CN117892008A - Tool item recommendation method, device, electronic equipment, storage medium and computer program product - Google Patents
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Abstract
The disclosure provides a tool item recommending method, a device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, in particular to the technical field of machine learning and intelligent recommending. The method comprises the following steps: responding to the operation of triggering a tool item display interface entering a target application by a user, and acquiring user portraits, file browsing behaviors of the user and a history record of the use of the tool item by the user; predicting the recommendation probability of each tool item provided by the target application locally according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item; and selecting target tool items meeting preset conditions according to the recommendation probability of each tool item, and displaying the target tool items in the tool item display interface. In the scheme, the recommendation of the tool item is realized locally in the application program, the intervention of a back-end server is not needed, the network bandwidth cost is reduced to a certain extent, and the recommendation calculation is performed locally, so that the network transmission time is not needed, the time delay caused by network blockage is avoided, and the efficiency of recommending the tool item is further ensured.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular, to the field of machine learning and intelligent recommendation technologies, and in particular, to a tool item recommendation method, apparatus, electronic device, storage medium, and computer program product.
Background
A mobile application (Mobile Application, app for short) refers to an application designed for a mobile device (e.g., smart phone, tablet, etc.). These applications may run on a corresponding operating system, for example in an android system.
Currently, mobile applications often provide a variety of tools and functions in order to enhance user needs and experience, enhance user viscosity, collect and analyze data, gain competitive advantages, build ecosystems, and achieve commercialization goals.
Disclosure of Invention
The present disclosure provides a tool item recommendation method, apparatus, electronic device, storage medium and computer program product.
According to an aspect of the present disclosure, there is provided a tool item recommendation method, including:
Responding to the operation of triggering a tool item display interface entering a target application by a user, and acquiring user portraits, file browsing behaviors of the user and a history record of the use of the tool item by the user;
predicting the recommendation probability of each tool item provided by the target application locally according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item;
and selecting target tool items meeting preset conditions according to the recommendation probability of each tool item, and displaying the target tool items in the tool item display interface.
According to another aspect of the present disclosure, there is provided a tool item recommendation device, including:
The data acquisition module is used for responding to the operation of triggering the tool item display interface entering the target application by the user to acquire the user portrait, the file browsing behavior of the user and the history record of the use of the tool item by the user;
the prediction module is used for predicting the recommendation probability of each tool item provided by the target application according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item;
and the recommendation module is used for selecting target tool items meeting preset conditions according to the recommendation probability of each tool item and displaying the target tool items in the tool item display interface.
According to another aspect of the present disclosure, there is provided an electronic device including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the tool item recommendation method of any embodiment of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the tool item recommendation method of any embodiment of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the tool item recommendation method of any embodiment of the present disclosure.
According to the technology disclosed by the disclosure, the recommendation of the tool item is realized locally on the application program, the intervention of a back-end server is not needed, the network bandwidth cost is reduced to a certain extent, and the recommendation calculation is performed locally, so that the time of network transmission is not needed, the time delay caused by network blocking is avoided, and the efficiency of recommending the tool item is further ensured. In the process of recommending the tool items, the user refers to the history of using the tool items and the file browsing behavior of the user at the same time outside the user portrait, so that the possibility that the recommended tool items are clicked by the user to be used is higher.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow diagram of a tool item recommendation method according to an embodiment of the present disclosure;
FIG. 2 is a flow diagram of another tool item recommendation method according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of another tool item recommendation method according to an embodiment of the present disclosure
FIG. 4 is a schematic structural view of a tool item recommendation device according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of an electronic device for implementing a tool item recommendation method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
When the tool items provided by the mobile application program are displayed, the display sequence of the tool items is that the mobile terminal recommends according to a simple strategy provided by a product manager, or the back terminal recommends according to a preset strategy, and then the determined recommendation result is sent to the mobile terminal for display through a network. These strategies can be simple or complex, and the simple strategies can be direct writing into fixed sequences or recommendation according to names and the use frequency of big data; a complex recommendation strategy may be to make recommendations by collaborative filtering methods, which refer to pushing tool item usage records of users with the same characteristics to another party as recommendation tools. Such complex recommendations require calculation of usage records of other users by the backend server, and communication by means of a network, however, this approach may have a problem that the recommendation is not timely due to network delay. Based on this, the disclosure proposes a method for recommending tool items locally to an application, and the flow of the method can be seen in the following embodiments.
