Page rendering method, device and electronic device

Abstract

The present application relates to the technical field of commercial information systems, and in particular to a page rendering method, device and electronic device. It includes the following steps: obtaining web page resources used by the current user when browsing a page within the first sliding time window of the current user; before completing the collection of web page resources of the current user browsing the page within the first sliding time window, using an association rule-based recommendation algorithm to generate a preloaded set of web page resources for the current user based on the web page resources used by the user's current browsing page; obtaining web page resources used by all users when browsing the page respectively; after completing the collection, using a user-based collaborative filtering recommendation algorithm to generate a preloaded set of web page resources based on the web page resources used by the current user and other users when browsing the page; when rendering the subsequent browsing web page resources of the current user, first obtain the web page resources of the current browsing page from the local preloaded set of web page resources. The present application can improve the speed of page rendering.

Description

Technical Field

[0001] The present application relates to the technical field of commercial information systems, and in particular to a page rendering method, device and electronic device. Background Art

[0002] On e-commerce platforms, the loading speed of product pages is an important factor affecting user experience and purchase conversion rate.

[0003] However, due to the significant differences in browsing behaviors and preferences of different users, the web resources they need to load are also different. Therefore, how to accurately preload web resources for each user group to improve page rendering speed has become a technical problem that needs to be solved. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a page rendering method, device and electronic device, which can improve the speed of page rendering.

[0005] In a first aspect, the present application provides a page rendering method, the page rendering method comprising the following steps:

[0006] Obtain the web page resources used by the current user when browsing the page within the first sliding time window of the current user;

[0007] Before completing the collection of web page resources of the current user's browsing page within the first sliding time window, using an association rule-based recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user's browsing page;

[0008] Get the web page resources used by each user when browsing the page;

[0009] After completing the collection of web page resources of the current user browsing pages within the first sliding time window, using a user-based collaborative filtering recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user and other users when browsing pages within the first sliding time window;

[0010] Sending a preloaded set of web resources of the current user to the device of the current user;

[0011] When rendering the subsequent web page resources browsed by the current user, the web page resources of the current browsing page are first obtained from the local web page resource preload collection.

[0012] Optionally, the page rendering method further includes the following steps:

[0013] The controllability of the current user is calculated according to the usage rate of the web resource preloading set used by the user through a preset controllability evaluation function;

[0014] The size of the user group referenced by the user-based collaborative filtering recommendation algorithm is adjusted according to the controllability of the current user, so that when the controllability of the current user is lower, the user group referenced by the current user is larger.

[0015] Optionally, before completing the collection of web page resources of the current user's browsing page within the first sliding time window, using an association rule-based recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the user's current browsing page, the following steps are included:

[0016] Mark each web resource with its unique resource identifier;

[0017] Collecting the access data of web resources of all users to obtain the transition probability between each pair of web resources, wherein the transition probability is the probability that a user visits another web resource after visiting a certain web resource;

[0018] According to the Markov model, the transition probability between each pair of web resources and the sequence of web resources used by the current user when browsing the page, the predicted probability of the current user accessing other web resources is predicted;

[0019] The web page resources whose predicted probabilities are above the predicted probability threshold are used as a web page resource preloading set.

[0020] Optionally, after completing the collection of web page resources of the current user browsing pages within the first sliding time window, using a user-based collaborative filtering recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user and other users when browsing pages within the first sliding time window, the steps include:

[0021] Based on the similarity between the web page resources in the first sliding time window of the current user and the web page resources in the first sliding time window of other users, clustering is performed with the current user as the core to obtain the reference customer group of the current user;

[0022] Obtaining web page resources with the highest number of usage from a reference customer group of a first preset number of current users as a web page resource preloading set;

[0023] The lower the controllability of the current user, the larger the K value in the KNN algorithm of the current customer.

[0024] Optionally, the preset controllability evaluation function is:

[0025]

[0026] Wherein, S(x) is the controllability, k is the first empirical parameter, x0 is the second empirical parameter, and x is the usage rate of the user using the webpage resource preloading set.

[0027] In a second aspect, the present application provides a page rendering device, the page rendering device comprising:

[0028] A data acquisition module is used to acquire web page resources used by the current user when browsing a page within the first sliding time window of the current user, and to acquire web page resources used by each user when browsing a page;

[0029] A first generating module, before completing the collection of web page resources of the current user browsing page within the first sliding time window, uses a recommendation algorithm based on association rules to generate a web page resource preload set for the current user according to the web page resources used by the user's current browsing page;

[0030] The second generation module, after completing the collection of web page resources of the current user browsing the page within the first sliding time window, uses a user-based collaborative filtering recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user and other users when browsing the page within the first sliding time window;

[0031] A data receiving module, used for receiving and saving the webpage resource preloading set sent by the first generating module or the second generating module;

[0032] The rendering module is used to obtain the web page resources of the current browsing page from the local web page resource preload collection when rendering the subsequent browsing web page resources of the current user.

