Intelligent application recommendation method and system

Abstract

The invention relates to the technical field of informatization software, and particularly provides an intelligent application recommendation method and system, and the method comprises the following steps: S1, data collection and preprocessing; s2, feature extraction and feature combination; s3, model training; and S4, generating a recommendation result. Compared with the prior art, the method and the device have the advantages that the time required by a user to find a proper application is shortened, and the satisfaction of the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of information software technology, and specifically provides an intelligent application recommendation method and system. Background Art

[0002] Big data analysis refers to the process of extracting useful information from large amounts of complex data. This process usually involves knowledge and techniques from multiple fields such as data mining, machine learning, and statistics. By analyzing big data, patterns and rules in the data can be discovered, thus providing a basis for decision-making. In the field of intelligent application recommendation, big data analysis can help the system better understand user needs and predict user behavior, thereby improving the accuracy and personalization of recommendations.

[0003] Intelligent application recommendation is to recommend applications or services that users may be interested in based on their historical behaviors, preferences and other relevant information. With the popularity of smart phones and tablets, the mobile application market has developed rapidly, and the number of applications that users can choose from has increased dramatically, making it difficult for users to find the most suitable application. Therefore, an efficient and personalized recommendation system becomes particularly important.

[0004] It can be seen that how to solve the problem that users can quickly find service items that meet their needs and are commonly used among numerous application services without having to spend a lot of time and energy on screening and trial is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] The present invention aims to overcome the above-mentioned deficiencies in the prior art and provides a highly practical intelligent application recommendation method.

[0006] A further technical task of the present invention is to provide a smart application recommendation system that is reasonably designed, safe and applicable.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] An intelligent application recommendation method comprises the following steps:

[0009] S1, data collection and preprocessing;

[0010] S2, feature extraction and feature combination;

[0011] S3, model training;

[0012] S4. Generate recommendation results.

[0013] Furthermore, in step S1, it includes:

[0014] S1-1. Data sources;

[0015] User behavior data: including user downloads and usage of apps;

[0016] User personal information: age, gender, geographic location;

[0017] Application metadata: application category, rating, description;

[0018] S1-2, data preprocessing;

[0019] Perform data cleaning, remove invalid or erroneous data records, unify the format, and then process missing values, using interpolation or other methods to fill in missing values. Finally, standardize and normalize the data, scale the numerical data, and ensure the balance between different features.

[0020] Furthermore, in step S2, feature extraction includes user feature extraction and application feature extraction. User feature extraction is to extract useful features from user behavior data and personal information, and application feature extraction is to extract basic attributes of the application and user feedback on the application.

[0021] When combining features, different types of features are cross-combined to capture deeper information associations; feature selection techniques are used to screen out the most representative feature set.

[0022] Furthermore, in step S3, it includes:

[0023] S3-1. Model selection: Select the appropriate recommendation algorithm based on the business scenario;

[0024] S3-2, Training process: Divide the training set and test set to ensure the generalization ability of the model, and use cross-validation technology to evaluate the model performance.

[0025] Furthermore, in step S4, it includes:

[0026] S4-1, Recommendation strategy: Real-time recommendation, combining long-term interests and short-term interests, balancing novelty and diversity, introducing exploration-exploitation mechanism, and encouraging users to try new applications;

[0027] S4-2. Recommendation result evaluation: Regularly collect user feedback and evaluate the recommendation effect; analyze the difference between the recommendation results and the actual user behavior, and continuously iterate and optimize the algorithm.

[0028] An intelligent application recommendation system, comprising a data collection and preprocessing module, a feature extraction and combination module, a model training module and a recommendation result generation module;

[0029] The data collection and preprocessing module is used to collect user behavior data and personal information data and preprocess the collected data;

[0030] The feature extraction and combination module is used to extract user features and application features from the preprocessed data;

[0031] The model training module is used to divide the training set and the test set to ensure the generalization ability of the model;

[0032] The recommendation result generation module is used for recommendation strategy and recommendation result evaluation.

[0033] Furthermore, the data collection and preprocessing module includes collecting user behavior data, user download and application usage; user personal information, age, gender, geographic location; application metadata, application category, rating, description;

[0034] Perform data cleaning, remove invalid or erroneous data records, unify the format, and then process missing values ​​by using interpolation or other methods to fill in missing values. Finally, standardize and normalize the data, scale the numerical data, and ensure the balance between different features.

[0035] Furthermore, the feature extraction and combination module feature extraction includes user feature extraction and application feature extraction. User feature extraction is to extract useful features from user behavior data and personal information, and application feature extraction is to extract basic attributes of the application and user feedback on the application.

[0036] When combining features, different types of features are cross-combined to capture deeper information associations; feature selection techniques are used to screen out the most representative feature set.

[0037] Furthermore, the model training module selects a suitable recommendation algorithm according to the business scenario, divides the training set and the test set to ensure the generalization ability of the model, and uses cross-validation technology to evaluate the model performance.