Fig. 1 is a schematic diagram of a tool item recommendation method according to an embodiment of the present disclosure, which is applicable to a scenario in which tool item recommendation is performed locally to an application. The method may be performed by a tool item recommendation device implemented in software and/or hardware and configured in an electronic device.
As shown in fig. 1, the method specifically includes the following steps:
S101, responding to the operation of triggering the tool item display interface entering the target application by the user, and acquiring user portraits, file browsing behaviors of the user and history records of the use of the tool items by the user.
In this embodiment, the target application refers to an application program installed on a mobile device such as a smart phone or a tablet computer, and the target application may be, for example, a web disc application or a browser application, which is not specifically limited herein. The tool items provided by the target application refer to functions within the target application and may be functional components within the target application. Taking the example that the target application is a network disc application, the tool items provided by the target application can include PDF watermarking, PDF conversion, picture watermarking, picture compression and the like. When the target application is a network disk application, the required tool items can be accurately and timely recommended for the network disk user through the scheme.
It should be noted that the tool item provided by the target application is different from the applet. Taking as an example that the target application is a web disc application, the difference is that: the tool item of the network disk application is a part of the network disk application, belongs to the internal function of the network disk application, and is mainly used for helping a user to manage and maintain files in the network disk, and in addition, the tool item of the network disk application can also be used for controlling local files of the mobile terminal. And the applet is a lightweight application running on a specific platform such as WeChat, payment device, etc. The tool items provided by the target application are thus not equivalent to applets, thereby making the method of recommending applets locally different from the method of recommending tool items locally.
For example, the target application is provided with a tool item display interface for displaying tool items recommended to a user. And triggering the recommendation of the tool item when the user enters the tool item display interface of the target application through touch operation. To locally implement the tool item recommendation in the target application, it is necessary to obtain a user portrait, a user's file browsing behavior, and a history of the user using the tool item; the user portraits, the user's file browsing behavior, and the user's history of using tool items can be stored as user data, typically local to the target application, so that these three types of data can be obtained locally to the target application. Wherein the user representation includes at least one of user identity (e.g., student, office worker), user preference (e.g., learning, entertainment), and user equity (e.g., membership equity purchased by the user); the file browsing behavior of the user comprises the current time (for example, the time when the user triggers the recommendation of the tool item), the type of the file browsed by the user in the preset time before the current time, and the type of the target file determined according to the browsing frequency corresponding to the type of the file; the target file type refers to the file type most frequently browsed by a user according to the browsing frequency; the history record of the user using the tool items comprises the tool items used by the user in a preset time before the current time, the types of the tool items, common tools determined according to the using frequency of the tool items, and common tool item types determined according to the corresponding using frequency of each type; that is, the history of the user using the tool items mainly includes the tool items that the user has used recently, the types to which the tool items that have been used recently belong, the tool items that the user has used most often, and the tool item types that the user has used most often. It will be appreciated that a tool item type may correspond to a PDF type of a respective tool item, which may be PDF reading, PDF watermarking, converting to PDF, etc.
S102, predicting the recommendation probability of each tool item provided by the target application locally according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item.
Optionally, predicting, locally to the target application, a recommendation probability of each tool item provided by the target application by adopting a machine learning mode or a data statistics mode based on the user portrait, the file browsing behavior of the user and the history record of the user using the tool item; wherein the recommendation probability is used to measure the likelihood that the tool item is recommended and displayed in the tool item presentation interface.
It can be understood that when the recommendation probability of the tool item is predicted by adopting a machine learning mode, a tool recommendation model can be trained by a machine learning method and deployed on the target application, and the user portrait, the file browsing behavior of the user and the history of the user using the tool item are taken as model inputs, so that the recommendation probability of the tool item can be determined according to model outputs.
When the recommendation probability of the tool items is predicted in a data statistics mode, data analysis can be directly carried out on the user portrait, the file browsing behavior of the user and the history record of the tool items used by the user, the tool items possibly used by the user are determined, and the probability value is set to determine the possibility of being used.
