining module sequentially calculates the conditional pattern bases of the frequent item sets according to the arrangement order of the frequent item sets in the item header table;

[0042] The mining module obtains a maximum frequent itemset corresponding to a frequent itemset according to the conditional pattern base.

[0043] In some embodiments, before the preprocessing module preprocesses the original data records in the user-level purchase statistics table, the system further includes:

[0044] The database operation module sets a preset time according to the date of reaching the current level in the user's level-by-level purchase statistics table;

[0045] The database operation module reads the original data records of the user level purchase statistics table within a preset time in the game server database.

[0046] Compared with the related art, the embodiment of the present application provides a method and system for setting a game prop package based on frequency association rules, which pre-processes the original data records in the user's level-by-level purchase statistics table to obtain a preset data set for each level, generates a corresponding item header table based on the preset data set, and then obtains a frequent data set based on the item header table and the preset data set, further generates a corresponding frequent pattern tree based on the frequent data set, mines the frequent pattern tree to obtain the maximum frequent item set, outputs the maximum frequent item set to the game prop frequent item set table in the game server database, and sets the game prop package based on the data in the game prop frequent item set table, thereby solving the problem of inaccurate prop package push for different game user groups, and realizing dynamic generation of corresponding game prop package recommendations based on historical game users' purchase records at different levels, effectively improving the gaming experience of different game user groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0048] Figure 1 is a flowchart of the steps of a method for setting a game prop package based on frequency association rules according to an embodiment of the present application;

[0049] Figure 2 is a schematic diagram of a frequent pattern tree generated according to an embodiment of the present application;

[0050] Figure 3 It is a structural block diagram of a method for setting a game prop package based on frequency association rules according to an embodiment of the present application;

[0051] Figure 4 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application.

[0052] Description of the accompanying drawings: 31. Preprocessing module; 32. Generation module; 33. Mining module; 34. Database operation module; 35. Setting module. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0054] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0055] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0056] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0057] Example 1

[0058] This application provides a method for setting a game prop package based on frequency association rules. Figure 1 is a flowchart of the steps of the method for setting a game prop package based on frequency association rules according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0059] Step S102, pre-processing the original data records in the user's level-by-level purchase statistics table to obtain a preset data set for each level;

[0060] Specifically, the game server database contains a user level-by-level purchase statistics table, wherein each record in the table contains the dimension [user id, date of reaching the current level, next level, purchase item set string]; the original data records in the user level-by-level purchase statistics table are preprocessed to obtain a preset data set for each level, and each record in the preset data set contains the dimension [user id, next level, purchase item hash set], wherein if the next level is 2, it represents a preset data set D2, indicating the game item purchase records of users from level 1 to level 2; if the next level is 3, it represents a preset data set D3, indicating the game item purchase records of users from level 2 to level 3, and so on.

[0061] Step S104, generating a corresponding header table according to a preset data set;

[0062] Specifically, a preset data set is scanned for the first time to obtain the support of all item sets in the preset data set, and a corresponding item header table is generated according to the support of the item set.

[0063] Step S106, obtaining a frequent data set according to the item header table and the preset data set, and generating a corresponding frequent pattern tree according to the frequent data set;

[0064] Specifically, the preset data set is scanned for the second time, and the relevant data in the preset data set is deleted and sorted according to the item header table to obtain a frequent data set, and then a corresponding frequent pattern tree is generated according to the frequent data set. It should be noted that the frequent pattern tree (Frequent Pattern tree) is referred to as FP Tree. As a general algorithm for mining frequent item sets, it is necessary to scan the data multiple times. I / O is a big bottleneck. However, no matter how much data there is, the FP Tree algorithm only needs to scan the data set twice, which improves the efficiency of the algorithm.

[0065] Step S108, mining the frequent pattern tree to obtain the maximum frequent item set;

[0066] Specifically, the conditional pattern base of the frequent itemset in the item header table is calculated to obtain the maximum frequent itemset.

