A method and apparatus for matching user interaction behavior patterns based on sequences
Through the sequence-based user interaction behavior pattern matching method, the problem of insufficient response efficiency and accuracy of the intelligent human-computer interaction system is solved, real-time adaptability and efficient matching are achieved, and user experience is improved.
Patent Information
- Application Number
- CN202411540245.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2025-04-01
AI Technical Summary
The existing intelligent human-computer interaction system has insufficient response efficiency and accuracy in matching user interaction behavior patterns, and cannot achieve immediate adaptation.
The user interaction behavior pattern matching method based on sequence is adopted. By obtaining the user's initial interaction behavior, it updates it to the interactive behavior string, and performs similarity calculations with the pattern string library, and selects the target pattern string that meets the preset threshold to dynamically match the user interaction behavior pattern.
It improves the efficiency and accuracy of user interaction behavior pattern matching, realizes instant response and adaptation of intelligent human-computer interaction systems, and improves user experience.
Smart Images
Figure CN119740034A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of intelligent human-computer interaction, and more specifically, to a sequence-based user interaction behavior pattern matching method and device. Background Art
[0002] At present, many intelligent human-computer interaction systems have the ability to present interaction solutions on demand according to user interaction needs. For example, many software and web products have personalized interaction interfaces, personalized information push and other functions. However, the current solutions for obtaining user interaction needs are mostly based on "natural language understanding", "user settings", "user portrait construction" and "script presets", which require users or systems to actively, directly and clearly provide demand information, inevitably generating interaction costs and information collection costs, and there is a certain time lag, which makes it impossible to achieve instant adaptation of interaction solutions.
[0003] In many interactive scenarios, the user's interactive behavior can be understood to obtain the user's interactive needs, so that the interactive scheme can be adaptive without relying on the input of clear information. The understanding of user interactive behavior can be achieved through matching the user's interactive behavior pattern, but the existing methods cannot achieve continuous matching of user interactive behavior sequences, resulting in poor real-time matching of user interactive behavior patterns, which in turn leads to insufficient response efficiency and accuracy of the system.
[0004] Therefore, how to improve the response efficiency and accuracy of intelligent human-computer interaction systems is a technical problem that needs to be solved urgently. Summary of the invention
[0005] In view of the defects of the prior art, the purpose of the present application is to provide a sequence-based user interaction behavior pattern matching method and device, aiming to solve the problems of poor efficiency and low accuracy of intelligent human-computer interaction systems in the prior art.
[0006] In a first aspect, the present application provides a sequence-based user interaction behavior pattern matching method, comprising:
[0007] Acquire the initial interaction behavior of the user, and update the initial interaction behavior according to the first mapping relationship to obtain a user interaction behavior string;
[0008] Calculate the similarity between the current user interaction behavior string and each pattern string in the pattern string library to obtain a similarity calculation result set;
[0009] Based on the comparison between the similarity calculation result set and a preset threshold, a target pattern string that meets the judgment condition is selected;
[0010] The user interaction behavior pattern sequence is matched to the target pattern string through the second mapping relationship, the user interaction behavior pattern sequence is determined by mapping matching, and the current user interaction behavior string is cleared to cyclically match the next interaction behavior until the interaction task is completed.
[0011] Optionally, the comparing the similarity calculation result set with a preset threshold value to select a target pattern string that meets a judgment condition includes:
[0012] Based on the comparison between the similarity calculation result set and the screening threshold, determining that there is a pattern string in the user interaction behavior string whose similarity is higher than the screening threshold;
[0013] Determine the target pattern string based on the comparison between the similarity calculation result set and the screening threshold;
[0014] Among them, the similarity between the target pattern string and the current user interaction behavior string is higher than the matching threshold, and the number of strings with the highest similarity is 1.
[0015] Optionally, it also includes:
[0016] Determining that there is no pattern string with a similarity higher than the screening threshold in the user interaction behavior string, obtaining the next interaction behavior, and performing user interaction behavior pattern matching on the next interaction behavior;
[0017] It is determined that there is no pattern string with a similarity higher than the matching threshold in the user interaction behavior string, the current user interaction behavior string is cleared, and the next interaction behavior is obtained to perform user interaction behavior pattern matching on the next interaction behavior.
