Page interaction behavior recognition method, device, readable medium and electronic device

Abstract

The present disclosure relates to a method, device, readable medium and electronic device for identifying page interaction behaviors. The method includes: obtaining interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction position where a preset interaction behavior occurs on the target page and the web page partition to which the interaction position belongs, and the interaction position corresponds to an interaction attribute, and the interaction attribute of the interaction position is interactive or non-interactive; according to the interaction position, determining the preset interaction behavior ratio of each web page partition; judging whether unexpected interaction behaviors occur in each web page partition according to the preset interaction behavior ratio of each web page partition, wherein the unexpected interaction behavior is characterized by the occurrence of the preset interaction behavior at a non-interactive interaction position. In this way, it is convenient for designers to optimize the page design of the landing page based on the recognition results of unexpected interaction behaviors in the landing page, thereby improving design efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular, to a method, device, readable medium, and electronic device for identifying page interaction behavior. Background Art

[0002] Landing pages contain a wealth of elements, such as prompt elements, interactive elements, etc. A landing page that is reasonably designed in terms of structure, color, style, etc. can let users know intuitively which parts of the page are interactive and which parts are not, so that users can make better use of the landing page. However, for landing pages that are not designed reasonably, some non-interactive elements on the page will give users the illusion of interactivity, misleading users to interact with such elements, resulting in a poor user experience and even the loss of the next level of traffic conversion. Therefore, the rationality judgment of the landing page design is very important in the design of the landing page. The rationality judgment of the landing page design can include, for example, whether the design of the page elements is reasonable and whether the page elements can avoid misleading the user. At present, the judgment of the rationality of the landing page design is usually made manually by relevant personnel, which requires the relevant personnel to have relatively professional knowledge in design, aesthetics, etc., and has high professional requirements for personnel. Therefore, it takes a certain amount of human resources, and there is also the problem of low efficiency. Summary of the Invention

[0003] This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] In a first aspect, the present disclosure provides a method for identifying page interaction behaviors, the method comprising:

[0005] Obtaining interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and a web page partition to which the interaction location belongs, and the interaction location corresponds to an interaction attribute, wherein the interaction attribute of the interaction location is interactive or non-interactive;

[0006] Determining a preset interactive behavior ratio for each web page partition based on the interactive position;

[0007] According to the preset interactive behavior ratio of each web page partition, it is determined whether an unexpected interactive behavior occurs in each web page partition, wherein the unexpected interactive behavior is characterized by the preset interactive behavior occurring at an interactive position that is not interactive.

[0008] In a second aspect, the present disclosure provides a device for identifying page interaction behaviors, the device comprising:

[0009] an acquisition module, configured to acquire interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and a web page partition to which the interaction location belongs, and the interaction location has an interaction attribute corresponding to the interaction location, and the interaction attribute of the interaction location is interactive or non-interactive;

[0010] A first determining module is configured to determine a preset interactive behavior ratio of each web page partition according to the interactive position;

[0011] The judgment module is used to judge whether unexpected interaction behavior occurs in each web page partition according to the preset interaction behavior ratio of each web page partition, wherein the unexpected interaction behavior is characterized by the preset interaction behavior occurring at an interaction position that is not interactive.

[0012] In a third aspect, the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processing device, implements the steps of the method described in the first aspect of the present disclosure.

[0013] In a fourth aspect, the present disclosure provides an electronic device, comprising:

[0014] a storage device having a computer program stored thereon;

[0015] A processing device is used to execute the computer program in the storage device to implement the steps of the method described in the first aspect of the present disclosure.

[0016] Through the above technical solution, the interactive behavior data of the target page is obtained, wherein each interactive behavior data includes at least the interactive position where the preset interactive behavior occurs on the target page and the web page partition to which the interactive position belongs, and the interactive position corresponds to an interactive attribute, and the interactive attribute of the interactive position is interactive or non-interactive. Then, according to the interactive position, the preset interactive behavior ratio of each web page partition is determined, and then, according to the preset interactive behavior ratio of each web page partition, whether unexpected interactive behavior occurs in each web page partition is judged. Unexpected interactive behavior is characterized by the occurrence of preset interactive behavior at the non-interactive interactive position. Thus, by obtaining the interactive behavior data of the target page, the preset interactive behavior ratio of each web page partition in the target page is determined, and further, according to the preset interactive behavior ratio, whether unexpected interactive behavior occurs in each web page partition, that is, whether the preset interactive behavior occurs at the non-interactive position in each web page partition. In this way, it is possible to automatically judge whether unexpected interactive behavior occurs in each web page partition of the landing page based on the interactive behavior data of the landing page itself, and then, it is convenient for the landing page designer to optimize the page design of the landing page based on the identification result of the unexpected interactive behavior in the landing page, eliminating the step of manual identification and improving design efficiency.