[0038] Furthermore, the recommendation result generation module makes real-time recommendations, combines long-term interests with short-term interests, balances novelty and diversity, introduces an exploration-exploitation mechanism, and encourages users to try new applications;

[0039] Regularly collect user feedback and evaluate recommendation effectiveness; analyze the differences between recommendation results and actual user behavior, and continuously iterate and optimize the algorithm.

[0040] Compared with the prior art, the intelligent application recommendation method and system of the present invention has the following outstanding beneficial effects:

[0041] (1) Improve user experience: Personalized recommendations can significantly improve user experience. By analyzing user usage habits, historical behaviors, and preferences, intelligent recommendation systems can provide users with application recommendations that are more in line with their needs, reducing the time users spend searching for suitable applications and increasing user satisfaction.

[0042] (2) Promote application discovery and innovation. For developers, intelligent recommendation methods can help those niche but high-quality applications gain more exposure opportunities, promoting the diversity and innovation of the application market. At the same time, by analyzing user feedback and usage data, developers can obtain valuable market insights and guide product iteration and optimization.

[0043] (3) Optimize resource allocation. The platform can use intelligent recommendation technology to achieve more effective resource allocation, such as advertising and promotional activities, to ensure that marketing resources are invested in the target user groups that are most likely to convert, thereby improving the return on investment.

[0044] (4) Enhance user stickiness and activity. High-quality recommendations can enhance user loyalty to the platform and improve user activity and retention. When users find that the platform can continuously provide valuable information or services, they are more willing to visit frequently and stay longer.

[0045] (5) Supporting data-driven decision making. For enterprises, intelligent recommendation systems can not only serve as user-oriented tools, but also as an important source of internal decision support. Through in-depth analysis of recommendation results and the data behind them, enterprises can better understand market demand and guide product planning, marketing strategies and other aspects.

[0046] (6) Promote social and economic benefits. From a broader social perspective, intelligent recommendation helps create a more prosperous application ecosystem, promote information circulation and technology diffusion, and ultimately promote the overall development of the social economy.

[0047] (7) Dealing with information overload: As the amount of information on the Internet continues to increase, users are facing the problem of information overload. Intelligent recommendation systems help users quickly find content of interest by filtering irrelevant information, thereby improving the efficiency of information acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Attached Figure 1The present invention is a flowchart of an intelligent application recommendation method. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] A best embodiment is given below:

[0052] like Figure 1 As shown, a smart application recommendation method in this embodiment has the following steps:

[0053] S1, data collection and preprocessing;

[0054] include:

[0055] S1-1. Data sources;

[0056] User behavior data: including user downloads and usage of apps, such as click-through rate, usage duration, etc.

[0057] User personal information: age, gender, geographic location;

[0058] Application metadata: application category, rating, description;

[0059] S1-2, data preprocessing;

[0060] Perform data cleaning, remove invalid or erroneous data records, unify the format, and then process missing values, using interpolation or other methods to fill in missing values. Finally, standardize and normalize the data, scale the numerical data, and ensure the balance between different features.

[0061] S2, feature extraction and feature combination;

[0062] Feature extraction includes user feature extraction and application feature extraction. User feature extraction is to extract useful features from user behavior data and personal information, such as active time periods, commonly used device types, etc.; application feature extraction is to extract the basic attributes of the application, as well as user feedback on the application, such as comment sentiment tendency, update frequency, etc.

[0063] When combining features, different types of features are cross-combined to capture deeper information associations; feature selection techniques such as chi-square test and mutual information are used to screen out the most representative feature set.

[0064] S3, model training;

[0065] include:

[0066] S3-1. Model selection: Select appropriate recommendation algorithms according to business scenarios, such as collaborative filtering, content-based recommendation, matrix decomposition, etc.

[0067] S3-2, Training process: Divide the training set and test set to ensure the generalization ability of the model, and use cross-validation technology to evaluate the model performance.

[0068] S4, generation of recommendation results;

[0069] include:

[0070] S4-1, Recommendation strategy: Real-time recommendation, combining long-term interests and short-term interests, balancing novelty and diversity, introducing exploration-exploitation mechanism, and encouraging users to try new applications;

[0071] S4-2. Recommendation result evaluation: Regularly collect user feedback and evaluate the recommendation effect; analyze the difference between the recommendation results and the actual user behavior, and continuously iterate and optimize the algorithm.

[0072] Based on the above method, an intelligent application recommendation system in this embodiment includes a data collection and preprocessing module, a feature extraction and combination module, a model training module and a recommendation result generation module;

[0073] The data collection and preprocessing module is used to collect user behavior data and personal information data and preprocess the collected data;

[0074] The feature extraction and combination module is used to extract user features and application features from the preprocessed data;

[0075] The model training module is used to divide the training set and the test set to ensure the generalization ability of the model;

[0076] The recommendation result generation module is used for recommendation strategy and recommendation result evaluation.