It should be noted that, in the present disclosure, the selection of using the user portrait, the user's file browsing behavior, and the history of using the tool items by the user to predict the recommendation probability of each tool item provided by the target application, instead of predicting the recommendation probability of each tool item by using a single user portrait, considers that the user portrait will not change substantially in a short time, and if the prediction of the recommendation probability of the tool item is performed based on the user portrait only, there is a problem that the recommended tool item is not timely and inaccurate. And the problem can be avoided by predicting based on the three data of the present disclosure.
And S103, selecting a target tool item meeting a preset condition according to the recommendation probability of each tool item, and displaying the target tool item in the tool item display interface.
In an alternative implementation, the tool items may be reordered according to the recommendation probability of each tool item; e.g. reorder in order of the recommended probability from high to low; further selecting a target tool item meeting a preset condition based on the re-ordering result; the preset condition may be that the recommended probability is ranked in the first N bits, or that the recommended probability is greater than a preset probability threshold; when the target tool item is selected according to the reordering result, the target tool item with the recommendation probability larger than the preset probability threshold value can be used as the target tool item, or the recommendation probability is ranked in the first N bits as the target tool item. Therefore, the tool items most likely to be used by the user can be selected and displayed to the user, and the user can be ensured to conveniently and quickly reach the required tool items.
In this embodiment, after the locally recommended tool item is displayed in the tool item display interface of the target application, the tool item and the type to which the tool item belongs that are actually used by the user later can be recorded, and meanwhile, whether the user uses a tool item or a tool item type that is not used before can be determined, or whether the user browses a file or a file type that is not seen before, that is, whether the file browsing behavior of the user and the history record of the tool item used by the user are changed can be determined. It should be noted that, recording the tool items used by the user and the browsed files is performed on the basis of the authorization of the user. On the basis, if the user exits the tool item display interface and then re-triggers the tool item display interface entering the target application within a short time, whether the file browsing behavior of the user and the history record of the tool item used by the user are changed or not is firstly determined, if no change occurs, the target tool item determined last time is recommended to the user, if the change occurs, a new user portrait, the file browsing behavior of the user and the history record of the tool item used by the user are acquired, and the recommendation is carried out again according to the steps of the steps S102-S103.
In this embodiment, the recommendation of the tool item is implemented locally in the application program, without intervention of a back-end server, so that network bandwidth cost is reduced to a certain extent, and because recommendation calculation is performed locally, network transmission time is not needed, delay caused by network blocking is avoided, and efficiency of recommending the tool item is further ensured. In the process of recommending the tool items, the user refers to the history of using the tool items and the file browsing behavior of the user at the same time outside the user portrait, so that the possibility that the recommended tool items are clicked by the user to be used is higher.
FIG. 2 is a flow diagram of another tool item recommendation method according to an embodiment of the present disclosure. As shown in fig. 2, the tool item recommendation method specifically includes the following steps:
and S201, responding to the operation of triggering the tool item display interface entering the target application by the user, and acquiring the user portrait, the file browsing behavior of the user and the history record of the use of the tool item by the user.
In this embodiment, the process of predicting, locally in the target application, the recommendation probability of each tool item provided by the target application according to the user portrait, the user' S file browsing behavior, and the history of the user using the tool item may refer to steps S202-S203.
S202, carrying out feature coding on the user portrait, the file browsing behavior of the user and the history record of the use of the tool item by the user locally at the target application to obtain corresponding feature values.
In this embodiment, a preset encoding algorithm (for example, a single-hot encoding algorithm) may be adopted to perform feature encoding on the user portrait, the user's file browsing behavior, and the history of the user using the tool item, so as to obtain a corresponding feature value.
S203, predicting the recommendation probability of each tool item provided by the target application according to the obtained characteristic value and combining a tool recommendation model which is deployed in the target application in advance.
In this embodiment, the tool recommendation model that is pre-trained and deployed locally to the target application may be a decision tree model. Since the decision tree model is a model allowing multi-feature input, the feature values of the different data obtained in step S202 may be input into the tool recommendation model according to a preset format, and the recommendation probability of the tool item may be determined according to the output of the tool recommendation model.