[0017] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale. In the drawings:

[0019] Figure 1 is a flow chart of a method for identifying page interaction behaviors according to an embodiment of the present disclosure;

[0020] Figure 2 This is an exemplary flow chart of determining whether unexpected interactive behavior occurs in a target web page partition in the page interactive behavior identification method provided by the present disclosure;

[0021] Figure 3 is a block diagram of a device for identifying page interaction behaviors according to an embodiment of the present disclosure;

[0022] Figure 4 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0024] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0025] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0026] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] Figure 1 This is a flow chart of a method for identifying page interaction behaviors according to an embodiment of the present disclosure. Figure 1 As shown, the method may include steps 11 to 13.

[0030] In step 11, the interactive behavior data of the target page is obtained.

[0031] The target page can be any landing page that needs to be identified, such as an H5 page. For example, you can embed a point in the target page in advance to obtain the interactive behavior data of the target page.

[0032] Among them, each interactive behavior data can at least include the interactive position where the preset interactive behavior occurs on the target page and the web page partition to which the interactive position belongs, and the interactive position corresponds to an interactive attribute, which is interactive or non-interactive.

[0033] The preset interactive behavior can be specified based on the user behavior that needs to be identified. For example, the preset interactive behavior can be the user's click behavior on the target page. For another example, the preset interactive behavior can be the user's cursor (or touch point) hovering behavior on the target page. The web page partitions of the target page can be divided based on the structure of the DOM (Document Object Model) of the target page. For example, the area corresponding to each DOM node is a web page partition. The interactive properties of the interactive position can be determined based on whether the page element at the interactive position can trigger an interactive event, that is, based on whether the page element at the interactive position is bound to the front-end event.

[0034] For example, if the preset interactive behavior is the user's click behavior on the target page, accordingly, the location clicked by the user is the interactive location, and if the location clicked by the user can trigger an event, it means that the location clicked by the user is for the user to perform click interaction, so the interactive attribute of the interactive location is interactive; otherwise, if the location clicked by the user cannot trigger any event, it means that the location clicked by the user is not for the user to perform click interaction, so the interactive attribute of the interactive location is non-interactive.

[0035] In step 12, the preset interactive behavior ratio of each web page partition is determined according to the interactive position.

[0036] Based on the interaction locations contained in all the obtained interaction behavior data and the web page partition to which each interaction location belongs, the preset interaction behavior ratio of each web page partition can be determined. In other words, based on the interaction locations obtained, they are aggregated according to the dimensions of the web page partition to determine the number of interaction locations contained in each web page partition, and then the proportion of each web page partition in the total number of interaction locations is determined. For example, the preset interaction behavior ratio target_ratio(A) of web page partition A can be calculated using the following formula:

[0037] target_ratio(A)=target_num(A) / sum

[0038] Wherein, target_num(A) is the number of interaction positions contained in web page partition A, and sum is the total number of interaction positions in the obtained interaction behavior data.

[0039] In step 13, whether unexpected interactive behaviors occur in each web page partition is determined based on the preset interactive behavior ratio of each web page partition.

[0040] The proportion of preset interactive behaviors in a web page partition can reflect, to a certain extent, the frequency of the preset interactive behaviors in the web page partition. When the preset interactive behaviors frequently occur in the web page partition, it can be considered that unexpected interactive behaviors have occurred in the web page partition.

[0041] Among them, the unexpected interactive behavior represents the occurrence of preset interactive behavior at an interactive position that is not interactive.

[0042] For example, if the preset interactive behavior is a user clicking on a target page, determining whether unexpected interactive behavior occurs within a webpage section involves determining whether the webpage section receives multiple clicks when click interaction is not possible, that is, determining whether misclicks frequently occur within the webpage section. An misclick can be understood as a user clicking on an element on a webpage because they are interested in it, i.e., the user mistakenly believes that the element is interactive when in fact it is not.

[0043] By identifying whether unexpected interactive behaviors occur in each web page section and obtaining the recognition results, it is possible to determine which non-interactive elements of the target page always mislead users to interact. Then, based on the recognition results of the target page, the relevant designers of the target page can improve the design of the target page, for example, modifying the design of the web page section where unexpected interactive behaviors occur to reduce the interaction dance for users, or adding interactive design to the web page section where unexpected interactive behaviors occur to enhance the user's interactive experience.

[0044] Through the above technical solution, the interactive behavior data of the target page is obtained, wherein each interactive behavior data includes at least the interactive position where the preset interactive behavior occurs on the target page and the web page partition to which the interactive position belongs, and the interactive position corresponds to an interactive attribute, and the interactive attribute of the interactive position is interactive or non-interactive. Then, according to the interactive position, the preset interactive behavior ratio of each web page partition is determined, and then, according to the preset interactive behavior ratio of each web page partition, whether unexpected interactive behavior occurs in each web page partition is judged. Unexpected interactive behavior is characterized by the occurrence of preset interactive behavior at the non-interactive interactive position. Thus, by obtaining the interactive behavior data of the target page, the preset interactive behavior ratio of each web page partition in the target page is determined, and further, according to the preset interactive behavior ratio, whether unexpected interactive behavior occurs in each web page partition, that is, whether the preset interactive behavior occurs at the non-interactive position in each web page partition. In this way, it is possible to automatically judge whether unexpected interactive behavior occurs in each web page partition of the landing page based on the interactive behavior data of the landing page itself, and then, it is convenient for the landing page designer to optimize the page design of the landing page based on the identification result of the unexpected interactive behavior in the landing page, eliminating the step of manual identification and improving design efficiency.