[0067] Step S110, outputting the maximum frequent itemset to the game props frequent itemset table of the game server database, wherein the records in the game props frequent itemset table contain the dimension [today's date, next level, frequent itemset string];

[0068] Step S112, setting the game props package according to the data in the game props frequent item set table.

[0069] Through steps S102 to S112 in the embodiment of the present application, the problem of inaccurate push of prop packages for different game user groups is solved, and corresponding game prop package recommendations are dynamically generated based on historical game users' purchase records at different levels, effectively improving the gaming experience of different game user groups.

[0070] In some embodiments, step S104, generating a corresponding header table according to a preset data set includes:

[0071] Scan the preset data set to obtain the support of all item sets in the preset data set;

[0072] Delete the non-frequent item sets whose support is less than the preset threshold, and get the frequent item set;

[0073] The item header table is generated by arranging the frequent item sets from large to small according to their support.

[0074] Specifically, the preset data set is scanned for the first time to obtain the support of all item sets in the preset data set, and then the infrequent item sets with support less than the preset threshold are deleted to obtain the frequent item sets, and the frequent item sets are put into the item header table and arranged in descending order from large to small according to the support. Specific examples are as follows:

[0075] The preset data set contains 10 records, namely R1(abcefo), R2(ej), R3(ac dgp), R4(acegn), R5(abcefm), R6(acd), R7(eg), R8(acdeg), R9(aci) and R10(ace gl);

[0076] The first scan of the preset data set shows that the support of item sets a and c is 80%, the support of item set e is 70%, the support of item set g is 50%, the support of item set d is 30%, the support of item sets b and f is 20%, and the support of item sets i, j, l, m, n, o, and p is 10%;

[0077] If the preset threshold (support threshold) is set to 20%, then item sets a, c, e, g, d, b and f are frequent item sets, and item sets i, j, l, m, n, o and p are infrequent item sets. The infrequent item sets in the item sets are deleted to obtain the frequent item sets.

[0078] Table 1 is an item header table generated according to an embodiment of the present application. As shown in Table 1, a frequent item set is put into the item header table and arranged in descending order from large to small according to the support.

[0079] Table 1

[0080] a:8 c:8 e:7 g:5 d:3 b:2 f:2

[0081] In some embodiments, step S106, obtaining a frequent data set according to the item header table and the preset data set includes:

[0082] Scan the preset data set and delete the non-frequent item sets in the preset data set that do not exist in the item header table;

[0083] The frequent data set is obtained by reordering the item data in the preset data set according to the arrangement order of the frequent item set in the item header table.

[0084] Specifically, the preset data set is scanned for the second time, the non-frequent item sets in the preset data set that do not exist in the item header table are deleted, and the item data in the preset data set are arranged in descending order according to the support size of the frequent item set in the item header table to obtain a frequent data set. The specific examples are as follows:

[0085] The preset data set contains 10 records, namely R1(abcefo), R2(ej), R3(acdgp), R4(acegn), R5(abcefm), R6(acd), R7(eg), R8(acdeg), R9(aci) and R10(acegl); among them, a, c, e, g, d, b and f are frequent item sets, and i, j, l, m, n, o and p are infrequent item sets;

[0086] Delete the non-frequent item sets in 10 records, and according to the support of the frequent item sets in the item header table shown in Table 1, sort the item data in the preset data set in descending order to obtain the frequent data sets R1(acebf), R2(e), R3(acgd), R4(aceg), R5(acebf), R6(acd), R7(eg), R8(acegd), R9(ac) and R10(aceg)

[0087] also, Figure 2 is a schematic diagram of a frequent pattern tree generated according to an embodiment of the present application, such as Figure 2 As shown, step S106 also includes generating a corresponding frequent pattern tree according to the frequent data set.

[0088] In some embodiments, step S108, mining the frequent pattern tree to obtain the maximum frequent item set includes:

[0089] According to the order of the frequent item sets in the item header table, the conditional pattern bases of the frequent item sets are calculated in sequence;

[0090] According to the conditional pattern base, the maximum frequent itemset corresponding to the frequent itemset is obtained.

