CN111538930A - Home page display method and related equipment - Google Patents
Home page display method and related equipment Download PDFInfo
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- CN111538930A CN111538930A CN202010225139.1A CN202010225139A CN111538930A CN 111538930 A CN111538930 A CN 111538930A CN 202010225139 A CN202010225139 A CN 202010225139A CN 111538930 A CN111538930 A CN 111538930A
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
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Abstract
The application discloses a home page display method and related equipment, wherein the method comprises the following steps: when a request of a target user for opening a target website is detected, first user characteristics of a first preset number of the target user are obtained, and content characteristics of a second preset number are obtained from content to be recommended; determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics; determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components; and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page. The content and page components of the home page can be all thousands of people.
Description
Technical Field
The present application relates to the field of interface display technologies, and in particular, to a home page display method and related devices.
Background
With the advent of the big data era, the difficulty of obtaining effective information from massive information increases, and the technology of thousands of people can realize personalized information recommendation, thereby reducing the difficulty of obtaining effective information by users to a certain extent.
However, thousands of people and thousands of faces are recommended in content of the current webpage, that is, components displayed on the webpage are all uniform components, and recommendation is based on user individuality on content display of the components. Therefore, when different users enter the web page, the overall frame presented by the web page is the same.
Disclosure of Invention
The embodiment of the application provides a home page display method and related equipment, and the richness of home page display is improved by recommending content and components presented by a home page in a personalized manner.
In a first aspect, an embodiment of the present application provides a home page display method, which is applied to an electronic device, and the method includes:
when a request of a target user for opening a target website is detected, first user characteristics of a first preset number of the target user are obtained, and content characteristics of a second preset number are obtained from content to be recommended;
determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics;
determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components;
and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page.
In a second aspect, an embodiment of the present application provides a home page display apparatus, which is applied to an electronic device, and the apparatus includes:
the system comprises an acquisition unit, a recommendation unit and a recommendation unit, wherein the acquisition unit is used for acquiring a first preset number of user characteristics of a target user and acquiring a second preset number of content characteristics from contents to be recommended when a request of the target user for opening a target website is detected;
the determining unit is used for determining a third preset number of page component types according to the first preset number of user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of user characteristics and the second preset number of content characteristics;
the generating unit is used for determining a third preset number of page components according to the third preset number of page component types and generating a target home page according to the third preset number of page components;
and the display unit is used for displaying the target home page on a page of the target website and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page.
In a third aspect, embodiments of the present application provide an electronic device, which includes a processor, a memory, a communication interface, and one or more programs, stored in the memory and configured to be executed by the processor, the programs including instructions for performing some or all of the steps described in the method according to the first aspect of the embodiments of the present application.
In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, where the computer program is executed by a processor to implement part or all of the steps described in the method according to the first aspect of the present application.
In a fifth aspect, the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps described in the method according to the first aspect of the present application. The computer program product may be a software installation package.
It can be seen that, in the embodiment of the application, when the electronic device detects a request of a target user for opening a target website, a first preset number of first user features of the target user are obtained, and a second preset number of content features are obtained from content to be recommended; determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics; determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components; and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page. Therefore, according to the technical scheme provided by the embodiment of the application, the content and the components presented by the home page are recommended in a personalized manner, and the richness of home page display is improved.
These and other aspects of the present application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of hardware of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a home page display method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another home page display method provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a home page display device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The electronic device according to the embodiments of the present application may be an electronic device with communication capability, and the electronic device may include various handheld devices with wireless communication function, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and various forms of User Equipment (UE), Mobile Stations (MS), terminal devices (terminal device), and so on.
Referring to fig. 1, fig. 1 is a schematic structural diagram of hardware of an electronic device 100 according to an exemplary embodiment of the present application. The electronic device 100 may be a smart phone, a tablet computer, an electronic book, or other electronic devices capable of running an application. The electronic device 100 in the present application may include one or more of the following components: processor, memory, transceiver, etc.
A processor may include one or more processing cores. The processor, using various interfaces and lines to connect various parts throughout the electronic device 100, performs various functions of the electronic device 100 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in memory, and calling data stored in memory. Alternatively, the processor may be implemented in hardware using at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is to be understood that the modem may be implemented by a communication chip without being integrated into the processor.