It should be noted that, the decision tree model is selected as the tool recommendation model, which considers that the decision tree model is more accurate than other logistic regression models, and there is no need to worry about over-fitting; in addition, compared with a support vector machine model or a neural network model, the decision tree model has smaller volume and faster inference, and can ensure the recommending efficiency of the tool items.
S204, selecting a target tool item meeting a preset condition according to the recommendation probability of each tool item, and displaying the target tool item in the tool item display interface.
In this embodiment, the tool item recommendation is locally performed by using a machine learning manner, so that the efficiency of tool item recommendation can be ensured. In addition, the decision tree model is selected as a tool recommendation model, so that model overfitting can be avoided, and the occupied space in local deployment is small under the model volume.
FIG. 3 is a flow diagram of another tool item recommendation method according to an embodiment of the present disclosure. As shown in fig. 3, the tool item recommendation method specifically includes the following steps:
S301, training the tool recommendation model by utilizing a pre-acquired training sample.
Each training sample includes a tool item used by the user, and a history record of the user using other tool items before the user uses the tool item (including a common tool item of the user, a common tool item type, a recently used tool item type), a user portrait (including a user identity, a preference, and the like), and a file browsing behavior of the user (including a file type frequently browsed by the user, a recently used file type, and the like). In training engineering, the common tool item type, the most recently used tool item and the most recently used tool item type are respectively a list, the list can contain N tool items, coding is needed, and other single values only need to be mapped into one data value. And training the coded and mapped data as the input of the model, comparing the recommended probability of each tool item output by the model with the tool item used by the user included in the sample, and returning to adjust the model parameters according to the comparison result. By means of continuous training, a tool recommendation model for predicting the recommendation probability of the tool item can be obtained.
In this embodiment, for the trained tool recommendation model, clipping optimization may be performed to reduce the model size, so as to avoid occupying a large amount of space when the tool recommendation model is deployed locally by the target application.
S302, converting the trained tool recommendation model into a model file in a target format according to the type of a system operated by the terminal where the target application is located.
The system type of the terminal where the target application is located may be a common IOS system, an android system, or other systems capable of running on the mobile terminal, which is not limited herein.
In this embodiment, for convenience of explanation, the system type of the terminal operation is taken as an IOS system as an example. For IOS systems, the tool recommendation model may be converted to a model file in a target format, which may be mlmodel format, using format conversion tool coremltools.
S303, compiling the model file in the target format to obtain a compiled model file.
For example, after obtaining the model file in mlmodel format, in order to ensure that the model can be deployed in the target application, compiling processing may be performed on the model file in mlmodel format to obtain a compiled model file (mlmodelc file).
S304, compressing the compiled model file to obtain a model file compression package corresponding to the tool recommendation model, and storing the model file compression package.
For example, a compression algorithm may be used to compress mlmodelc files to obtain a model file compression package, where the model file compression package may be a zip file package, and further, the model file compression package is stored. The saved model file compression package is essentially an offline package.
The steps S301 to S304 may be performed in the local machine or the server, and are not particularly limited herein.
S305, when a user logs in the target application for the first time, acquiring the model file compression package; decompressing the model file compression package to deploy the tool recommendation model locally to the target application.
In order to avoid overlarge target application installation packages, the method and the device disclosed by the disclosure do not integrate the model file compression packages into the target application installation packages directly, but acquire the model file compression packages from a server or a local machine when a user installs the target application and logs in the target application for the first time, decompress the model file compression packages to obtain mlmodelc files, so that the target application can locally predict the recommendation of the tool items by using mlmodelc files according to steps S306-S308.
S306, responding to the operation of triggering the tool item display interface entering the target application by the user, and acquiring the user portrait, the file browsing behavior of the user and the history record of the use of the tool item by the user.
S307, predicting the recommendation probability of each tool item provided by the target application according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item.
And S308, selecting a target tool item meeting a preset condition according to the recommendation probability of each tool item, and displaying the target tool item in the tool item display interface.
In this embodiment, the trained model file is converted into an offline package, and when the target application is installed and logged in for the first time, the offline package is obtained and the local deployment of the tool recommendation model is completed, so that the problem that the installation package is too large due to the fact that the model file is directly integrated in the installation package is avoided.