[0045] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present invention, the corresponding steps above are described in detail below.

[0046] First, the acquisition of the interactive behavior data in step 11 is explained.

[0047] In the present disclosure, the interactive behavior data obtained is not data collected directly by tracking user behavior, but data filtered out after further processing of the data collected by tracking user behavior.

[0048] In the early stage, tracking points can be set for the target page to capture the original behavior data of the user in the process of accessing the target page. The original behavior data of the user captured by tracking points may include but are not limited to the following: the unique identifier of the target page, the length of time the user visits the target page, the height of the target page, the screen width of the device used by the user to access the target page, the screen height of the device, the distance from the location where the preset interactive behavior occurs to the left end of the page, the distance from the location where the preset interactive behavior occurs to the top of the page, the distance from the location where the preset interactive behavior occurs to the left end of the visual area, the distance from the location where the preset interactive behavior occurs to the top of the visual area, the coordinates of the web page element targeted by the preset interactive behavior (including relative X-axis coordinates and relative Y-axis coordinates), and the DOM structure corresponding to the web page element targeted by the preset interactive behavior (which can determine the web page partition to which the location where the preset interactive behavior occurs belongs).

[0049] Once the raw behavioral data has been obtained, appropriate filtering strategies can be used to remove noise data. For example, if a user's visit to a target page is too short, the raw behavioral data corresponding to that visit can be deleted to prevent the short visit data from becoming noise data in the subsequent recognition process.

[0050] Since the screen sizes of the devices used by users to access the target page are not necessarily exactly the same, when using access duration to filter data, it is necessary to consider the differences between screen sizes and perform screen standardization, and filter the original behavior data based on the results of the standardization.

[0051] For example, assuming that the width of the standard screen used in normalization is vw0, if in an original behavior data, the screen width of the device used by the user to access the target page is vw1, and the height of the target page is pageHeight, the screen number SN corresponding to the target page can be calculated using the following formula to perform normalization on the target page:

[0052] SN=(vw1 / vw0)*pageHeight

[0053] Next, based on the user's dwell time ST during their visit to the target page in the original behavior data, the dwell time ST is compared with the screening threshold ST'. If ST is greater than ST', it can be determined that the original behavior data is not noise data and can be used as valid data. Thus, the original behavior data can be used as interactive behavior data for the target page for subsequent identification steps. The screening threshold ST' needs to be determined based on the number of screens SN obtained after normalization. For example, the determination of the screening threshold ST' can refer to the following rules:

[0054] If SN≤1, determine ST'=1000;

[0055] If 1<SN≤2, determine ST'=1000+(SN-1)*1.2;

[0056] If SN>2, determine ST'=1000+(SN-2)*1.5.

[0057] In the above formula, ST' is in ms (milliseconds), that is, 1000 in the formula for determining ST' means 1000 ms (milliseconds).

[0058] Based on the above ideas, the original behavior data collected by the embedding points can be screened for effectiveness to filter out the noise data. The data that is not filtered out will be used as the interactive behavior data of the target page in the subsequent data processing.

[0059] The following explains how, in step 12, the preset interactive behavior ratio of each web page partition is determined based on the interactive position.

[0060] In a possible implementation, step 12 may include the following steps:

[0061] Determine the number of interactive positions contained in each web page partition according to the web page partition to which each interactive position belongs;

[0062] For each web page partition, the ratio of the number of interaction positions corresponding to the web page partition to the total number of interaction positions is determined as the preset interaction behavior ratio of the web page partition.

[0063] As mentioned above, the interactive behavior data includes the interactive positions where preset interactive behaviors occur on the target page. At the same time, the web page partition to which each interactive position belongs can also be known. Therefore, based on the interactive positions in each interactive behavior data of the target page, aggregation can be performed according to the dimension of web page partition to determine the number of interactive positions contained in each of the above-mentioned web page partitions. Furthermore, combined with the total number of interactive positions, the proportion of preset interactive behaviors in each web page partition can be determined separately.

[0064] Optionally, after step 12, in order to ensure recognition accuracy and avoid excessive sample data affecting the final recognition result, and at the same time, in order to ensure the speed of data processing, the method provided by the present disclosure may further include data sampling processing.

[0065] The data sampling process may include the following steps:

[0066] Based on the total sampling volume and the preset interactive behavior ratio corresponding to each web page partition, data is sampled from each web page partition in proportion.

[0067] Among them, assuming that the total sampling amount is n, and the preset interactive behavior ratio corresponding to the web page partition A is target_ratio(A), then the sample extraction number T(A) corresponding to the web page partition A is calculated by the following formula: T(A)=n*target_ratio(A).

[0068] After determining the number of samples to be drawn, sampling can be performed in the interactive locations included in each web page partition. For example, random sampling can be used as the sampling method.