The Memory may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory includes a non-transitory computer-readable medium (non-transitory-readable storage medium). The memory may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (machine learning algorithms, TF-IDF algorithms, factoring machine algorithms, feature extraction, model training, etc.), instructions for implementing various method embodiments described below, and the like, the operating system may be an Android (Android) system (including systems based on Android system depth development), an apple developed IOS system (including systems based on IOS system depth development), or other systems. The storage data area may also store data created during use by the electronic device 100 (e.g., historical user data, historical home pages, historical recommendation components, historical recommendation content, etc.).
Referring to fig. 2, fig. 2 is a flowchart illustrating a home page display method according to an embodiment of the present disclosure, where the home page display method can be applied to the electronic device shown in fig. 1.
As shown in fig. 2, the main body of execution of the home page display method is an electronic device, and the method includes the following operation steps.
S201, when a request of a target user for opening a target website is detected, first user characteristics of a first preset number of the target user are obtained, and content characteristics of a second preset number are obtained from content to be recommended.
Wherein the first user characteristics may include, but are not limited to, age, income, gender, nationality, education level, place of residence, online duration, search keywords, and the like.
The first user characteristics of the corresponding first preset number of the target users can be generated according to the content input by the target users on the target website.
Generating a first user characteristic corresponding to a first preset number of the target users according to the content input by the target users on the target website comprises: extracting a certain number of keywords from the input content; matching the certain number of keywords with historical search keywords; and proposing a first preset number of user characteristics from a database of the history users corresponding to the history search keywords which are successfully matched as the first preset number of first user characteristics of the target user.
The content to be recommended may be various types of information that needs to be displayed on the target webpage.
The content features may include, but are not limited to, pictures, comics, merchandise, advertisements, songs, videos, news information, and the like.
S202, determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics.
Wherein the component types may include, but are not limited to, insurance topics, rental topics, dining topics, and nursery topics, among others.
S203, determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components.
The method includes the steps that after the electronic device acquires a certain number of components according to first user characteristics of a target user, a home page of a website recommended to the target user in a personalized manner can be generated according to the components.
S204, displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page.
For example, recommendations for a restaurant may be displayed entirely within the restaurant theme component on the top page, recommendations for sports may be displayed entirely within the sports theme component on the top page, recommendations for a rental may be displayed entirely within the rental theme component on the top page, and so on.
It can be understood that, when the target user enters the target website for the first time, the component of the first display home page of the electronic device to the user is set by default according to the data of the historical user in advance, and the content displayed in the component is also set by default according to the preference of the historical user.
It can be seen that in the home page display method of the embodiment of the application, when the electronic device detects a request of a target user to open a target website, a first user characteristic of a first preset number of the target user is obtained, and a second preset number of content characteristics are obtained from content to be recommended; determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics; determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components; and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page. Therefore, according to the method for displaying the home page, the content and the components displayed on the home page are recommended in a personalized mode, and the richness of home page display is improved.
In one possible example, the obtaining a first preset number of user characteristics of the target user includes: extracting user data of the target user in a first preset time period from a user database; and performing feature extraction on the user data to obtain a first preset number of user features of the target user.
As can be seen, in this example, the user characteristics of the target user are extracted from the user data of the target user, which is beneficial for the acquired user characteristics to be specific to the target user.
In one possible example, the correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target top page includes: classifying the recommended contents of the fourth preset number according to the page component types of the third preset number; and correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target home page according to the classification.
Wherein the correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target home page according to the classification includes: extracting at least one component feature from each page component of the third preset number of page components to obtain a plurality of component features; extracting at least one content feature from each content of the fourth preset number of recommended contents to obtain a plurality of recommended content features; clustering and classifying the component characteristics and the recommended content characteristics to obtain a plurality of clusters; and displaying each recommended content in the fourth preset number of recommended contents on one of the page components corresponding to the component characteristics of the recommended contents in the same cluster.
Therefore, in this example, the recommended contents are classified according to the types of the page components, the recommended contents are displayed on the page components classified correspondingly, and the recommended contents of the same type are displayed on the same component, which is beneficial for a user to quickly obtain effective information.