[0033] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a page rendering method as described in any one of the first aspects.

[0034] Compared with the prior art, the technical solution provided by this application has the following advantages:

[0035] One of the beneficial effects of the present application is that the present application can realize more accurate and efficient web resource preloading by combining the recommendation algorithm based on association rules and the collaborative filtering algorithm based on users. This combination method fully considers the particularity of web resource preloading and is different from the application of traditional recommendation systems in product recommendation.

[0036] In the field of product recommendations, users are usually faced with a range of choices that they can actively make. The recommendation system predicts which products are most likely to attract users by analyzing their purchase history and browsing habits. Such recommendations are often based on users' explicit preferences or implicit preferences derived from comparing the user's behavior with that of other similar users.

[0037] In contrast, web resource preloading must handle more dynamic and immediate needs. Users' browsing behavior is more random and greatly influenced by instant information, and users often have not explicitly expressed their preferences for certain types of resources. Therefore, although the single use of association rule-based recommendation algorithms can capture strong correlation patterns in user behavior, such as the web page paths that users frequently visit, this method tends to ignore those resources that are only weakly associated with the user's current browsing content but may attract the user's attention due to their presentation or content.

[0038] In addition, although directly applying user-based collaborative filtering algorithms can help discover users' potential interests and recommend resources by analyzing similar behaviors with other users, this method may not be immediately effective when the amount of data is insufficient or the user behavior is not typical enough, especially when the user browsing behavior changes rapidly.

[0039] Therefore, the present application first uses an algorithm based on association rules to quickly predict the web resources that need to be preloaded to assist the browsing behavior of fixed users, and then uses a user-based collaborative filtering algorithm to expand the web resources that need to be preloaded, thereby overcoming the limitations of a single algorithm and taking advantage of the complementary advantages of the two. The method provided by the present application improves the accuracy and timeliness of preloading web resources, thereby increasing the speed of page rendering.

[0040] The second beneficial effect of the present application is that, by combining a recommendation algorithm based on association rules and a collaborative filtering recommendation algorithm based on users, the present application realizes a dynamic and adaptive web resource preloading method. In particular, after the initial recommendation is made through association rules, a user controllability evaluation is introduced into the process of the collaborative filtering recommendation algorithm, thereby optimizing the accuracy and efficiency of the preloading process.

[0041] In this method, first, association rules are used to quickly identify and preload resources that users may need immediately. This step is based on the user's historical behavior data and can quickly respond to the user's direct needs. Then, the system evaluates the controllability of the user's browsing behavior by analyzing the user's browsing behavior, that is, the predictability and consistency of the user's behavior. For example, when users browse casually, the behavior pattern is relatively scattered, and the preloaded set of web resources used is small, so the controllability is low at this time; while when users are deeply researching a product or content, they use a large preloaded set of web resources, showing a coherent and purposeful browsing behavior, and the controllability is high at this time.

[0042] This controllability assessment enables the system to adjust the preloading strategy according to the different stages of user behavior. When user behavior is controllable, the system can use a smaller reference group for collaborative filtering, which not only reduces the consumption of computing power, but also improves the relevance and accuracy of recommendations, because the user's needs are clearer and closer to the user behavior within a specific group. Conversely, when user behavior is uncontrollable, the system will expand the scope of the reference group, which helps capture a wider range of potential points of interest.

[0043] Furthermore, when user behavior is controllable, that is, when the behavior pattern is clear and consistent, the system tends to use a smaller user group for precise resource preloading. Since the size of the web resource preloading set is fixed, the smaller the user group, the easier it is for the specific loaded content to enter the web resource preloading set. The specific loaded content usually includes specific resources for specific content needs, such as large images or videos. This precise preloading helps ensure that resource-intensive pages can be loaded quickly and efficiently, improving the access speed and browsing quality of the target content.

[0044] On the contrary, when user behavior is uncontrollable, that is, browsing randomly and lacking clear goals, the system expands the scope of the reference group. Since the size of the web resource preloading set is fixed, when the reference group is larger, there will be more types and numbers of general loading resources. General loading resources will occupy most of the web resource preloading set, so that general resources such as web UI elements and basic scripts can be preloaded. Although these resources are small, because they are widely shared and reused, their preloading can also improve the rendering speed of multiple pages. This strategy effectively reduces the unnecessary preloading of large resources, avoids unnecessary occupation of bandwidth and user storage, and ensures efficient operation of the system and a good user experience.