[0069] Optionally, after sampling is completed, the sampled data can be stored and / or transmitted. For example, after obtaining sample data corresponding to the total sample amount through data sampling, the sample data can be spliced ​​in a sequence format to form serialized data of the sample data for storage. For another example, the stored serialized data can be deserialized to convert it into structured data of the sample data for transmission.

[0070] For example, if the format of a sample data is: vw, vh, px, py, cx, cy, x, y, target. Among them, vw is the screen width of the device used by the user to access the target page, vh is the screen height of the device, px is the distance from the position where the preset interactive behavior occurs to the left end of the page, py is the distance from the position where the preset interactive behavior occurs to the top of the page, cx is the distance from the position where the preset interactive behavior occurs to the left end of the visual area, cy is the distance from the position where the preset interactive behavior occurs to the top of the visual area, x is the coordinate of the web page element targeted by the preset interactive behavior relative to the X axis, y is the coordinate of the web page element targeted by the preset interactive behavior relative to the Y axis, and target is the DOM structure corresponding to the web page element targeted by the preset interactive behavior. Then, if a total of n sample data are sampled, the sequence data of the sample data after splicing can be {vw(1), vh(1), px(1), py(1), cx(1), cy(1), x(1), y(1), target(1); vw(2), vh(2), px(2), py(2), cx(2), cy(2), x(2), y(2), target(2); vw(3), vh(3), px(3), py(3), cx(3), cy(3), x(3), y(3), target(3); ...; vw(n), vh(n), px(n), py(n), cx(n), cy(n), x(n), y(n), target(n)}.

[0071] After deserializing the serialized data, the structured data of the converted sample data can refer to the following code.

[0072]

[0073]

[0074] Based on the structured data of the sample data above, the front-end webview can also perform XPath parsing and adaptation to aggregate and render the data. The purpose of rendering the data here is to generate a heat map corresponding to the preset interactive behavior.

[0075] A heatmap is a data visualization technique that uses color to represent the absolute magnitude of a phenomenon in two-dimensional space. Color changes can occur through hue or intensity, providing users with clear visual cues about how the phenomenon is clustered or changing spatially. For example, a click heatmap is a graph that aggregates and summarizes user click behavior on a web page. Areas with more clicks appear brighter, while areas with fewer clicks appear darker. Heatmaps can be used to analyze user interest in a page and the strength of element interactions, thereby identifying potential issues with the page's layout, structure, content, and style.

[0076] Step 13 can be performed after the front-end webview completes the rendering of the heat map, or it can be performed during the rendering process of the heat map.

[0077] The following explains how, in step 13, whether unexpected interactive behaviors occur in each web page partition according to the preset interactive behavior ratio of each web page partition is determined.

[0078] In a possible implementation, each web page partition may be used as a target web page partition and the following operations may be performed, including steps 21 to 23. Figure 2 shown.

[0079] In step 21, a behavior evaluation value of the target web page partition is determined according to a preset interactive behavior ratio of the target web page partition.

[0080] In a possible embodiment, step 21 may include the following steps:

[0081] Get the partition size of the target web page partition;

[0082] If the partition size of the target webpage partition is smaller than the preset size, the preset interactive behavior ratio of the target webpage partition is determined as the behavior evaluation value of the target webpage partition;

[0083] If the partition size of the target web page partition is greater than or equal to the preset size, determine the unexpected interaction behavior evaluation factor corresponding to the target web page partition, and determine the behavior evaluation value of the target web page partition as the product of the preset interaction behavior ratio of the target web page partition and the unexpected interaction behavior evaluation factor corresponding to the target web page partition.

[0084] The preset size can be set based on the size of the standard screen used above. For example, assuming that the standard screen is 375 (pixels) wide and 667 (pixels) high, the preset size can be set to 375 (pixels) wide and 20 (pixels) high.

[0085] When the partition size of the target web page partition is smaller than the preset size, it can be considered that the scope of the target web page partition is small. No matter how the interactive positions and non-interactive positions are distributed in the target web page partition, the impact on the judgment of unexpected interactive behaviors in the entire target web page partition is negligible. Therefore, the preset interactive behavior ratio can be directly used as the behavior evaluation value corresponding to the target web page partition to evaluate whether unexpected interactive behaviors occur in the target web page partition.

[0086] When the partition size of the target web page partition is greater than or equal to the preset size, it can be considered that the scope of the target web page partition is large, and the distribution of interactive positions and non-interactive interactive positions in the target web page partition has a great influence on the judgment of unexpected interactive behaviors in the entire target web page partition. Therefore, in order to ensure the accuracy of unexpected behavior identification, on the basis of the preset interactive behavior ratio, it is necessary to further combine the unexpected interactive behavior evaluation factor to determine the behavior evaluation value of the target web page partition.