In one possible example, the method further comprises: acquiring user data of A historical users in a second preset time period, wherein A is a positive integer; performing feature extraction on user data of each historical user to obtain B second user features corresponding to each historical user, wherein B is a positive integer; obtaining C page component types corresponding to each historical user according to the user data of each historical user, and determining C first type features according to the C page component types corresponding to each historical user, wherein the C page component types are in one-to-one correspondence with the C first type features, and C is a positive integer; performing vector representation on second user features corresponding to each historical user and first type features corresponding to each historical user by using a TF-IDF algorithm to obtain A first user feature vector sets and A first type feature vector sets, wherein the A historical users correspond to the A first user feature vector sets one by one, and the A historical users correspond to the A first type feature vector sets one by one; respectively solving intersection of second user feature vectors in a first user feature vector set corresponding to each historical user to obtain A second user feature vector sets, wherein the A historical users correspond to the A second user feature vector sets one by one; respectively solving intersection of the first type feature vectors in the first type feature vector set corresponding to each historical user to obtain A second type feature vector sets, wherein the A historical users correspond to the A second type feature vector sets one by one; calculating the TF value of each third user feature vector in the second user feature vector set corresponding to each historical user in the corresponding first user feature vector set, and calculating the TF value of the second type feature vector in the corresponding second type feature vector set corresponding to each historical user in the corresponding first type feature vector set; calculating the IDF value of each third user feature vector in the second user feature vector set corresponding to each historical user, and calculating the IDF value of the second type feature vector in the second type feature vector set corresponding to each historical user; calculating a corresponding first TF-IDF value according to the TF value and the IDF value of each third user feature vector of each historical user, and calculating a corresponding second TF-IDF value according to the TF value and the IDF value of each second type feature vector of each historical user; sequencing all the first TF-IDF values in a descending order, and sequencing all the second TF-IDF values in a descending order; extracting the third user characteristic vector and the second type characteristic vector which are sequenced to be larger than the first preset sequence, and calculating cosine similarity between every two characteristic vectors to obtain a plurality of cosine similarities; judging whether each cosine similarity is larger than a first preset threshold value or not; and if the cosine similarity is larger than a first preset threshold, inputting the corresponding page component types corresponding to the third user characteristic vector, the second type characteristic vector and the second type characteristic vector into a preset machine learning algorithm for training one by one to obtain a first recommendation model.
Among them, TF-IDF (term frequency-inverse document frequency) is a common weighting technique for data retrieval and data mining, which is a statistical method that can be used to evaluate the importance degree of a feature, and the importance of a feature increases in proportion to the number of times it appears in a feature set. TF means Term Frequency (Term Frequency), and IDF means Inverse text Frequency index (Inverse Document Frequency). Wherein, the calculation formulas of TF, IDF and TF-IDF are as follows:
TF-IDF＝TF×IDF (3)
the cosine similarity is also called cosine similarity, and the similarity of two eigenvectors is evaluated by calculating the cosine value of the included angle of the two eigenvectors.
The preset machine learning algorithm may include, but is not limited to, a supervised learning algorithm, a bayesian algorithm, and the like.
Wherein the method further comprises: and acquiring the user characteristics of the user and the component characteristics corresponding to the user within preset time, and updating the first recommendation model.
In one possible example, the determining a third preset number of page component types according to the first preset number of first user characteristics includes: and inputting the first user characteristics of the first preset number into the first recommendation model to obtain page component types of a third preset number.
It should be noted that the page component types of the third preset number output from the first recommendation model are sorted from large to small according to their corresponding TF-IDF values.
Inputting the first user characteristics of the first preset number into the first recommendation model to obtain page component types of a third preset number, wherein the page component types of the third preset number comprise: performing vector representation on the first user characteristics of the first preset number by using a TF-IDF algorithm to obtain a target user characteristic vector set; calculating the target cosine similarity of each target user feature vector in the target user feature vector set and each second type feature vector in the second type feature vector set; and when the target cosine similarity is greater than a first preset threshold, taking the page component type corresponding to the corresponding second type feature vector as the page component types of the third preset number.
It can be seen that, in this example, vector representation is performed on the historical user characteristics and the page component type characteristics of the home page corresponding to the historical user by using a TF-IDF algorithm, and then a TF-IDF value is calculated to obtain the common characteristics of the historical user and the page component types universally favored by the historical user, then a machine learning algorithm is used to train the general characteristics and the page component types universally favored by the historical user to obtain a first recommendation model for recommending the page component types to a target user, the first user characteristics of the target user are input into the first recommendation model to obtain the page component types favored by the target user, and then the home page is generated according to the page component types favored by the target user, so that personalized recommendation of the page components in the home page is facilitated, and thousands of faces of the page component are realized.