[0091] Specifically, start mining from the bottom item of the item header table upwards. For each item in the item header table corresponding to the frequent pattern tree, find its conditional pattern base. Among them, the conditional pattern base is the FP subtree corresponding to the node to be mined as the leaf node. Get this FP subtree, set the count of each node in the subtree to the count of the leaf node, and delete the nodes whose count is lower than the preset threshold (support threshold). Based on this conditional pattern base, recursively mine to obtain the maximum frequent item set. Specific examples are as follows:

[0092] According to the item header table shown in Table 1, we mine upward from the bottom item in the item header table, that is, starting from the frequent 1-item set f. Since f has only one node, there is only one path {a:8,c:8,e:5,b:2,f:2}. The counts of all ancestor nodes are set to the counts of leaf nodes (f is a leaf node), and the conditional pattern base of f is {a:2,c:2,e:2,b:2,f:2}, and the conditional pattern base of F is obtained;

[0093] Thus, we can mine the frequent two-item sets {a:2,f:2}, {b:2,f:2}, {c:2,f:2}, {e:2,f:2} about the frequent one-item set f, and further we can mine the frequent three-item sets, frequent four-item sets and frequent five-item sets, and finally the frequent five-item set {a:2,c:2,e:2,b:2,f:2}. This frequent five-item set is the maximum frequent item set of the frequent one-item set f.

[0094] A frequent item set b has only one node in the frequent pattern tree, and the method for finding it is the same as f.

[0095] A frequent item set d has 3 nodes in the frequent pattern tree. The count of the ancestor nodes is set to the number of leaf nodes (d is a leaf node), that is, {a:3,c:3,e:1,g:1,d:1,g:1,d:1,d:1}. Among them, the support of e, g, g (two ancestor nodes g) and d, d, d (three leaf nodes d) is 10%, which is less than the preset threshold of 20%, so they are deleted, and the conditional pattern base of d is obtained as {a:3,c:3}.

[0096] In this way, we can mine the frequent two-itemset {a:3,d:3},{c:3,d:3} about the frequent one-itemset d, and further get the frequent three-itemset {a:3,c:3,d:3}, which is the maximum frequent itemset of the frequent one-itemset d.

[0097] Similarly, the maximum frequent itemset of other frequent itemsets can be obtained.

[0098] In some embodiments, before preprocessing the original data records in the user-level purchase statistics table in step S102, the method further includes:

[0099] Set the preset time according to the date of reaching the current level in the user's level-by-level purchase statistics table;

[0100] Read the original data records of the user level purchase statistics table in the game server database within the preset time.

[0101] Specifically, the records in the user level purchase statistics table contain the dimensions [user id, date of reaching the current level, next level, purchased item collection string], and the preset time is set according to the "date of reaching the current level", which can generally be set to the most recent year of the date of reaching the current level;

[0102] Read the user level purchase statistics table in the game server database, and the original data records within the most recent year before the current level is reached.

[0103] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0104] Example 2

[0105] The embodiment of the present application provides a game prop package setting system based on frequency association rules. Figure 3 is a structural block diagram of a method for setting a game prop package based on frequency association rules according to an embodiment of the present application, such as Figure 3 As shown, the system includes a preprocessing module 31, a generating module 32, a mining module 33, a database operating module 34 and a setting module 35;

[0106] The preprocessing module 31 preprocesses the original data records in the user level purchase statistics table to obtain a preset data set for each level.

[0107] The generating module 32 generates a corresponding item header table according to a preset data set;

[0108] The generating module 32 obtains a frequent data set according to the item header table and the preset data set, and generates a corresponding frequent pattern tree according to the frequent data set.

[0109] The mining module 33 mines the frequent pattern tree to obtain the maximum frequent item set;

[0110] The database operation module 34 outputs the maximum frequent item set to the game prop frequent item set table of the game server database;

[0111] The setting module 35 sets the game prop package according to the data in the game prop frequent item set table.