[0087] For example, the unexpected interaction behavior evaluation factor corresponding to the target web page partition can be determined in the following way:

[0088] Based on the preset interactive behavior ratio of each web page partition, sample data is proportionally extracted from the interactive behavior data;

[0089] From the sample data, determine the target data whose interactive attributes are non-interactive;

[0090] Calculate the dispersion of unexpected interaction behaviors of each web page partition based on the interaction position in the target data and the center position of the region corresponding to each web page partition;

[0091] Normalizing the discrete degree of the unexpected interactive behavior of the target web page partition according to the discrete degree of the unexpected interactive behavior of each web page partition to obtain a normalized result of the target web page partition;

[0092] The normalized results of the target web page partitions are negatively processed to obtain the unexpected interaction behavior evaluation factors corresponding to the target web page partitions.

[0093] Among them, according to the preset interactive behavior ratio of each web page partition, sample data is proportionally extracted from the interactive behavior data. The description of data sampling processing has been given in the previous article and will not be repeated here.

[0094] After extracting sample data of each web page partition in proportion, target data having an interactive attribute of non-interactive can be determined based on the interactive attributes of the interactive positions in the sample data.

[0095] Furthermore, for each web page partition, the dispersion of the unexpected interactive behavior of the web page partition can be calculated based on the interactive position within the web page partition in the target data and the center position of the region corresponding to the web page partition.

[0096] The dispersion of unexpected interactive behaviors of a web page partition can be obtained by calculating the standard deviation circle radius of the web page partition. For example, the standard deviation circle radius r of web page partition B (i.e., the dispersion of unexpected interactive behaviors) can be obtained by the following formula:

[0097]

[0098] Among them, (x0, y0) is the center position of the area corresponding to the web page partition B, (x j ,y j ) is the j-th interaction position in the target data located in the web page partition B, and z is the total number of interaction positions in the target data located in the web page partition B.

[0099] After calculating the discrete degrees of unexpected interactive behaviors of each web page partition in the above manner, in order to ensure the consistency of the data scale, the discrete degrees of unexpected interactive behaviors of the target web page partition can be further normalized to obtain the normalized results of the target web page partition, and the normalized results of the target web page partition can be negatively processed to obtain the unexpected interactive behavior evaluation factors corresponding to the target web page partition.

[0100] For example, the normalized result Nr of the target web page partition can be obtained according to the following formula:

[0101] Nr=(r i -r min ) / (r max -r min )

[0102] Among them, r i is the dispersion of unexpected interactive behaviors of the target web page partition, r min is the minimum value of the calculated discreteness of unexpected interactive behaviors of each web page partition, r max is the maximum value among the calculated discrete degrees of unexpected interactive behaviors of each web page partition.

[0103] After obtaining the normalized results for the target web page partition, in order to ensure the accuracy of data processing, the normalized results can be further negatively processed so that the normalized results for the target web page partition are within the positive range. For example, the unexpected interaction behavior evaluation factor α corresponding to the target web page partition can be obtained according to the following formula:

[0104] α=k(1-Nr)+m

[0105] Wherein, k is a preset adjustment coefficient, Nr is the normalized result of the target web page partition, and m is a preset constant. For example, m can be 0.2.

[0106] Therefore, for a target web page partition whose partition size is greater than or equal to the preset size, the product of the preset interactive behavior ratio of the target web page partition and the unexpected interactive behavior evaluation factor corresponding to the target web page partition is determined as the behavior evaluation value of the target web page partition.

[0107] After determining the behavior evaluation value of the target web page partition, it is possible to further determine whether unexpected interactive behavior occurs in the target web page partition based on the behavior evaluation value:

[0108] In step 22, if the behavior evaluation value of the target web page partition is greater than the judgment threshold corresponding to the target web page partition, it is determined that an unexpected interactive behavior occurs in the target web page partition;

[0109] In step 23 , if the behavior evaluation value of the target webpage partition is less than or equal to the judgment threshold corresponding to the target webpage partition, it is determined that no unexpected interactive behavior occurs in the target webpage partition.

[0110] The determination threshold corresponding to the target webpage partition may be determined according to the number of interactive positions within the target webpage partition.

[0111] For example, the judgment threshold corresponding to the target web page partition can be determined in the following way:

[0112] Obtain the number Ct of non-interactive interactive locations in the target web page partition;

[0113] If Ct<10, set the judgment threshold corresponding to the target web page partition to 0.1;

[0114] If 10≤Ct<20, set the judgment threshold corresponding to the target web page partition to (1 / Ct);

[0115] If Ct≥20, the judgment threshold corresponding to the target web page partition is set to 0.05.

[0116] Figure 3 This is a block diagram of a device for identifying page interaction behaviors according to an embodiment of the present disclosure. Figure 3 As shown, the device 30 may include:

[0117] An acquisition module 31 is configured to acquire interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and the web page partition to which the interaction location belongs, and each interaction location has an interaction attribute corresponding to it, and the interaction attribute of the interaction location is interactive or non-interactive;

[0118] A first determining module 32 is configured to determine a preset interactive behavior ratio of each web page partition based on the interactive position;

[0119] The judgment module 33 is configured to judge whether unexpected interaction occurs in each web page partition according to the preset interaction behavior ratio of each web page partition, wherein the unexpected interaction behavior is characterized by the preset interaction behavior occurring at a non-interactive interaction position.