Fig. 4 is a schematic structural diagram of a tool item recommendation device according to an embodiment of the present disclosure, which is applicable to a scenario in which tool item recommendation is performed locally to an application. The device can realize the tool item recommending method according to any embodiment of the disclosure. As shown in fig. 4, the apparatus 400 specifically includes:
A data acquisition module 401, configured to acquire a user portrait, a file browsing behavior of a user, and a history of use of a tool item by the user in response to a user triggering an operation of entering a tool item display interface of a target application;
a prediction module 402, configured to predict, locally in the target application, a recommendation probability of each tool item provided by the target application according to the user portrait, a file browsing behavior of the user, and a history of use of the tool item by the user;
And the recommendation module 403 is configured to select, according to the recommendation probability of each tool item, a target tool item that meets a preset condition, and display the target tool item in the tool item display interface.
In an alternative implementation, the prediction module is further configured to:
Performing feature coding on the user portrait, the file browsing behavior of the user and the history record of the tool item used by the user locally at the target application to obtain a corresponding feature value;
and predicting the recommendation probability of each tool item provided by the target application according to the obtained characteristic value and combining a tool recommendation model which is deployed in the target application in advance.
In an alternative implementation, the method further includes:
the training module is used for training the tool recommendation model by utilizing a pre-acquired training sample; each training sample comprises a tool item used by a user, and a history record of the tool item used by the user before the user uses the tool item, a user portrait and file browsing behaviors of the user;
the conversion module is used for converting the trained tool recommendation model into a model file in a target format according to the type of a system operated by the terminal where the target application is located;
The compiling module is used for compiling the model file in the target format to obtain a compiled model file;
And the compression and storage module is used for compressing the compiled model file to obtain a model file compression package corresponding to the tool recommendation model, and storing the model file compression package.
In an alternative implementation, the method further includes:
when a user logs in the target application for the first time, acquiring the model file compression package;
decompressing the model file compression package to deploy the tool recommendation model locally to the target application.
In an alternative implementation, the tool recommendation model is a decision tree model.
In an alternative implementation, the recommendation module is further configured to:
Reordering the tool items according to the recommendation probability of each tool item;
and selecting target tool items meeting preset conditions based on the reordering result.
In an alternative implementation, the user representation includes at least one of a user identity, a user preference, and a user benefit;
The file browsing behavior of the user comprises the current time, the file types browsed by the user in a preset time before the current time and target file types determined according to browsing frequencies corresponding to the file types;
The history record of the tool items used by the user comprises the tool items used by the user in a preset time period before the current time, the types of the tool items, the common tools determined according to the use frequency of the tool items, and the common tool types determined according to the corresponding use frequency of each type.
In an alternative implementation, the target application is a web-disk application.
The product can execute the method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of executing the method.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 5 illustrates a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units executing machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the tool item recommendation method. For example, in some embodiments, the tool item recommendation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the tool item recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the tool item recommendation method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs executing on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligent software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.
Cloud computing (cloud computing) refers to a technical system that a shared physical or virtual resource pool which is elastically extensible is accessed through a network, resources can comprise servers, operating systems, networks, software, applications, storage devices and the like, and resources can be deployed and managed in an on-demand and self-service mode. Through cloud computing technology, high-efficiency and powerful data processing capability can be provided for technical application such as artificial intelligence and blockchain, and model training.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions provided by the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (19)
1. A tool item recommendation method, comprising:
Responding to the operation of triggering a tool item display interface entering a target application by a user, and acquiring user portraits, file browsing behaviors of the user and a history record of the use of the tool item by the user;
predicting the recommendation probability of each tool item provided by the target application locally according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item;
and selecting target tool items meeting preset conditions according to the recommendation probability of each tool item, and displaying the target tool items in the tool item display interface.
2. The method of claim 1, wherein predicting, locally at the target application, a recommendation probability for each tool item provided by the target application based on the user representation, a user's file browsing behavior, and a user's history of using tool items, comprises:
Performing feature coding on the user portrait, the file browsing behavior of the user and the history record of the tool item used by the user locally at the target application to obtain a corresponding feature value;
and predicting the recommendation probability of each tool item provided by the target application according to the obtained characteristic value and combining a tool recommendation model which is deployed in the target application in advance.