In one possible example, the method further comprises: d historical recommendation contents are obtained from a historical home page corresponding to each historical user, and a plurality of historical recommendation contents are obtained; extracting the characteristics of the plurality of historical recommended contents to obtain a plurality of historical recommended content characteristics; and inputting the second user characteristics of all the historical users and the plurality of historical recommended content characteristics into a factorization machine algorithm for training to obtain a second recommendation model.
The Factorization Machine algorithm (FM) is a Machine learning algorithm based on matrix decomposition proposed by Steffen Rendle, and takes relevance among features into consideration, so that the problem of feature combination under the condition of sparse data can be solved.
In one possible example, the determining a fourth preset number of recommended contents according to the first preset number of first user features and the second preset number of content features includes: inputting the first user characteristics of the first preset quantity and the content characteristics of the second preset quantity into the second recommendation model to obtain a decomposition matrix; calculating to obtain a cross term coefficient of a combined feature formed by pairwise combination of the first preset number of first user features and the second preset number of content features according to the decomposition matrix, and calculating to obtain the importance of the combined feature according to the cross term coefficient; constructing a combined feature importance matrix according to the importance of the combined features; removing combined features and repeated combined features obtained by combining features in the same feature domain from the combined feature importance matrix; sorting the combined feature importance matrixes after removing the combined features in the same feature domain and removing the repeated combined features to obtain the importance sorting of the combined features; and taking the contents to be recommended corresponding to the combined features with the sequence larger than the second threshold value as the recommended contents of the fourth preset number.
In this example, the characteristic combination is performed on the historical user characteristics and the historical recommended content characteristics by using a factorization algorithm, the relevance between the user characteristics and the recommended content characteristics is considered for the obtained combined characteristics, a second recommendation model for the personalized recommended content is obtained after training, the recommended content liked by the target user can be obtained by inputting the first user characteristics of the target user and the content characteristics of the content to be recommended into the second recommendation model, the personalized recommendation of the recommended content in the top page can be favorably realized, and thousands of people and thousands of faces of the content can be realized.
Referring to fig. 3, fig. 3 is a flowchart illustrating a home page display method according to an embodiment of the present disclosure, where the home page display method can be applied to the electronic device shown in fig. 1.
As shown in fig. 3, an execution subject of the home page display method is an electronic device, and the home page display method includes the following operations.
S301, when a request of a target user for opening a target website is detected, extracting user data of the target user in a first preset time period from a user database.
S302, extracting the features of the user data to obtain a first preset number of user features of the target user.
S303, obtaining a second preset number of content features from the content to be recommended.
S304, determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics.
S305, determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components.
S306, displaying the target home page on the page of the target website.
S307, classifying the recommended contents in the fourth preset number according to the page component types in the third preset number.
And S308, correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target home page according to the classification.
It can be seen that, in the home page display method of the embodiment of the application, when the electronic device detects a request of a target user to open a target website, user characteristics of the target user are obtained from user data of the target user in a user database, and content characteristics of content to be recommended are obtained; secondly, recommending components of the home page to the target user according to the user characteristic personality of the target user, and recommending content to the target user according to the user characteristic of the target user and the content characteristic of the content to be recommended; and generating a home page display according to the recommended home page components, and displaying the recommended content on each component of the home page according to the component type in a classified manner. Therefore, through the home page display method provided by the embodiment of the application, the content and the components displayed on the home page can be recommended individually, the content and the components of the home page are more than one person and more than one person, and the richness of home page display is improved.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device 400 according to an embodiment of the present disclosure, which is similar to the embodiments shown in fig. 2 and fig. 3. As shown in fig. 4, the electronic device 400 includes an application processor 410, a memory 420, a communication interface 430, and one or more programs 421, where the one or more programs 421 are stored in the memory 420 and configured to be executed by the application processor 410, and the one or more programs 421 include instructions for performing any of the steps of the above method embodiments.
In one possible example, the program 421 includes instructions for performing the following steps: when a request of a target user for opening a target website is detected, first user characteristics of a first preset number of the target user are obtained, and content characteristics of a second preset number are obtained from content to be recommended; determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics; determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components; and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page.
It can be seen that, in the electronic device according to the embodiment of the present application, when a request for opening a target website by a target user is detected, a first user characteristic of a first preset number of the target user is obtained, and a second preset number of content characteristics are obtained from content to be recommended; determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics; determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components; and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page. Therefore, through the electronic equipment provided by the embodiment of the application, the content and the components presented by the home page can be recommended in a personalized manner, and the richness of home page display is improved.