[0120] Optionally, the judgment module 33 is configured to respectively use each of the web page partitions as a target web page partition, and judge whether the unexpected interactive behavior occurs in the target web page partition through the following submodules:

[0121] A first determining submodule is configured to determine a behavior evaluation value of a target web page partition according to a preset interactive behavior ratio of the target web page partition;

[0122] a second determining submodule, configured to determine that the unexpected interactive behavior occurs in the target webpage partition if the behavior evaluation value of the target webpage partition is greater than a judgment threshold corresponding to the target webpage partition;

[0123] a third determining submodule, configured to determine that the unexpected interactive behavior has not occurred in the target webpage partition if the behavior evaluation value of the target webpage partition is less than or equal to the judgment threshold corresponding to the target webpage partition;

[0124] The judgment threshold corresponding to the target webpage partition is determined according to the number of interactive positions in the target webpage partition.

[0125] Optionally, the first determining submodule includes:

[0126] An acquisition submodule, configured to acquire the partition size of the target web page partition;

[0127] a fourth determining submodule, configured to determine a preset interactive behavior ratio of the target webpage partition as a behavior evaluation value of the target webpage partition if the partition size of the target webpage partition is smaller than a preset size;

[0128] The fifth determination submodule is used to determine the unexpected interaction behavior evaluation factor corresponding to the target web page partition if the partition size of the target web page partition is greater than or equal to the preset size, and determine the product of the preset interaction behavior ratio of the target web page partition and the unexpected interaction behavior evaluation factor corresponding to the target web page partition as the behavior evaluation value of the target web page partition.

[0129] Optionally, the device 30 determines the unexpected interaction behavior evaluation factor corresponding to the target webpage partition through the following modules:

[0130] A sampling module, configured to extract sample data in proportion from the interaction behavior data according to a preset interaction behavior ratio of each web page partition;

[0131] A second determining module is configured to determine target data having an interactive attribute of non-interactive from the sample data;

[0132] a calculation module, configured to calculate a dispersion of unexpected interaction behaviors of each web page partition based on the interaction position in the target data and the center position of the region corresponding to each web page partition;

[0133] a normalization module, configured to normalize the non-expected interaction behavior dispersion of the target web page partition according to the non-expected interaction behavior dispersion of each web page partition, and obtain a normalized result of the target web page partition;

[0134] The negative processing module is used to perform negative processing on the normalized result of the target web page partition to obtain an unexpected interaction behavior evaluation factor corresponding to the target web page partition.

[0135] Optionally, the negative processing module is configured to obtain an unexpected interaction behavior evaluation factor α corresponding to the target webpage partition according to the following formula:

[0136] α=k(1-Nr)+m

[0137] Wherein, k is a preset adjustment coefficient, Nr is the normalized result of the target web page partition, and m is a preset constant.

[0138] Optionally, the first determining module 32 includes:

[0139] a sixth determining submodule, configured to determine the number of interactive positions included in each web page partition according to the web page partition to which each interactive position belongs;

[0140] The seventh determining submodule is configured to determine, for each web page partition, a ratio of the number of interaction positions corresponding to the web page partition to the total number of interaction positions as a preset interaction behavior ratio of the web page partition.

[0141] Optionally, the preset interactive behavior is a click behavior of the user.

[0142] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0143] Reference below Figure 4, which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0144] like Figure 4 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0145] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0146] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0147] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0148] In some embodiments, the client or server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0149] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0150] The computer-readable medium carries one or more programs. When executed by the electronic device, the one or more programs cause the electronic device to: obtain interactive behavior data for a target page, wherein each interactive behavior data set includes at least an interactive location on the target page where a preset interactive behavior occurs and the webpage section to which the interactive location belongs, and each interactive location has an interactive attribute corresponding to it, wherein the interactive attribute is interactive or non-interactive; determine the preset interactive behavior ratio for each webpage section based on the interactive location; and determine whether an unexpected interactive behavior occurs within each webpage section based on the preset interactive behavior ratio for each webpage section, wherein the unexpected interactive behavior indicates that the preset interactive behavior occurs at a non-interactive interactive location.

[0151] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The modules described in the embodiments of the present disclosure may be implemented in software or hardware. In some cases, the name of a module does not necessarily define the module itself. For example, an acquisition module may also be described as a "module for acquiring interactive behavior data of a target page."

[0154] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0155] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0156] According to one or more embodiments of the present disclosure, a method for identifying page interaction behavior is provided, the method comprising:

[0157] Obtaining interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and a web page partition to which the interaction location belongs, and the interaction location corresponds to an interaction attribute, wherein the interaction attribute of the interaction location is interactive or non-interactive;

[0158] Determining a preset interactive behavior ratio for each web page partition based on the interactive position;

[0159] According to the preset interactive behavior ratio of each web page partition, it is determined whether an unexpected interactive behavior occurs in each web page partition, wherein the unexpected interactive behavior is characterized by the preset interactive behavior occurring at an interactive position that is not interactive.