[0018] Optionally, the user interaction behavior string is generated in a manner including:
[0019] Mapping user interactions to unique characters, each character representing a specific interaction;
[0020] Generate an interaction behavior string based on the mapped unique characters to represent a series of interaction behaviors of the user;
[0021] The format of the user interaction behavior string is <abc>, where a, b, and c each represent a single item set data and are arranged in chronological order according to the user interaction behavior.
[0022] Optionally, the user interaction behavior pattern sequence is composed of i groups of user interaction behavior sets, and the i-th group of user interaction behavior sets is m i , i is a positive integer;
[0023] In each user interaction behavior set m i In the example, there are multiple interactive behaviors t j , represents the jth interaction behavior in the i-th set, where j is a positive integer;
[0024] Among them, each interaction behavior set m i There is a temporal relationship between them, and any interaction behavior set m i Interaction behavior within j There is no temporal relationship between them.
[0025] Optionally, the pattern string is represented by a single character representing a unique interaction behavior, and each character corresponds to a specific interaction behavior item set; the pattern string includes multiple item set data, and the format of the item set data is<a(bc)d> ;
[0026] Among them, a represents a single item set data; bc represents an item set data containing multiple interaction items, b and c represent two interaction behavior items, and there is no temporal order relationship in the item set data; d represents another single item set data, and there is a temporal order relationship between the item set data in the pattern string.
[0027] Optionally, it also includes:
[0028] When the length of the current user interaction behavior string is less than the pattern string in the pattern string library, a similarity calculation cold start process is performed.
[0029] In a second aspect, the present application further provides a sequence-based user interaction behavior pattern matching device, comprising:
[0030] An acquisition module, used to acquire an initial interaction behavior of a user, and update the initial interaction behavior according to a first mapping relationship to obtain a user interaction behavior string;
[0031] A similarity module is used to calculate the similarity between the current user interaction behavior string and each pattern string in the pattern string library to obtain a similarity calculation result set;
[0032] A judgment module, used for comparing the similarity calculation result set with a preset threshold value and selecting a target pattern string that meets the judgment condition;
[0033] The matching module is used to match the user interaction behavior pattern sequence of the target pattern string through the second mapping relationship, determine the user interaction behavior pattern through mapping matching of the user interaction behavior pattern sequence, and clear the current user interaction behavior string to cyclically match the next interaction behavior until the interaction task is completed.
[0034] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.
[0035] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.
[0036] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.
[0037] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.
[0038] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:
[0039] (1) This application can more accurately analyze the user's real behavior pattern by converting the user's initial interaction behavior into a user interaction behavior string and performing similarity calculation. By using the pattern string library and the current user interaction behavior string to perform similarity calculation, the pattern that matches the user behavior can be quickly and effectively identified. Through a flexible matching mechanism, it can dynamically adapt to the behavior changes of different users, thereby improving the matching efficiency of user interaction behavior. By setting a preset threshold to only select the target pattern string that meets the judgment conditions, information overload or mismatching is avoided, further improving the efficiency and accuracy of user interaction behavior matching, and ensuring that the final matching result is more in line with the user's real needs, thereby improving the response efficiency and accuracy of the intelligent human-computer interaction system.
[0040] (2) This application enables the system to respond and adapt in real time by clearing the current user interaction behavior string and cyclically matching the next interaction behavior, so that each user interaction can be quickly identified and processed, thereby improving the real-time matching of user interaction behavior patterns, thereby improving interaction efficiency and user experience.
[0041] (3) Through the application of the first mapping relationship and the second mapping relationship, the present application can better link user behavior with specific behavior patterns and achieve effective behavior pattern recognition.
[0042] (4) The present application can instantly obtain the user's behavior pattern according to the user's interactive operation context. As an automated means of instantly matching the user's interactive behavior pattern, it can avoid falling into local optimality and infinite loops. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] Figure 1 This is one of the flow charts of the sequence-based user interaction behavior pattern matching method provided in the embodiment of the present application;
[0044] Figure 2 This is the second flow chart of the sequence-based user interaction behavior pattern matching method provided in the embodiment of the present application;
[0045] Figure 3 is a similarity calculation flow chart of an embodiment of the present application;
[0046] Figure 4 is a schematic diagram of the relationship between the data forms in the embodiments of the present application;
[0047] Figure 5 is a structural schematic diagram of a sequence-based user interaction behavior pattern matching device provided in an embodiment of the present application;
[0048] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail 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 used to limit the present application.