In one possible example, in terms of obtaining a first preset number of user characteristics of the target user, the instructions in the program 421 are specifically configured to: extracting user data of the target user in a first preset time period from a user database; and performing feature extraction on the user data to obtain a first preset number of user features of the target user.
In one possible example, in terms of correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target top page, the instructions in the program 421 are specifically configured to perform the following operations: classifying the recommended contents of the fourth preset number according to the page component types of the third preset number; and correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target home page according to the classification.
In one possible example, the instructions in the program 421 are specifically further configured to: acquiring user data of A historical users in a second preset time period, wherein A is a positive integer; performing feature extraction on user data of each historical user to obtain B second user features corresponding to each historical user, wherein B is a positive integer; obtaining C page component types corresponding to each historical user according to the user data of each historical user, and determining C first type features according to the C page component types corresponding to each historical user, wherein the C page component types are in one-to-one correspondence with the C first type features, and C is a positive integer; performing vector representation on second user features corresponding to each historical user and first type features corresponding to each historical user by using a TF-IDF algorithm to obtain A first user feature vector sets and A first type feature vector sets, wherein the A historical users correspond to the A first user feature vector sets one by one, and the A historical users correspond to the A first type feature vector sets one by one; respectively solving intersection of second user feature vectors in a first user feature vector set corresponding to each historical user to obtain A second user feature vector sets, wherein the A historical users correspond to the A second user feature vector sets one by one; respectively solving intersection of the first type feature vectors in the first type feature vector set corresponding to each historical user to obtain A second type feature vector sets, wherein the A historical users correspond to the A second type feature vector sets one by one; calculating the TF value of each third user feature vector in the second user feature vector set corresponding to each historical user in the corresponding first user feature vector set, and calculating the TF value of the second type feature vector in the corresponding second type feature vector set corresponding to each historical user in the corresponding first type feature vector set; calculating the IDF value of each third user feature vector in the second user feature vector set corresponding to each historical user, and calculating the IDF value of the second type feature vector in the second type feature vector set corresponding to each historical user; calculating a corresponding first TF-IDF value according to the TF value and the IDF value of each third user feature vector of each historical user, and calculating a corresponding second TF-IDF value according to the TF value and the IDF value of each second type feature vector of each historical user; sequencing all the first TF-IDF values in a descending order, and sequencing all the second TF-IDF values in a descending order; extracting the third user characteristic vector and the second type characteristic vector which are sequenced to be larger than the first preset sequence, and calculating cosine similarity between every two characteristic vectors to obtain a plurality of cosine similarities; judging whether each cosine similarity is larger than a first preset threshold value or not; and if the cosine similarity is larger than a first preset threshold, inputting the corresponding page component types corresponding to the third user characteristic vector, the second type characteristic vector and the second type characteristic vector into a preset machine learning algorithm for training one by one to obtain a first recommendation model.
In one possible example, in terms of determining a third preset number of page component types according to the first preset number of first user characteristics, the instructions in the program 421 are specifically configured to: and inputting the first user characteristics of the first preset number into the first recommendation model to obtain page component types of a third preset number.
In one possible example, the instructions in the program 421 are specifically further configured to: d historical recommendation contents are obtained from a historical home page corresponding to each historical user, and a plurality of historical recommendation contents are obtained; extracting the characteristics of the plurality of historical recommended contents to obtain a plurality of historical recommended content characteristics; and inputting the second user characteristics of all the historical users and the plurality of historical recommended content characteristics into a factorization machine algorithm for training to obtain a second recommendation model.