[0160] According to one or more embodiments of the present disclosure, a method for identifying page interaction behaviors is provided, wherein determining whether unexpected interaction behaviors occur in each web page partition based on the preset interaction behavior ratio of each web page partition includes:

[0161] Each of the web page partitions is used as a target web page partition, and the following operations are performed:

[0162] Determining a behavior evaluation value of the target web page partition according to a preset interactive behavior ratio of the target web page partition;

[0163] If the behavior evaluation value of the target webpage partition is greater than the judgment threshold corresponding to the target webpage partition, it is determined that the unexpected interactive behavior occurs in the target webpage partition;

[0164] If the behavior evaluation value of the target webpage partition is less than or equal to the judgment threshold corresponding to the target webpage partition, it is determined that the unexpected interactive behavior does not occur in the target webpage partition;

[0165] The judgment threshold corresponding to the target webpage partition is determined according to the number of interactive positions in the target webpage partition.

[0166] According to one or more embodiments of the present disclosure, a method for identifying page interaction behaviors is provided, wherein determining a behavior evaluation value of a target webpage partition based on a preset interaction behavior ratio of the target webpage partition comprises:

[0167] Obtaining the partition size of the target web page partition;

[0168] If the partition size of the target webpage partition is smaller than a preset size, determining a preset interactive behavior ratio of the target webpage partition as a behavior evaluation value of the target webpage partition;

[0169] If the partition size of the target web page partition is greater than or equal to the preset size, determine the unexpected interaction behavior evaluation factor corresponding to the target web page partition, and determine the product of the preset interaction behavior ratio of the target web page partition and the unexpected interaction behavior evaluation factor corresponding to the target web page partition as the behavior evaluation value of the target web page partition.

[0170] According to one or more embodiments of the present disclosure, a method for identifying page interaction behaviors is provided, wherein the unexpected interaction behavior evaluation factor corresponding to the target web page partition is determined by:

[0171] Extracting sample data proportionally from the interaction behavior data based on a preset interaction behavior ratio of each web page partition;

[0172] Determining target data whose interactive attribute is non-interactive from the sample data;

[0173] Calculating the dispersion of unexpected interaction behaviors of each web page partition according to the interaction position in the target data and the center position of the region corresponding to each web page partition;

[0174] Normalizing the unexpected interaction behavior dispersion of the target web page partition according to the unexpected interaction behavior dispersion of each web page partition to obtain a normalized result of the target web page partition;

[0175] Negative processing is performed on the normalized result of the target web page partition to obtain an unexpected interaction behavior evaluation factor corresponding to the target web page partition.

[0176] According to one or more embodiments of the present disclosure, a method for identifying page interaction behaviors is provided, wherein negative processing is performed on the normalized result of the target web page partition to obtain an unexpected interaction behavior evaluation factor corresponding to the target web page partition, including:

[0177] The unexpected interaction behavior evaluation factor α corresponding to the target web page partition is obtained according to the following formula:

[0178] α=k(1-Nr)+m

[0179] Wherein, k is a preset adjustment coefficient, Nr is the normalized result of the target web page partition, and m is a preset constant.

[0180] According to one or more embodiments of the present disclosure, a method for identifying page interaction behaviors is provided, wherein determining a preset interaction behavior ratio of each web page partition based on the interaction position includes:

[0181] Determining the number of interactive positions contained in each web page partition according to the web page partition to which each interactive position belongs;

[0182] For each web page partition, the ratio of the number of interaction positions corresponding to the web page partition to the total number of interaction positions is determined as the preset interaction behavior ratio of the web page partition.

[0183] According to one or more embodiments of the present disclosure, a method for identifying page interaction behaviors is provided, wherein the preset interaction behaviors are user click behaviors.

[0184] According to one or more embodiments of the present disclosure, a device for identifying page interaction behavior is provided, the device comprising:

[0185] an acquisition module, configured to acquire interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and a web page partition to which the interaction location belongs, and the interaction location has an interaction attribute corresponding to the interaction location, and the interaction attribute of the interaction location is interactive or non-interactive;

[0186] A first determining module is configured to determine a preset interactive behavior ratio of each web page partition according to the interactive position;

[0187] The judgment module is used to judge whether unexpected interaction behavior occurs in each web page partition according to the preset interaction behavior ratio of each web page partition, wherein the unexpected interaction behavior is characterized by the preset interaction behavior occurring at an interaction position that is not interactive.

[0188] According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processing device, the steps of the page interaction behavior recognition method described in any embodiment of the present disclosure are implemented.

[0189] According to one or more embodiments of the present disclosure, there is provided an electronic device, including:

[0190] a storage device having a computer program stored thereon;

[0191] A processing device is used to execute the computer program in the storage device to implement the steps of the page interaction behavior identification method described in any embodiment of the present disclosure.

[0192] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0193] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0194] Although the subject matter has been described using language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims. Regarding the apparatus in the above-described embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method and will not be elaborated upon here.