[0050] The term "and / or" in this document is a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this document indicates that the associated objects are in an or relationship, for example, A / B means A or B.
[0051] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.
[0052] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.
[0053] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.
[0054] Next, the technical solutions provided in the embodiments of the present application are introduced.
[0055] Reference Figure 1 , the present application provides a sequence-based user interaction behavior pattern matching method, comprising:
[0056] S101. Obtaining the initial interaction behavior of the user, updating the initial interaction behavior according to the first mapping relationship, and obtaining a user interaction behavior string;
[0057] S102. Calculate the similarity between the current user interaction behavior string and each pattern string in the pattern string library to obtain a similarity calculation result set;
[0058] S103. Based on the comparison of the similarity calculation result set and the preset threshold, a target pattern string that meets the judgment condition is selected;
[0059] S104. Match the user interaction behavior pattern sequence of the target pattern string through the second mapping relationship, determine the user interaction behavior pattern through mapping matching of the user interaction behavior pattern sequence, and clear the current user interaction behavior string to cyclically match the next interaction behavior until the interaction task is completed.
[0060] Specifically, in the above S101, the user's initial interaction behavior is first captured. Before capturing the user's initial interaction behavior, it is necessary to set the initial user interaction behavior string to be empty. After obtaining the initial interaction behavior, these behaviors are updated according to the first mapping relationship defined in advance to form a structured user interaction behavior string.
[0061] A user interaction behavior sequence is composed of multiple user interaction behaviors. The user interaction behavior sequence can be expressed as T = <t1,t2,...,t n >(n≥1), a user interaction behavior sequence is a sequence consisting of n user interaction behaviors, t n is the nth interaction behavior in the interaction behavior sequence.
[0062] Furthermore, in step S102, the current user's interaction behavior string (such as "abc") is similarly calculated with each pattern string in the pattern string library. The pattern string library contains a plurality of predefined user behavior patterns, which are used to represent common user interaction sequences.
[0063] Similarity describes the degree of match between the user interaction behavior string and each pattern string in the pattern string library. Usually, existing mature algorithms can be used, including exact matching and fuzzy matching, including but not limited to Levenshtein distance, Jaccard coefficient, TF-IDF and other methods, which can be selected according to actual conditions and will not be elaborated here.
[0064] For example, suppose the pattern string library contains the following patterns: Pattern 1: "ab", Pattern 2: "ac", Pattern 3: "abc". A similarity calculation method will be used to measure the similarity between the user behavior string and each pattern, and generate a similarity calculation result set.
[0065] For example, the result set might be:
[0066] The similarity to pattern 1 ("a") is 0.7, the similarity to pattern 2 ("ab") is 0.9, and the similarity to pattern 3 ("abc") is 1.0.
[0067] In step S103, the target pattern string in the similarity calculation result set is screened based on a preset threshold value. If the similarity value of a pattern string exceeds the threshold value, it is marked as the target pattern string.
[0068] Specifically manifested as:
[0069] Based on the comparison between the similarity calculation result set and the screening threshold, determining that there is a pattern string in the user interaction behavior string whose similarity is higher than the screening threshold;
[0070] Determine the target pattern string based on the comparison between the similarity calculation result set and the screening threshold;
[0071] Among them, the similarity between the target pattern string and the current user interaction behavior string is higher than the matching threshold, and the number of strings with the highest similarity is 1.
[0072] It should be noted that the range of the screening threshold TH1 is defined as (0%, 100%), and if the similarity calculation result is lower than the screening threshold, it can be considered that the interaction behavior string does not match the pattern string.
[0073] The range of the matching threshold TH2 is defined as (0%, 100%). If the similarity calculation result is higher than the matching threshold, it can be considered that the interaction behavior string matches the pattern string. If the number of similarity calculation results higher than the matching threshold is more than one, the highest value is selected.