[0045] Similarly, when subsequent users use the web resource preloading collection, the user's controllability will change. Once the user's browsing behavior becomes targeted, the current user's reference group will be narrowed, thereby switching to a more precise preloading mode, reducing the size of the reference group, and focusing on loading resources directly related to the user's current needs, such as specific pictures, videos or detailed content pages. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a page rendering method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, not all of the embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0049] In a first aspect, an embodiment of the present application provides a page rendering method, the page rendering method comprising the following steps:

[0050] S101: Acquire web page resources used by the current user when browsing pages within the first sliding time window of the current user.

[0051] Specifically, each webpage resource is marked with its unique resource identifier. In the embodiment of the present application, the first sliding time window is half an hour. During the time of the first sliding time window, the unique resource identifier of the webpage resource called by the current user browsing the page is obtained from the background, so as to form the webpage resource used by the current user when browsing the page within the first sliding time window of the current user.

[0052] S102: before completing the collection of web page resources of the current user browsing page within the first sliding time window, using an association rule-based recommendation algorithm to generate a web page resource preload set for the current user according to the web page resources used by the user's current browsing page.

[0053] Specifically, the following steps are included:

[0054] The access data of web resources of all users are collected to obtain the transition probability between each pair of web resources, wherein the transition probability is the probability that a user visits another web resource after visiting a certain web resource.

[0055] Specifically, each unique resource identifier of a unique webpage resource is defined as a state.

[0056] The access data includes web resources accessed successively, for example, calling web resource A first and then calling web resource B, and the number of times such transition occurs among all users. The number of times the transition occurs is divided by the total number of users to obtain the transition probability.

[0057] Based on the Markov model, the transition probability between each pair of web resources and the sequence of web resources currently collected when the current user browses the page, the predicted probability of the current user accessing other web resources is predicted.

[0058] Specifically, a Markov chain is used to calculate the possible probabilities that the user may access other network resources next time according to the order in which the current user accesses network resources, as the predicted probability of other network resources.

[0059] The web page resources with predicted probabilities above a predicted probability threshold are used as a preloaded set of web page resources. The predicted probability threshold is a value set in advance by a human based on experience.

[0060] The web page resources used by each user when browsing the page are obtained respectively. Specifically, after the web page resources of the first sliding time window of the current user are collected, the web page resources used by the remaining users when browsing the page within the previous preset time period are collected.

[0061] S103: After completing the collection of web page resources of the current user browsing the page within the first sliding time window, a user-based collaborative filtering recommendation algorithm is used to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user and other users when browsing the page within the first sliding time window.

[0062] Specifically, it also includes calculating the controllability of the current user according to the usage rate of the webpage resource preloading set used by the user through a preset controllability evaluation function;

[0063] The size of the user group referenced by the user-based collaborative filtering recommendation algorithm is adjusted according to the controllability of the current user, so that when the controllability of the current user is lower, the user group referenced by the current user is larger.

[0064] Specifically, the following steps are included:

[0065] The coverage ratio between the web page resources used by the current user in the first sliding time window and the web page resources used by other users when browsing pages in the preset time period is used as a measurement standard. The web page resources in the first sliding time window of the current user and the web page resources in the first sliding time window of other users are calculated by the KNN algorithm, and clustering is performed with the current user as the core to obtain the reference customer group of the current user.

[0066] Obtaining web page resources with the highest number of usage from a reference customer group of a first preset number of current users as a web page resource preloading set;

[0067] The lower the controllability of the current user, the larger the K value in the KNN algorithm of the current customer. Specifically, in the embodiment of the present application, the K value has multiple preset values ​​corresponding to different controllability values. When the controllability value is lower, the K value is larger.

[0068] Specifically, the preset controllability evaluation function is:

[0069]

[0070] Among them, S(x) is the controllability, k is the first empirical parameter, x0 is the second empirical parameter, and x is the usage rate of the web resource preloading set by the user, that is, when the user subsequently browses the web resources, the number of times the web resources in the web resource preloading set are used divided by the total number of web resources in the web resource preloading set.

[0071] S104: Send the webpage resource preload set of the current user to the device of the current user, so that when rendering the subsequent webpage resources browsed by the current user, the webpage resources of the current browsing page are first obtained from the local webpage resource preload set.