[0112] Through the system in the embodiment of the present application, the preprocessing module 31 preprocesses the original data records in the user's level-by-level purchase statistics table to obtain a preset data set for each level, the generation module 32 generates a corresponding item header table based on the preset data set, and then obtains a frequent data set based on the item header table and the preset data set, and further generates a corresponding frequent pattern tree based on the frequent data set, the mining module 33 mines the frequent pattern tree to obtain the maximum frequent item set, the database operation module 34 outputs the maximum frequent item set to the game props frequent item set table of the game server database, and the setting module 35 sets the game prop package according to the data in the game props frequent item set table, which solves the problem of inaccurate prop package push for different game user groups, and realizes the dynamic generation of corresponding game prop package recommendations based on historical game users' purchase records at different levels, effectively improving the gaming experience of different game user groups.

[0113] In some embodiments, the generating module 32 generates a corresponding header table according to a preset data set, including:

[0114] The generating module 32 scans the preset data set to obtain the support of all item sets in the preset data set;

[0115] The generating module 32 deletes the infrequent item set whose support is less than a preset threshold, and obtains a frequent item set;

[0116] The generating module 32 generates an item header table by arranging the frequent item sets from large to small according to their support.

[0117] In some embodiments, the generating module 32 obtains the frequent data set according to the item header table and the preset data set, including:

[0118] The generation module 32 scans the preset data set and deletes the non-frequent item sets in the preset data set that are not present in the item header table;

[0119] The generating module 32 reorders the item data in the preset data set according to the arrangement order of the frequent item set in the item header table to obtain a frequent data set.

[0120] In some embodiments, the mining module 33 mines the frequent pattern tree to obtain the maximum frequent item set including:

[0121] The mining module 33 calculates the conditional pattern bases of the frequent item sets in sequence according to the arrangement order of the frequent item sets in the item header table;

[0122] The mining module 33 obtains the maximum frequent item set corresponding to the frequent item set according to the conditional pattern base.

[0123] In some embodiments, before the preprocessing module 31 preprocesses the original data records in the user-level purchase statistics table, the system further includes:

[0124] The database operation module 34 sets a preset time according to the date of reaching the current level in the user's level-by-level purchase statistics table;

[0125] The database operation module 34 reads the original data records of the user level purchase statistics table in the game server database within a preset time.

[0126] The preset data set of each level is obtained by preprocessing the original data records in the user's level-by-level purchase statistics table, and the corresponding item header table is generated according to the preset data set. Then, the frequent data set is obtained according to the item header table and the preset data set. The corresponding frequent pattern tree is further generated according to the frequent data set. The frequent pattern tree is mined to obtain the maximum frequent item set, and the maximum frequent item set is output to the game props frequent item set table of the game server database. Game props packages are set according to the data in the game props frequent item set table, which solves the problem of inaccurate prop package push for different game user groups and realizes the dynamic generation of corresponding game props package recommendations based on the historical game users' purchase records at different levels, which effectively improves the gaming experience of different game user groups.

[0127] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0128] Example 3

[0129] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for setting a game prop package based on frequency association rules is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0130] In one embodiment, Figure 4is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 4 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 4 As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system and the computer program, the computer program is executed by the processor to implement a game prop package setting method based on frequency association rules, and the database is used to store data.

[0131] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0133] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for setting a game prop package based on frequency association rules, characterized in that: The method comprises: Each record in the user level purchase statistics table contains the following dimensions: user id, date of reaching the current level, next level, and purchase item set string; a preset time is set according to the date of reaching the current level in the user level purchase statistics table; and the original data record within the preset time closest to the date of reaching the current level is read from the game server database; Preprocess the original data records in the user level purchase statistics table to obtain a preset data set for each level; each record in the preset data set contains dimensions: user id, next level, purchase item hash set; if the next level is 2, it represents a preset data set D2, indicating the game item purchase records of users from level 1 to level 2; if the next level is 3, it represents a preset data set D3, indicating the game item purchase records of users from level 2 to level 3; Performing a first scan on the preset data set to obtain the support of all item sets in the preset data set, and generating a corresponding item header table according to the support of the item set; Scanning the preset data set for a second time, deleting and sorting the relevant data in the preset data set according to the header table to obtain a frequent data set, and then generating a corresponding frequent pattern tree according to the frequent data set; Mining the frequent pattern tree to obtain a maximum frequent item set; Outputting the maximum frequent item set to the game props frequent item set table of the game server database; The game props package is set according to the data in the game props frequent item set table.