[0074] It should be noted that in this step, the interaction behavior of the current user is captured and a user interaction behavior string is formed, such as "abc". Then, the string is similarity calculated with all pattern strings in the pattern string library.
[0075] The system presets a screening threshold, such as 0.8, for subsequent screening. This threshold can be adjusted based on historical data or specific business needs to ensure efficient identification of patterns related to user behavior.
[0076] According to the above similarity calculation results: the similarity of pattern 1 ("a") is 0.7, the similarity of pattern 2 ("ab") is 0.9, and the similarity of pattern 3 ("abc") is 1.0. Compared with the screening threshold, the system will screen out pattern strings with similarity higher than the threshold. At this time, the pattern string in the user interaction behavior string will be marked as a "candidate pattern".
[0077] In the above example, pattern 2 (similarity 0.9) and pattern 3 (similarity 1.0) will be considered as candidate patterns, both of which have similarities higher than 0.8.
[0078] Further, the target pattern string is determined from the candidate pattern strings. This step requires finding the pattern string with the highest similarity to the current user interaction behavior string from the selected candidate patterns.
[0079] For example, among the candidate pattern strings, pattern 3 has the highest similarity (1.0). Therefore, pattern 3 will be selected as the target pattern string. It should be noted that the similarity between the selected target pattern string and the user interaction behavior string must be higher than the preset matching threshold (for example, 0.8). The number of pattern strings with the highest similarity must be 1 to ensure clear matching results. It can effectively screen and identify user interaction behavior strings, and then match the most semantically and behaviorally relevant target pattern strings in real time.
[0080] In the final step S104, the selected target pattern string is mapped to the corresponding user interaction behavior pattern sequence through the second mapping relationship, which can help the system better understand the user's behavior intention and provide personalized response or recommendation according to the identified behavior pattern.
[0081] The current user interaction behavior string is cleared to prepare for the next new user operation. This process will be repeated until the user completes the entire interaction task to ensure real-time behavior pattern recognition.
[0082] Assuming the target pattern string is "abc", the system will confirm the user's behavior pattern based on the second mapping relationship, and the subsequent operations will continue to be tracked to form a new user interaction behavior string. The above steps can achieve real-time capture, analysis and pattern recognition of user interaction behaviors. From the user's initial behavior string to similarity calculation, to the selection of the target pattern and the matching of the behavior pattern, the whole process forms an efficient dynamic feedback mechanism.
[0083] The embodiment of the present application can more accurately analyze the user's real behavior pattern by converting the user's initial interaction behavior into a user interaction behavior string and performing similarity calculation. By using the pattern string library and the current user interaction behavior string to perform similarity calculation, the pattern that matches the user behavior can be quickly and effectively identified, and the behavior changes of different users can be dynamically adapted through a flexible matching mechanism. By setting a preset threshold and only selecting the target pattern string that meets the judgment conditions, information overload or mismatching is avoided, the matching efficiency and accuracy are improved, and the final matching result can be ensured to be more in line with the user's real needs, thereby improving the response efficiency and accuracy of the intelligent human-computer interaction system.
[0084] Optionally, it also includes:
[0085] Determining that there is no pattern string with a similarity higher than the screening threshold in the user interaction behavior string, obtaining the next interaction behavior, and performing user interaction behavior pattern matching on the next interaction behavior;
[0086] It is determined that there is no pattern string with a similarity higher than the matching threshold in the user interaction behavior string, the current user interaction behavior string is cleared, and the next interaction behavior is obtained to perform user interaction behavior pattern matching on the next interaction behavior.
[0087] This embodiment shows how to obtain and match the next interaction behavior if there is no pattern string in the current user interaction behavior string whose similarity is higher than a set threshold during the process of processing the user interaction behavior pattern matching.
[0088] In the similarity calculation result set, the similarity value of each pattern string is compared with the preset screening threshold. If the similarity of any pattern string is higher than the threshold, it is confirmed that there is no pattern string that meets the conditions in the user interaction behavior string.
[0089] If it is confirmed that there is no pattern string with a similarity higher than the screening threshold in the current user interaction behavior string, the current user interaction behavior string will be cleared to prepare for the next user interaction.