3. The method of claim 2, further comprising:
Training the tool recommendation model by utilizing a pre-acquired training sample; each training sample comprises a tool item used by a user, and a history record of the tool item used by the user before the user uses the tool item, a user portrait and file browsing behaviors of the user;
according to the type of a system operated by the terminal where the target application is located, converting the trained tool recommendation model into a model file in a target format;
compiling the model file in the target format to obtain a compiled model file;
compressing the compiled model file to obtain a model file compression package corresponding to the tool recommendation model, and storing the model file compression package.
4. A method according to claim 3, further comprising:
when a user logs in the target application for the first time, acquiring the model file compression package;
decompressing the model file compression package to deploy the tool recommendation model locally to the target application.
5. The method of any of claims 2-4, wherein the tool recommendation model is a decision tree model.
6. The method of claim 1, wherein the selecting the target tool item satisfying the preset condition according to the recommendation probability of each tool item comprises:
Reordering the tool items according to the recommendation probability of each tool item;
and selecting target tool items meeting preset conditions based on the reordering result.
7. The method of claim 1 or 2, wherein the user representation comprises at least one of a user identity, a user preference, and a user benefit;
The file browsing behavior of the user comprises the current time, the file types browsed by the user in a preset time before the current time and target file types determined according to browsing frequencies corresponding to the file types;
The history record of the tool items used by the user comprises the tool items used by the user in a preset time period before the current time, the types of the tool items, the common tools determined according to the use frequency of the tool items, and the common tool types determined according to the corresponding use frequency of each type.
8. The method of any of claims 1-4, wherein the target application is a web-disk application.
9. A tool item recommendation device, comprising:
The data acquisition module is used for responding to the operation of triggering the tool item display interface entering the target application by the user to acquire the user portrait, the file browsing behavior of the user and the history record of the use of the tool item by the user;
the prediction module is used for predicting the recommendation probability of each tool item provided by the target application according to the user portrait, the file browsing behavior of the user and the history record of the user using the tool item;
and the recommendation module is used for selecting target tool items meeting preset conditions according to the recommendation probability of each tool item and displaying the target tool items in the tool item display interface.
10. The apparatus of claim 9, wherein the prediction module is further to:
Performing feature coding on the user portrait, the file browsing behavior of the user and the history record of the tool item used by the user locally at the target application to obtain a corresponding feature value;
and predicting the recommendation probability of each tool item provided by the target application according to the obtained characteristic value and combining a tool recommendation model which is deployed in the target application in advance.
11. The apparatus of claim 10, further comprising:
the training module is used for training the tool recommendation model by utilizing a pre-acquired training sample; each training sample comprises a tool item used by a user, and a history record of the tool item used by the user before the user uses the tool item, a user portrait and file browsing behaviors of the user;
the conversion module is used for converting the trained tool recommendation model into a model file in a target format according to the type of a system operated by the terminal where the target application is located;
The compiling module is used for compiling the model file in the target format to obtain a compiled model file;
And the compression and storage module is used for compressing the compiled model file to obtain a model file compression package corresponding to the tool recommendation model, and storing the model file compression package.
12. The apparatus of claim 11, further comprising:
when a user logs in the target application for the first time, acquiring the model file compression package;
decompressing the model file compression package to deploy the tool recommendation model locally to the target application.
13. The apparatus of any of claims 10-12, wherein the tool recommendation model is a decision tree model.
14. The apparatus of claim 9, wherein the recommendation module is further to:
Reordering the tool items according to the recommendation probability of each tool item;
and selecting target tool items meeting preset conditions based on the reordering result.
15. The apparatus of claim 9 or 10, wherein the user representation comprises at least one of a user identity, a user preference, and a user benefit;
The file browsing behavior of the user comprises the current time, the file types browsed by the user in a preset time before the current time and target file types determined according to browsing frequencies corresponding to the file types;
The history record of the tool items used by the user comprises the tool items used by the user in a preset time period before the current time, the types of the tool items, the common tools determined according to the use frequency of the tool items, and the common tool types determined according to the corresponding use frequency of each type.
16. The apparatus of any of claims 9-12, wherein the target application is a web-disk application.
17. An electronic device, comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the tool item recommendation method of any one of claims 1-8.
18. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the tool item recommendation method according to any one of claims 1-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements the tool item recommendation method according to any one of claims 1-8.
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