2. The method according to claim 1, characterized in that Generating a corresponding header table according to the preset data set includes: Scan the preset data set to obtain the support of all item sets in the preset data set; Deleting a non-frequent item set whose support is less than a preset threshold in the item set to obtain a frequent item set; An item header table is generated by arranging the frequent itemsets from large to small according to their support.

3. The method according to claim 1, characterized in that: Obtaining a frequent data set according to the header table and the preset data set includes: Scanning a preset data set and deleting a non-frequent item set in the preset data set that does not exist in the item header table; The item data in the preset data set is reordered according to the arrangement order of the frequent item set in the item header table to obtain a frequent data set.

4. The method according to claim 1, characterized in that: The maximum frequent item set obtained by mining the frequent pattern tree includes: According to the arrangement order of the frequent item sets in the item header table, the conditional pattern bases of the frequent item sets are calculated in sequence; The maximum frequent itemset corresponding to the frequent itemset is obtained according to the conditional pattern base.

5. A game prop package setting system based on frequency association rules, characterized in that: The system includes a preprocessing module, a generating module, a mining module, a database operation module and a setting module; Each record in the user level purchase statistics table contains the following dimensions: user ID, date of reaching the current level, next level, and purchase item collection string; The database operation module sets the preset time according to the date of reaching the current level in the user's level-by-level purchase statistics table; Read the original data records in the game server database within the preset time closest to the date of reaching the current level; The preprocessing module preprocesses the original data records in the user level purchase statistics table to obtain a preset data set for each level; each record in the preset data set contains dimensions: user id, next level, purchase item hash set; wherein, if the next level is 2, it represents a preset data set D2, indicating the game item purchase records of users from level 1 to level 2; if the next level is 3, it represents a preset data set D3, indicating the game item purchase records of users from level 2 to level 3; Performing a first scan on the preset data set to obtain the support of all item sets in the preset data set, and generating a corresponding item header table according to the support of the item set; The generating module performs a second scan on the preset data set, deletes and sorts the relevant data in the preset data set according to the header table to obtain a frequent data set, and then generates a corresponding frequent pattern tree according to the frequent data set; The mining module mines the frequent pattern tree to obtain the maximum frequent item set; The database operation module outputs the maximum frequent item set to the game prop frequent item set table of the game server database; The setting module sets the game prop package according to the data in the game prop frequent item set table.

6. The system according to claim 5, characterized in that The generating module generates a corresponding header table according to the preset data set, including: The generating module scans the preset data set to obtain the support of all item sets in the preset data set; The generating module deletes a non-frequent item set whose support is less than a preset threshold in the item set to obtain a frequent item set; The generating module generates an item header table by arranging the frequent itemsets from large to small according to their support.

7. The system according to claim 5, characterized in that The generating module obtains the frequent data set according to the header table and the preset data set, including: The generating module scans a preset data set and deletes a non-frequent item set in the preset data set that does not exist in the item header table; The generating module reorders the item data in the preset data set according to the arrangement order of the frequent item set in the item header table to obtain a frequent data set.

8. The system according to claim 5, characterized in that The mining module mines the frequent pattern tree to obtain the maximum frequent item set, including: The mining module sequentially calculates the conditional pattern bases of the frequent item sets according to the arrangement order of the frequent item sets in the item header table; The mining module obtains a maximum frequent itemset corresponding to a frequent itemset according to the conditional pattern base.

Images