[0090] Get the next user interaction behavior. The above scheme S101-S104 will be repeated to calculate the similarity between the new user interaction behavior string and all patterns in the pattern string library. After obtaining the new interaction behavior, continue to perform the similarity calculation and pattern matching process.
[0091] Optionally, the user interaction behavior string is generated in a manner including:
[0092] Mapping user interactions to unique characters, each character representing a specific interaction;
[0093] Generate an interaction behavior string based on the mapped unique characters to represent a series of interaction behaviors of the user;
[0094] The format of the user interaction behavior string is <abc>, where a, b, and c each represent a single item set data and are arranged in chronological order according to the user interaction behavior.
[0095] Specifically, the string in this embodiment is used as a way to describe user interaction behavior. A single character represents a unique interaction behavior (such as the letter "a"), and a mapping relationship is established between the character and the user interaction behavior. Taking the expression of item set data as an example, if a user interaction behavior is T=<t1,t2,t3> , then the corresponding user interaction behavior string can be expressed as: <abc>.
[0096] In the above example, each letter represents a single item set data, and the above sequence consists of three item set data, a, b, and c, and they are sequential in time.
[0097] The user interaction behavior pattern sequence is composed of i groups of user interaction behavior sets, and the i-th group of user interaction behavior sets is m i , i is a positive integer;
[0098] In each user interaction behavior set m i In the example, there are multiple interactive behaviors t j , represents the jth interaction behavior in the i-th set, where j is a positive integer;
[0099] Among them, each interaction behavior set m i There is a temporal relationship between them, and any interaction behavior set m i Interaction behavior within j There is no temporal relationship between them.
[0100] Specifically, the user interaction behavior pattern sequence in this embodiment forms a mapping relationship with the user interaction behavior pattern, and matching the user interaction behavior pattern sequence means matching the user interaction behavior pattern. <m1,m2,...,m i >(i≥1) is a sequence consisting of i sets of user interaction behaviors, m i is the i-th interactive behavior set in the interactive behavior pattern, m i ={t1,t2,...,t k }(j≥1), t k is the set of interaction behaviors m i The jth interaction behavior of the interactive behavior set. There is a temporal order relationship between the interactive behavior sets, and there is no temporal order relationship between the interactive behaviors in the interactive behavior set. Therefore, it is possible to capture complex interactive behavior patterns and analyze the changes in user behavior in different time periods. In each set, there is no temporal order relationship between the interactive behaviors. This design allows users to perform multiple behaviors in a specific time, thereby more realistically reflecting the user's interaction habits.
[0101] Optionally, the pattern string is represented by a single character representing a unique interaction behavior, and each character corresponds to a specific interaction behavior item set; the pattern string includes multiple item set data, and the format of the item set data is<a(bc)d> ;
[0102] Among them, a represents a single item set data; bc represents an item set data containing multiple interaction items, b and c represent two interaction behavior items, and there is no temporal order relationship in the item set data; d represents another single item set data, and there is a temporal order relationship between the item set data in the pattern string.
[0103] Specifically, the pattern string in this embodiment is used as a way to describe the user interaction behavior pattern sequence. A single character represents a unique interaction behavior (such as the letter "a"), and a mapping relationship is established between the character and the user interaction behavior. Taking the expression of item set data as an example, if a user interaction behavior pattern is M1=<m1,m2,m3> , where m1 = {t1}, m2 = {t2, t3}, m3 = {t4}, then the corresponding pattern string can be expressed as:<a(bc)d> ;
[0104] In the above example, a represents a single item set data, (bc) also represents a single item set data (including two interaction items), and the interaction items in brackets have no temporal order. The above sequence consists of a, (bc), d, a total of 3 item set data, and they have a temporal order.
[0105] The structured representation of this embodiment enables the pattern recognition algorithm to more easily extract and analyze complex interactive behavior patterns, improving the accuracy and efficiency of recognition. In the pattern string, there is a temporal relationship between different item sets, which can better understand the causal relationship and logical order of user behavior, thereby providing more targeted support in recommendation and decision-making.