[0072] Specifically, before initiating a request for a webpage resource to an external server, first search for the webpage resource preload set in the local device to see if the webpage resource exists, and if not, then initiate a request for the webpage resource to the external server.

[0073] In summary, one of the beneficial effects of the page rendering method provided in the embodiment of the present application is that the embodiment of the present application can achieve more accurate and efficient web resource preloading by combining the recommendation algorithm based on association rules and the collaborative filtering algorithm based on users. This combination method fully considers the particularity of web resource preloading, which is different from the application of traditional recommendation systems in product recommendation.

[0074] In the field of product recommendations, users are usually faced with a range of choices that they can actively make. The recommendation system predicts which products are most likely to attract users by analyzing their purchase history and browsing habits. Such recommendations are often based on users' explicit preferences or implicit preferences derived from comparing the user's behavior with that of other similar users.

[0075] In contrast, web resource preloading must handle more dynamic and immediate needs. Users' browsing behavior is more random and greatly influenced by instant information, and users often have not explicitly expressed their preferences for certain types of resources. Therefore, although the single use of association rule-based recommendation algorithms can capture strong correlation patterns in user behavior, such as the web page paths that users frequently visit, this method tends to ignore those resources that are only weakly associated with the user's current browsing content but may attract the user's attention due to their presentation or content.

[0076] In addition, although directly applying user-based collaborative filtering algorithms can help discover users' potential interests and recommend resources by analyzing similar behaviors with other users, this method may not be immediately effective when the amount of data is insufficient or the user behavior is not typical enough, especially when the user browsing behavior changes rapidly.

[0077] Therefore, the present application first uses an algorithm based on association rules to quickly predict the web resources that need to be preloaded to assist the browsing behavior of fixed users, and then uses a user-based collaborative filtering algorithm to expand the web resources that need to be preloaded, thereby overcoming the limitations of a single algorithm and taking advantage of the complementary advantages of the two. This strategy improves the accuracy and timeliness of web resource preloading.

[0078] The second beneficial effect is that the present application realizes a dynamic and adaptive web resource preloading method by combining an association rule-based recommendation algorithm and a user-based collaborative filtering recommendation algorithm. In particular, in the process of using the collaborative filtering recommendation algorithm followed by the initial association rule recommendation, a user controllability evaluation is introduced, thereby optimizing the accuracy and efficiency of the preloading process.

[0079] In this method, first, association rules are used to quickly identify and preload resources that users may need immediately. This step is based on the user's historical behavior data and can quickly respond to the user's direct needs. Then, the system evaluates the controllability of the user's browsing behavior by analyzing the user's browsing behavior, that is, the predictability and consistency of the user's behavior. For example, when users browse casually, the behavior pattern is relatively scattered, and the preloaded set of web resources used is small, so the controllability is low at this time; while when users are deeply researching a product or content, they use a large preloaded set of web resources, showing a coherent and purposeful browsing behavior, and the controllability is high at this time.

[0080] This controllability assessment enables the system to adjust the preloading strategy according to the different stages of user behavior. When user behavior is controllable, the system can use a smaller reference group for collaborative filtering, which not only reduces the consumption of computing power, but also improves the relevance and accuracy of recommendations, because the user's needs are clearer and closer to the user behavior within a specific group. Conversely, when user behavior is uncontrollable, the system will expand the scope of the reference group, which helps capture a wider range of potential points of interest.

[0081] Furthermore, when user behavior is controllable, that is, when the behavior pattern is clear and consistent, the system tends to use a smaller user group for precise resource preloading. Since the size of the web resource preloading set is fixed, the smaller the user group, the easier it is for the specific loaded content to enter the web resource preloading set. The specific loaded content usually includes specific resources for specific content needs, such as large images or videos. This precise preloading helps ensure that resource-intensive pages can be loaded quickly and efficiently, improving the access speed and browsing quality of the target content.

[0082] On the contrary, when user behavior is uncontrollable, that is, browsing randomly and lacking clear goals, the system expands the scope of the reference group. Since the size of the web resource preloading set is fixed, when the reference group is larger, there will be more types and numbers of general loading resources. General loading resources will occupy most of the web resource preloading set, so that general resources such as web UI elements and basic scripts can be preloaded. Although these resources are small, because they are widely shared and reused, their preloading can also improve the rendering speed of multiple pages. This strategy effectively reduces the unnecessary preloading of large resources, avoids unnecessary occupation of bandwidth and user storage, and ensures efficient operation of the system and a good user experience.