Claims

1. A page interaction behavior recognition method, characterized in that: The method comprises: Obtaining interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and a web page partition to which the interaction location belongs, and the interaction location corresponds to an interaction attribute, wherein the interaction attribute of the interaction location is interactive or non-interactive; Determining a preset interactive behavior ratio for each web page partition based on the interactive position; Determining whether an unexpected interactive behavior occurs in each web page partition based on the preset interactive behavior ratio of each web page partition, wherein the unexpected interactive behavior is characterized by the preset interactive behavior occurring at a non-interactive interactive position; The determining whether unexpected interaction occurs in each web page partition according to the preset interaction behavior ratio of each web page partition includes: Each of the web page partitions is used as a target web page partition, and the following operations are performed: Determining a behavior evaluation value of the target web page partition according to a preset interactive behavior ratio of the target web page partition; If the behavior evaluation value of the target webpage partition is greater than the judgment threshold corresponding to the target webpage partition, it is determined that the unexpected interactive behavior occurs in the target webpage partition; If the behavior evaluation value of the target webpage partition is less than or equal to the judgment threshold corresponding to the target webpage partition, it is determined that the unexpected interactive behavior does not occur in the target webpage partition; The judgment threshold corresponding to the target webpage partition is determined according to the number of interactive positions in the target webpage partition.

2. The method according to claim 1, characterized in that Determining the behavior evaluation value of the target webpage partition according to the preset interactive behavior ratio of the target webpage partition includes: Obtaining the partition size of the target web page partition; If the partition size of the target webpage partition is smaller than a preset size, determining a preset interactive behavior ratio of the target webpage partition as a behavior evaluation value of the target webpage partition; If the partition size of the target web page partition is greater than or equal to the preset size, determine the unexpected interaction behavior evaluation factor corresponding to the target web page partition, and determine the product of the preset interaction behavior ratio of the target web page partition and the unexpected interaction behavior evaluation factor corresponding to the target web page partition as the behavior evaluation value of the target web page partition.

3. The method according to claim 2, characterized in that The unexpected interaction behavior evaluation factor corresponding to the target web page partition is determined in the following manner: Extracting sample data proportionally from the interaction behavior data based on a preset interaction behavior ratio of each web page partition; Determining target data whose interactive attribute is non-interactive from the sample data; Calculating the dispersion of unexpected interaction behaviors of each web page partition according to the interaction position in the target data and the center position of the region corresponding to each web page partition; Normalizing the unexpected interaction behavior dispersion of the target web page partition according to the unexpected interaction behavior dispersion of each web page partition to obtain a normalized result of the target web page partition; Negative processing is performed on the normalized result of the target web page partition to obtain an unexpected interaction behavior evaluation factor corresponding to the target web page partition.

4. The method according to claim 3, characterized in that The negative processing of the normalized result of the target web page partition to obtain the unexpected interaction behavior evaluation factor corresponding to the target web page partition includes: The unexpected interaction behavior evaluation factor corresponding to the target web page partition is obtained according to the following formula α : α = k (1-Nr)+ m in, k is the preset adjustment coefficient, Nr is the normalized result of the target web page partition, m is a preset constant.

5. The method according to claim 1, wherein Determining the preset interaction behavior proportion of each web page partition according to the interaction position includes: Determining the number of interactive positions contained in each web page partition according to the web page partition to which each interactive position belongs; For each web page partition, the ratio of the number of interaction positions corresponding to the web page partition to the total number of interaction positions is determined as the preset interaction behavior ratio of the web page partition.

6. The method according to any one of claims 1 to 5, characterized in that The preset interactive behavior is a click behavior of the user.

7. A device for identifying page interaction behavior, characterized in that: The device comprises: an acquisition module, configured to acquire interaction behavior data of a target page, wherein each interaction behavior data includes at least an interaction location on the target page where a preset interaction behavior occurs and a web page partition to which the interaction location belongs, and the interaction location has an interaction attribute corresponding to the interaction location, and the interaction attribute of the interaction location is interactive or non-interactive; A first determining module is configured to determine a preset interactive behavior ratio of each web page partition according to the interactive position; a determination module, configured to determine whether an unexpected interaction behavior occurs in each web page partition based on the preset interaction behavior ratio of each web page partition, wherein the unexpected interaction behavior is characterized by the preset interaction behavior occurring at a non-interactive interaction location; The judgment module is used to respectively use each of the web page partitions as a target web page partition, and to judge whether the unexpected interactive behavior occurs in the target web page partition through the following submodules: A first determining submodule is configured to determine a behavior evaluation value of a target web page partition according to a preset interactive behavior ratio of the target web page partition; a second determining submodule, configured to determine that the unexpected interactive behavior occurs in the target webpage partition if the behavior evaluation value of the target webpage partition is greater than a judgment threshold corresponding to the target webpage partition; a third determining submodule, configured to determine that the unexpected interactive behavior has not occurred in the target webpage partition if the behavior evaluation value of the target webpage partition is less than or equal to the judgment threshold corresponding to the target webpage partition; The judgment threshold corresponding to the target webpage partition is determined according to the number of interactive positions in the target webpage partition.

8. A computer-readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processing device, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: include: a storage device having a computer program stored thereon; A processing device, configured to execute the computer program in the storage device to implement the steps of the method according to any one of claims 1 to 6.

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