In one possible example, in determining a fourth preset number of recommended content according to the first preset number of first user characteristics and the second preset number of content characteristics, the instructions in the program 421 are specifically configured to: inputting the first user characteristics of the first preset quantity and the content characteristics of the second preset quantity into the second recommendation model to obtain a decomposition matrix; calculating to obtain a cross term coefficient of a combined feature formed by pairwise combination of the first preset number of first user features and the second preset number of content features according to the decomposition matrix, and calculating to obtain the importance of the combined feature according to the cross term coefficient; constructing a combined feature importance matrix according to the importance of the combined features; removing combined features and repeated combined features obtained by combining features in the same feature domain from the combined feature importance matrix; sorting the combined feature importance matrixes after removing the combined features in the same feature domain and removing the repeated combined features to obtain the importance sorting of the combined features; and taking the contents to be recommended corresponding to the combined features with the sequence larger than the second threshold value as the recommended contents of the fourth preset number.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It will be appreciated that the electronic device, in order to carry out the above-described functions, comprises corresponding hardware structure and/or software page components for performing the respective functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the electronic device may be divided into the functional units according to the method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Referring to fig. 5, fig. 5 is a block diagram illustrating functional units of a home page display apparatus 500 according to an embodiment of the present application. The home page display apparatus 500 is applied to an electronic device, and the home page display apparatus 500 includes:
the acquiring unit 501 is configured to, when a request for opening a target website by a target user is detected, acquire a first preset number of user features of the target user, and acquire a second preset number of content features from content to be recommended;
a determining unit 502, configured to determine a third preset number of page component types according to the first preset number of user features, and determine a fourth preset number of recommended contents according to the first preset number of user features and the second preset number of content features;
a generating unit 503, configured to determine a third preset number of page components according to the third preset number of page component types, and generate a target home page according to the third preset number of page components;
a display unit 504, configured to display the target home page on a page of the target website, and correspondingly display the fourth preset number of recommended contents on a corresponding page component of the target home page.
It can be seen that, in the home page display apparatus according to the embodiment of the present application, when a request for opening a target website by a target user is detected, a first user characteristic of a first preset number of the target user is obtained, and a second preset number of content characteristics are obtained from content to be recommended; determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics; determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components; and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page. Therefore, through the home page display device provided by the embodiment of the application, the content and the components presented by the home page can be recommended in a personalized manner, and the richness of home page display is improved.
In one possible example, in terms of obtaining a first preset number of user characteristics of a target user, the obtaining unit 501 is specifically configured to: extracting user data of the target user in a first preset time period from a user database; and performing feature extraction on the user data to obtain a first preset number of user features of the target user.
In a possible example, in terms of correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target top page, the display unit 504 is specifically configured to: classifying the recommended contents of the fourth preset number according to the page component types of the third preset number; and correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target home page according to the classification.
In one possible example, the home page display apparatus 500 further comprises a first training unit for: acquiring user data of A historical users in a second preset time period, wherein A is a positive integer; performing feature extraction on user data of each historical user to obtain B second user features corresponding to each historical user, wherein B is a positive integer; obtaining C page component types corresponding to each historical user according to the user data of each historical user, and determining C first type features according to the C page component types corresponding to each historical user, wherein the C page component types are in one-to-one correspondence with the C first type features, and C is a positive integer; performing vector representation on second user features corresponding to each historical user and first type features corresponding to each historical user by using a TF-IDF algorithm to obtain A first user feature vector sets and A first type feature vector sets, wherein the A historical users correspond to the A first user feature vector sets one by one, and the A historical users correspond to the A first type feature vector sets one by one; respectively solving intersection of second user feature vectors in a first user feature vector set corresponding to each historical user to obtain A second user feature vector sets, wherein the A historical users correspond to the A second user feature vector sets one by one; respectively solving intersection of the first type feature vectors in the first type feature vector set corresponding to each historical user to obtain A second type feature vector sets, wherein the A historical users correspond to the A second type feature vector sets one by one; calculating the TF value of each third user feature vector in the second user feature vector set corresponding to each historical user in the corresponding first user feature vector set, and calculating the TF value of the second type feature vector in the corresponding second type feature vector set corresponding to each historical user in the corresponding first type feature vector set; calculating the IDF value of each third user feature vector in the second user feature vector set corresponding to each historical user, and calculating the IDF value of the second type feature vector in the second type feature vector set corresponding to each historical user; calculating a corresponding first TF-IDF value according to the TF value and the IDF value of each third user feature vector of each historical user, and calculating a corresponding second TF-IDF value according to the TF value and the IDF value of each second type feature vector of each historical user; sequencing all the first TF-IDF values in a descending order, and sequencing all the second TF-IDF values in a descending order; extracting the third user characteristic vector and the second type characteristic vector which are sequenced to be larger than the first preset sequence, and calculating cosine similarity between every two characteristic vectors to obtain a plurality of cosine similarities; judging whether each cosine similarity is larger than a first preset threshold value or not; and if the cosine similarity is larger than a first preset threshold, inputting the corresponding page component types corresponding to the third user characteristic vector, the second type characteristic vector and the second type characteristic vector into a preset machine learning algorithm for training one by one to obtain a first recommendation model.