[0083] Similarly, when subsequent users use the web resource preloading collection, the user's controllability will change. Once the user's browsing behavior becomes targeted, the current user's reference group will be narrowed, thereby switching to a more precise preloading mode, reducing the size of the reference group, and focusing on loading resources directly related to the user's current needs, such as specific pictures, videos or detailed content pages.

[0084] In a second aspect, the present application provides a page rendering device, the page rendering device comprising:

[0085] A data acquisition module is used to acquire web page resources used by the current user when browsing a page within the first sliding time window of the current user, and to acquire web page resources used by each user when browsing a page;

[0086] A first generating module, before completing the collection of web page resources of the current user browsing page within the first sliding time window, uses a recommendation algorithm based on association rules to generate a web page resource preload set for the current user according to the web page resources used by the user's current browsing page;

[0087] The second generation module, after completing the collection of web page resources of the current user browsing the page within the first sliding time window, uses a user-based collaborative filtering recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user and other users when browsing the page within the first sliding time window;

[0088] A data receiving module, used for receiving and saving the webpage resource preloading set sent by the first generating module or the second generating module;

[0089] The rendering module is used to obtain the web page resources of the current browsing page from the local web page resource preload collection when rendering the subsequent browsing web page resources of the current user.

[0090] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a page rendering method as described in any one of the first aspects.

[0091] Among them, the technical solutions described in the second and third aspects have the same working principles and technical effects as the page rendering method of the first aspect in the embodiment of the present application, and will not be repeated here.

[0092] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In addition, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. Moreover, in the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can represent A or B; "and / or" in this article is only a kind of association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. Furthermore, in the description of the embodiments of the present application, “plurality” refers to two or more than two.

[0093] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A page rendering method, characterized in that: The page rendering method comprises the following steps: Obtain the web page resources used by the current user when browsing the page within the first sliding time window of the current user; Before completing the collection of web page resources of the current user's browsing page within the first sliding time window, using an association rule-based recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the user's current browsing page; Respectively obtain web page resources used by each user when browsing a page within the first sliding time window; After completing the collection of web page resources of the current user browsing pages within the first sliding time window, using a user-based collaborative filtering recommendation algorithm to generate a preloaded set of web page resources for the current user according to the web page resources used by the current user within the first sliding time window and other users within the first sliding time window when browsing pages; Sending a preloaded set of web resources of the current user to the device of the current user; When rendering the subsequent web page resources browsed by the current user, the web page resources of the current browsing page are first obtained from the local web page resource preload collection; The page rendering method further includes the following steps: The controllability of the current user is calculated according to the usage rate of the web resource preloading set used by the user through a preset controllability evaluation function; The size of the user group referenced by the user-based collaborative filtering recommendation algorithm is adjusted according to the controllability of the current user, so that when the controllability of the current user is lower, the user group referenced by the current user is larger, and when the controllability of the current user is higher, the user group referenced by the current user is smaller, so as to achieve a balance between increasing the possibility of loading resources directly related to the current needs of the user and increasing the utilization rate of the preloaded set of web page resources, including the following steps: The coverage ratio between the web page resources used by the current user in the first sliding time window and the web page resources used by other users when browsing pages in the preset time period is used as a measurement standard. The web page resources in the first sliding time window of the current user and the web page resources in the first sliding time window of other users are calculated by the KNN algorithm, and clustering is performed with the current user as the core to obtain the reference customer group of the current user. Obtaining web page resources with the highest number of usage from a reference customer group of a first preset number of current users as a web page resource preloading set; The lower the controllability of the current user, the larger the K value in the KNN algorithm of the current customer.

2. The page rendering method according to claim 1, characterized in that: Before completing the collection of web page resources of the current user's browsing page within the first sliding time window, a recommendation algorithm based on association rules is used to generate a preloaded set of web page resources for the current user according to the web page resources used by the user's current browsing page, including the following steps: Mark each web resource with its unique resource identifier; Collecting the access data of web resources of all users to obtain the transition probability between each pair of web resources, wherein the transition probability is the probability that a user visits another web resource after visiting a certain web resource; According to the Markov model, the transition probability between each pair of web resources and the sequence of web resources used by the current user when browsing the page, the predicted probability of the current user accessing other web resources is predicted; The web page resources whose predicted probabilities are above the predicted probability threshold are used as a web page resource preloading set.

3. The page rendering method according to claim 1, characterized in that: The preset controllability evaluation function is: ， Wherein, S(x) is the controllability, k is the first empirical parameter, x0 is the second empirical parameter, and x is the usage rate of the user using the webpage resource preloading set.

4. An electronic device, characterized in that It includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the page rendering method as described in any one of claims 1 to 3.

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