[0106] Optionally, it also includes:
[0107] When the length of the current user interaction behavior string is less than the pattern string in the pattern string library, a similarity calculation cold start process is performed.
[0108] It should be noted that although similarity calculation can be implemented through a variety of algorithms, since the length of the interaction behavior string changes dynamically starting from 0, there may be a situation where the length of the current user interaction behavior string is less than some pattern strings, which is not conducive to calculation. Therefore, a similarity calculation cold start rule is formulated. If the length of the current interaction behavior string is less than the current compared pattern string, the current interaction behavior string is temporarily completed backwards to be consistent with the current compared pattern string. After comparison, the current interaction behavior string is restored.
[0109] Reference Figure 2 , Figure 2 It is a complete flow chart of the embodiment of the present application;
[0110] include:
[0111] S1. Set the initial user interaction behavior string to be empty.
[0112] S2. Obtain the user's initial interaction behavior.
[0113] S3. Update the user interaction behavior string.
[0114] S4. Calculate the similarity between the current interaction behavior string and the pattern strings in the pattern string library.
[0115] S5. Determine whether there is a pattern string whose similarity with the current user interaction behavior string is higher than the screening threshold.
[0116] S51. If S5 exists, continue to determine whether there is a pattern string whose similarity with the current user interaction behavior string is higher than the matching threshold, and the number of strings with the highest similarity is 1.
[0117] If S51 exists, then this pattern string is selected, the corresponding user interaction behavior pattern of this pattern string is matched, the interaction behavior string is cleared, and the next interaction behavior is obtained, and then the process returns to S3.
[0118] If S51 does not exist, obtain the next interactive behavior and return to S3.
[0119] S52. If what is described in S5 does not exist, clear the interaction behavior string, obtain the next interaction behavior, and return to S3.
[0120] Reference Figure 3 , Figure 3 The similarity calculation flow chart for the embodiment of the present application includes:
[0121] Select the first pattern string;
[0122] Determine whether the length of the current user interaction behavior string is less than the length of the current mode string;
[0123] If yes, the similarity calculation is cold-started, if not, the similarity calculation is performed directly;
[0124] The result is added to the similarity calculation result set;
[0125] Determine whether to traverse all pattern strings;
[0126] If yes, the process ends, if no, the next pattern string is selected.
[0127] Reference Figure 4 , Figure 4 It is a schematic diagram of the relationship between the data formats in the embodiments of the present application.
[0128] User interaction behaviors constitute user interaction behavior sequences;
[0129] The user interaction behavior sequence and the user interaction behavior string are mapped to each other;
[0130] Calculate the similarity between the user interaction behavior string and the pattern string;
[0131] The pattern string and the user interaction behavior pattern sequence are mapped to each other;
[0132] Pattern sequences map to each other and user interaction behavior patterns map to each other.
[0133] Reference Figure 5 The present application also provides a sequence-based user interaction behavior pattern matching device, comprising:
[0134] An acquisition module 510 is used to acquire an initial interaction behavior of a user, and update the initial interaction behavior according to a first mapping relationship to obtain a user interaction behavior string;
[0135] A similarity module 520 is used to calculate the similarity between the current user interaction behavior string and each pattern string in the pattern string library to obtain a similarity calculation result set;
[0136] A judgment module 530, configured to compare the similarity calculation result set with a preset threshold value and select a target pattern string that meets a judgment condition;
[0137] The matching module 540 is used to match the user interaction behavior pattern sequence of the target pattern string through the second mapping relationship, determine the user interaction behavior pattern through mapping matching of the user interaction behavior pattern sequence, and clear the current user interaction behavior string to cyclically match the next interaction behavior until the interaction task is completed.
[0138] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.
[0139] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.
[0140] Reference Figure 6 Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, which may include: a processor (processor) 610, a communication interface (CommunicationsInterface) 620, a memory (memory) 630 and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call the logic instructions in the memory 630 to execute the method in the above embodiment.
[0141] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.
[0142] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.
[0143] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.
[0144] It is understandable that the processor in the embodiment of the present application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.
[0145] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.
[0146] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.
[0147] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.