In a possible example, in terms of determining a third preset number of page component types according to the first preset number of first user features, the determining unit 502 is specifically configured to: and inputting the first user characteristics of the first preset number into the first recommendation model to obtain page component types of a third preset number.
In one possible example, the home page display apparatus 500 further comprises a second training unit for: d historical recommendation contents are obtained from a historical home page corresponding to each historical user, and a plurality of historical recommendation contents are obtained; extracting the characteristics of the plurality of historical recommended contents to obtain a plurality of historical recommended content characteristics; and inputting the second user characteristics of all the historical users and the plurality of historical recommended content characteristics into a factorization machine algorithm for training to obtain a second recommendation model.
In one possible example, in terms of determining a fourth preset number of recommended contents according to the first preset number of first user features and the second preset number of content features, the determining unit 502 is specifically configured to: inputting the first user characteristics of the first preset quantity and the content characteristics of the second preset quantity into the second recommendation model to obtain a decomposition matrix; calculating to obtain a cross term coefficient of a combined feature formed by pairwise combination of the first preset number of first user features and the second preset number of content features according to the decomposition matrix, and calculating to obtain the importance of the combined feature according to the cross term coefficient; constructing a combined feature importance matrix according to the importance of the combined features; removing combined features and repeated combined features obtained by combining features in the same feature domain from the combined feature importance matrix; sorting the combined feature importance matrixes after removing the combined features in the same feature domain and removing the repeated combined features to obtain the importance sorting of the combined features; and taking the contents to be recommended corresponding to the combined features with the sequence larger than the second threshold value as the recommended contents of the fourth preset number.
It can be understood that, since the method embodiment and the apparatus embodiment are different presentation forms of the same technical concept, the content of the method embodiment portion in the present application should be synchronously adapted to the apparatus embodiment portion, and is not described herein again.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, the computer comprising an electronic device.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and page components referred to are not necessarily required by the application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A home page display method is applied to electronic equipment, and comprises the following steps:
when a request of a target user for opening a target website is detected, first user characteristics of a first preset number of the target user are obtained, and content characteristics of a second preset number are obtained from content to be recommended;
determining a third preset number of page component types according to the first preset number of first user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics;
determining a third preset number of page components according to the third preset number of page component types, and generating a target home page according to the third preset number of page components;
and displaying the target home page on a page of the target website, and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page.
2. The method of claim 1, wherein the obtaining a first preset number of user characteristics of a target user comprises:
extracting user data of the target user in a first preset time period from a user database;
and performing feature extraction on the user data to obtain a first preset number of user features of the target user.
3. The method according to claim 1, wherein said displaying the fourth preset number of recommended contents on the corresponding page component of the target top page correspondingly comprises:
classifying the recommended contents of the fourth preset number according to the page component types of the third preset number;
and correspondingly displaying the fourth preset number of recommended contents on the corresponding page component of the target home page according to the classification.
4. The method according to any one of claims 1-3, further comprising:
acquiring user data of A historical users in a second preset time period, wherein A is a positive integer;
performing feature extraction on user data of each historical user to obtain B second user features corresponding to each historical user, wherein B is a positive integer;
obtaining C page component types corresponding to each historical user according to the user data of each historical user, and determining C first type features according to the C page component types corresponding to each historical user, wherein the C page component types are in one-to-one correspondence with the C first type features, and C is a positive integer;
performing vector representation on second user features corresponding to each historical user and first type features corresponding to each historical user by using a TF-IDF algorithm to obtain A first user feature vector sets and A first type feature vector sets, wherein the A historical users correspond to the A first user feature vector sets one by one, and the A historical users correspond to the A first type feature vector sets one by one;
respectively solving intersection of second user feature vectors in a first user feature vector set corresponding to each historical user to obtain A second user feature vector sets, wherein the A historical users correspond to the A second user feature vector sets one by one;
respectively solving intersection of the first type feature vectors in the first type feature vector set corresponding to each historical user to obtain A second type feature vector sets, wherein the A historical users correspond to the A second type feature vector sets one by one;
calculating the TF value of each third user feature vector in the second user feature vector set corresponding to each historical user in the corresponding first user feature vector set, and calculating the TF value of the second type feature vector in the corresponding second type feature vector set corresponding to each historical user in the corresponding first type feature vector set;
calculating the IDF value of each third user feature vector in the second user feature vector set corresponding to each historical user, and calculating the IDF value of the second type feature vector in the second type feature vector set corresponding to each historical user;
calculating a corresponding first TF-IDF value according to the TF value and the IDF value of each third user feature vector of each historical user, and calculating a corresponding second TF-IDF value according to the TF value and the IDF value of each second type feature vector of each historical user;
sequencing all the first TF-IDF values in a descending order, and sequencing all the second TF-IDF values in a descending order;
extracting the third user characteristic vector and the second type characteristic vector which are sequenced to be larger than the first preset sequence, and calculating cosine similarity between every two characteristic vectors to obtain a plurality of cosine similarities;
judging whether each cosine similarity is larger than a first preset threshold value or not;
and if the cosine similarity is larger than a first preset threshold, inputting the corresponding page component types corresponding to the third user characteristic vector, the second type characteristic vector and the second type characteristic vector into a preset machine learning algorithm for training one by one to obtain a first recommendation model.