[0077] The data collection and preprocessing module includes collecting user behavior data, user downloads and application usage; user personal information, age, gender, and geographic location; application metadata, application category, rating, and description;

[0078] Perform data cleaning, remove invalid or erroneous data records, unify the format, and then process missing values, using interpolation or other methods to fill in missing values. Finally, standardize and normalize the data, scale the numerical data, and ensure the balance between different features.

[0079] Feature extraction and combination module feature extraction includes user feature extraction and application feature extraction. User feature extraction is to extract useful features from user behavior data and personal information, and application feature extraction is to extract the basic attributes of the application and the user's feedback on the application;

[0080] When combining features, different types of features are cross-combined to capture deeper information associations; feature selection techniques are used to screen out the most representative feature set.

[0081] The model training module selects the appropriate recommendation algorithm based on the business scenario, divides the training set and test set to ensure the generalization ability of the model, and uses cross-validation technology to evaluate the model performance.

[0082] The recommendation result generation module makes real-time recommendations, combines long-term interests with short-term interests, balances novelty and diversity, and introduces an exploration-exploitation mechanism to encourage users to try new applications.

[0083] Regularly collect user feedback and evaluate recommendation effectiveness; analyze the differences between recommendation results and actual user behavior, and continuously iterate and optimize the algorithm.

[0084] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent application recommendation, characterized in that: The steps are as follows: S1, data collection and preprocessing; S2, feature extraction and feature combination; S3, model training; S4: Generate recommendation results.

2. The intelligent application recommendation method according to claim 1, characterized in that: In step S1, it includes: S1-1. Data sources; User behavior data: including user downloads and usage of apps; User personal information: age, gender, geographic location; Application metadata: application category, rating, description; S1-2, data preprocessing; Perform data cleaning, remove invalid or erroneous data records, unify the format, and then process missing values ​​by using interpolation or other methods to fill in missing values. Finally, standardize and normalize the data, scale the numerical data, and ensure the balance between different features.

3. The intelligent application recommendation method according to claim 2, characterized in that: In step S2, feature extraction includes user feature extraction and application feature extraction. User feature extraction is to extract useful features from user behavior data and personal information, and application feature extraction is to extract basic attributes of the application and user feedback on the application. When combining features, different types of features are cross-combined to capture deeper information associations; Use feature selection techniques to select the most representative feature set.

4. The intelligent application recommendation method according to claim 3, characterized in that: In step S3, it includes: S3-1. Model selection: Select the appropriate recommendation algorithm based on the business scenario; S3-2, Training process: Divide the training set and test set to ensure the generalization ability of the model, and use cross-validation technology to evaluate the model performance.

5. The intelligent application recommendation method according to claim 4, characterized in that: In step S4, it includes: S4-1, Recommendation strategy: Real-time recommendation, combining long-term interests and short-term interests, balancing novelty and diversity, introducing exploration-exploitation mechanism, and encouraging users to try new applications; S4-2. Recommendation result evaluation: Regularly collect user feedback and evaluate the recommendation effect; analyze the difference between the recommendation results and the actual user behavior, and continuously iterate and optimize the algorithm.

6. An intelligent application recommendation system, characterized in that: It includes data collection and preprocessing module, feature extraction and combination module, model training module and recommendation result generation module; The data collection and preprocessing module is used to collect user behavior data and personal information data and preprocess the collected data; The feature extraction and combination module is used to extract user features and application features from the preprocessed data; The model training module is used to divide the training set and the test set to ensure the generalization ability of the model; The recommendation result generation module is used for recommendation strategy and recommendation result evaluation.

7. The intelligent application recommendation system according to claim 6, characterized in that: The data collection and preprocessing module includes collecting user behavior data, user downloads, and application usage; User personal information, age, gender, and geographic location; Application metadata, application category, rating, description; Perform data cleaning, remove invalid or erroneous data records, unify the format, and then process missing values ​​by using interpolation or other methods to fill in missing values. Finally, standardize and normalize the data, scale the numerical data, and ensure the balance between different features.

8. The intelligent application recommendation system according to claim 7, characterized in that: Feature extraction and combination module feature extraction includes user feature extraction and application feature extraction. User feature extraction is to extract useful features from user behavior data and personal information, and application feature extraction is to extract the basic attributes of the application and the user's feedback on the application; When combining features, different types of features are cross-combined to capture deeper information associations; Use feature selection techniques to select the most representative feature set.

9. The intelligent application recommendation system according to claim 8, characterized in that: The model training module selects a suitable recommendation algorithm according to the business scenario, divides the training set and the test set to ensure the generalization ability of the model, and uses cross-validation technology to evaluate the model performance.

10. The intelligent application recommendation system according to claim 9, characterized in that: The recommendation result generation module makes real-time recommendations, combines long-term interests with short-term interests, balances novelty and diversity, introduces an exploration-exploitation mechanism, and encourages users to try new applications; Regularly collect user feedback and evaluate recommendation effectiveness; analyze the differences between recommendation results and actual user behavior, and continuously iterate and optimize the algorithm.

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