[0148] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.< / abc> < / abc> < / abc>
Claims
1. A sequence-based user interaction behavior pattern matching method, characterized in that: include: Acquire the initial interaction behavior of the user, and update the initial interaction behavior according to the first mapping relationship to obtain a user interaction behavior string; Calculate the similarity between the current user interaction behavior string and each pattern string in the pattern string library to obtain a similarity calculation result set; Based on the comparison between the similarity calculation result set and a preset threshold, a target pattern string that meets the judgment condition is selected; The user interaction behavior pattern sequence is matched to the target pattern string through the second mapping relationship, the user interaction behavior pattern sequence is determined by mapping matching, and the current user interaction behavior string is cleared to cyclically match the next interaction behavior until the interaction task is completed.
2. The user interaction behavior pattern matching method according to claim 1, characterized in that: The comparing the similarity calculation result set with a preset threshold value and selecting a target pattern string that meets the judgment condition includes: Based on the comparison between the similarity calculation result set and the screening threshold, determining that there is a pattern string in the user interaction behavior string whose similarity is higher than the screening threshold; Determine the target pattern string based on the comparison between the similarity calculation result set and the screening threshold; Among them, the similarity between the target pattern string and the current user interaction behavior string is higher than the matching threshold, and the number of strings with the highest similarity is 1.
3. The user interaction behavior pattern matching method according to claim 2, characterized in that: Also includes: Determining that there is no pattern string with a similarity higher than the screening threshold in the user interaction behavior string, obtaining the next interaction behavior, and performing user interaction behavior pattern matching on the next interaction behavior; It is determined that there is no pattern string with a similarity higher than the matching threshold in the user interaction behavior string, the current user interaction behavior string is cleared, and the next interaction behavior is obtained to perform user interaction behavior pattern matching on the next interaction behavior.
4. The user interaction behavior pattern matching method according to claim 1, characterized in that: The generation method of the user interaction behavior string includes: Mapping user interactions to single characters, each character representing a specific interaction; Generate an interaction behavior string based on the mapped single character to represent a series of interaction behaviors of the user; The format of the user interaction behavior string is <abc> , where a, b, and c each represent a single item set data and are arranged in chronological order according to the user interaction behavior.< / abc> 5. The user interaction behavior pattern matching method according to claim 1, characterized in that: The user interaction behavior pattern sequence is composed of i groups of user interaction behavior sets, and the i-th group of user interaction behavior sets is m i , i is a positive integer; In each user interaction behavior set m i In the example, there are multiple interactive behaviors t j , represents the jth interaction behavior in the i-th set, where j is a positive integer; Among them, each interaction behavior set m i There is a temporal relationship between them, and any interaction behavior set m i Interaction behavior within j There is no temporal relationship between them.
6. The user interaction behavior pattern matching method according to claim 1, characterized in that: The pattern string is represented by a single character representing a unique interaction behavior, and each character corresponds to a specific interaction behavior item set; the pattern string includes multiple item set data, and the format of the item set data is<a(bc)d> ; Among them, a represents a single item set data; bc represents an item set data containing multiple interaction items, b and c represent two interaction behavior items, and there is no temporal order relationship in the item set data; d represents another single item set data, and there is a temporal order relationship between the item set data in the pattern string.
7. The user interaction behavior pattern matching method according to claim 1, characterized in that: Also includes: When the length of the current user interaction behavior string is less than the pattern string in the pattern string library, a similarity calculation cold start process is performed.
8. A sequence-based user interaction behavior pattern matching device, characterized in that: include: An acquisition module, used to acquire an initial interaction behavior of a user, and update the initial interaction behavior according to a first mapping relationship to obtain a user interaction behavior string; A similarity module is used to calculate the similarity between the current user interaction behavior string and each pattern string in the pattern string library to obtain a similarity calculation result set; A judgment module, used for comparing the similarity calculation result set with a preset threshold value and selecting a target pattern string that meets the judgment condition; The matching module is used to match the user interaction behavior pattern sequence of the target pattern string through the second mapping relationship, determine the user interaction behavior pattern through mapping matching of the user interaction behavior pattern sequence, and clear the current user interaction behavior string to cyclically match the next interaction behavior until the interaction task is completed.
9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.