5. The method of claim 4, wherein determining a third preset number of page component types according to the first preset number of first user characteristics comprises:
and inputting the first user characteristics of the first preset number into the first recommendation model to obtain page component types of a third preset number.
6. The method of claim 4, further comprising:
d historical recommendation contents are obtained from a historical home page corresponding to each historical user, and a plurality of historical recommendation contents are obtained;
extracting the characteristics of the plurality of historical recommended contents to obtain a plurality of historical recommended content characteristics;
and inputting the second user characteristics of all the historical users and the plurality of historical recommended content characteristics into a factorization machine algorithm for training to obtain a second recommendation model.
7. The method of claim 6, wherein determining a fourth preset number of recommended contents according to the first preset number of first user characteristics and the second preset number of content characteristics comprises:
inputting the first user characteristics of the first preset quantity and the content characteristics of the second preset quantity into the second recommendation model to obtain a decomposition matrix;
calculating to obtain a cross term coefficient of a combined feature formed by pairwise combination of the first preset number of first user features and the second preset number of content features according to the decomposition matrix, and calculating to obtain the importance of the combined feature according to the cross term coefficient;
constructing a combined feature importance matrix according to the importance of the combined features;
removing combined features and repeated combined features obtained by combining features in the same feature domain from the combined feature importance matrix;
sorting the combined feature importance matrixes after removing the combined features in the same feature domain and removing the repeated combined features to obtain the importance sorting of the combined features;
and taking the contents to be recommended corresponding to the combined features with the sequence larger than the second threshold value as the recommended contents of the fourth preset number.
8. A home page display device, applied to an electronic apparatus, the device comprising:
the system comprises an acquisition unit, a recommendation unit and a recommendation unit, wherein the acquisition unit is used for acquiring a first preset number of user characteristics of a target user and acquiring a second preset number of content characteristics from contents to be recommended when a request of the target user for opening a target website is detected;
the determining unit is used for determining a third preset number of page component types according to the first preset number of user characteristics, and determining a fourth preset number of recommended contents according to the first preset number of user characteristics and the second preset number of content characteristics;
the generating unit is used for determining a third preset number of page components according to the third preset number of page component types and generating a target home page according to the third preset number of page components;
and the display unit is used for displaying the target home page on a page of the target website and correspondingly displaying the fourth preset number of recommended contents on a corresponding page component of the target home page.
9. An electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method of any one of claims 1-7.
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| CN202010225139.1A CN111538930A (en) | 2020-03-26 | 2020-03-26 | Home page display method and related equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434198A (en) * | 2020-11-24 | 2021-03-02 | 深圳市明源云科技有限公司 | Chart component recommendation method and device |
| CN113254444A (en) * | 2021-05-25 | 2021-08-13 | 四川虹魔方网络科技有限公司 | Background attachment implementation method for customized television desktop component |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434198A (en) * | 2020-11-24 | 2021-03-02 | 深圳市明源云科技有限公司 | Chart component recommendation method and device |
| CN113254444A (en) * | 2021-05-25 | 2021-08-13 | 四川虹魔方网络科技有限公司 | Background attachment implementation method for customized television desktop component |
| CN113254444B (en) * | 2021-05-25 | 2022-11-04 | 四川虹魔方网络科技有限公司 | Background attachment implementation method for customized television desktop component |
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