Information recommendation method and device, storage medium and electronic equipment

Abstract

The invention discloses an information recommendation method and device, a storage medium and electronic equipment. The information recommendation method comprises the steps of obtaining to-be-recommended information; the to-be-recommended information is input into a pre-trained information extraction model, element point information contained in the to-be-recommended information is extracted through the information extraction model, and the element point information is used for representing information content corresponding to key time nodes in the to-be-recommended information; each piece of element point information is input into a pre-trained evaluation model, an evaluation value corresponding to each piece of element point information is determined through the evaluation model, and for each piece of element point information, the evaluation value corresponding to the element point information is used for representing the contribution degree of the element point information to the expected browsing condition of the to-be-recommended information; and according to the evaluation value, adjusting the to-be-recommended information so as to perform information recommendation through the adjusted to-be-recommended information. According to the scheme, the to-be-recommended information is optimized according to the evaluation value corresponding to each element point, and the information recommendation effect is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an information recommendation method, device, storage medium and electronic device. Background Art

[0002] Information recommendation is an effective marketing method. On the one hand, it can fully meet user needs, and on the other hand, it can attract users to use products or enjoy services. Therefore, it is widely used in the promotion and publicity of industrial products, food, games, film and television works, and service projects.

[0003] However, the information recommended at present usually relies on manual production. The producer's understanding of the business and production level seriously limit the effect of information recommendation. The information recommended to users is difficult to better express the actual content, resulting in low accuracy of content expression, which greatly affects the user experience.

[0004] Therefore, how to improve the effect of information recommendation is an urgent problem to be solved. Summary of the invention

[0005] This specification provides an information recommendation method, device, storage medium and electronic device to partially solve the above problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides an information recommendation method, including:

[0008] Get the information to be recommended;

[0009] Inputting the information to be recommended into a pre-trained information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model, wherein the element point information is used to characterize the information content corresponding to the key time node in the information to be recommended;

[0010] Inputting each element point information into a pre-trained evaluation model to determine an evaluation value corresponding to each element point information through the evaluation model, wherein for each element point information, the evaluation value corresponding to the element point information is used to represent the contribution of the element point information to the expected browsing situation of the information to be recommended;

[0011] The information to be recommended is adjusted according to the evaluation value, so as to perform information recommendation through the adjusted information to be recommended.

[0012] Optionally, before the step of inputting the information to be recommended into a pre-trained information extraction model, the method further includes:

[0013] Dividing the information to be recommended to obtain at least one type of sub-data, the type including: text, image, audio;

[0014] The step of inputting the information to be recommended into a pre-trained information extraction model to extract information of each element point contained in the information to be recommended through the information extraction model specifically includes:

[0015] The at least one type of sub-data is input into the information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model.

[0016] Optionally, the step of extracting information of each element point contained in the information to be recommended by using the information extraction model specifically includes:

[0017] For each time node in the information to be recommended, determining a richness value corresponding to each type of sub-data at the time node;

[0018] The information of each element point included in the information to be recommended is determined according to the richness value corresponding to each type of sub-data at each time node.

[0019] Optionally, the step of determining each element point information included in the information to be recommended according to the richness value corresponding to each type of sub-data at each time node specifically includes:

[0020] For each time node in the information to be recommended, determine the criticality corresponding to the time node according to the richness value corresponding to each type of sub-data at the time node and the preset weight corresponding to each type of sub-data;

[0021] If the criticality corresponding to the time node is greater than the preset criticality, the element point information is determined according to the information content corresponding to the time node.

[0022] Optionally, before the step of inputting the information to be recommended into a pre-trained information extraction model, the method further includes:

[0023] Processing the information to be recommended to obtain a data sequence of a specified number of frames;

[0024] The step of inputting the information to be recommended into a pre-trained information extraction model to extract information of each element point contained in the information to be recommended through the information extraction model specifically includes:

[0025] The data sequence of the designated number of frames is input into the information extraction model, so as to extract information of each element point contained in the data sequence through the information extraction model.

[0026] Optionally, the step before inputting the information to be recommended into a pre-trained information extraction model further includes:

[0027] Determining the application scenario corresponding to the information content of the information to be recommended;

[0028] The step of inputting the information of each element point into a pre-trained evaluation model to determine the evaluation value corresponding to each element point information through the evaluation model specifically includes:

[0029] The information of each element point and the scene information of the application scene are input into the evaluation model, so as to determine the evaluation value corresponding to each element point information in the application scene through the evaluation model.

[0030] Optionally, the step of optimizing the information to be recommended according to the evaluation value specifically includes:

[0031] For each element point information, if the evaluation value corresponding to the element point information is lower than the preset threshold, several element point information located before the time node corresponding to the element point information and several element point information located after the time node corresponding to the element point information are used as the context information corresponding to the element point information;

[0032] According to the context information, the information content at the time node corresponding to the element point information is adjusted.

[0033] Optionally, the step of training the information extraction model comprises:

[0034] Acquire historical recommendation information, and determine each user's browsing record for the historical recommendation information in history, wherein the browsing record is used to indicate each user's browsing and clicking on the information content at each time point in the historical recommendation information;

[0035] Inputting the historical recommendation information into the information extraction model, so as to determine the historical element point information contained in the historical recommendation information through the information extraction model, and determining the key time node in the historical recommendation information according to the browsing history;

[0036] The information extraction model is trained with the optimization goal of minimizing the deviation between the historical element point information and the information content corresponding to the key time nodes in the historical recommendation information.

[0037] Optionally, the step of training the evaluation model comprises:

[0038] Acquire historical recommendation information, and determine each user's browsing record for the historical recommendation information in history, wherein the browsing record is used to indicate each user's browsing and clicking on the information content at each time point in the historical recommendation information;

[0039] Inputting the historical recommendation information into a pre-trained information extraction model, so as to extract each element point information contained in the recommendation information as the historical element point information through the information extraction model;

[0040] Inputting the historical element point information into the evaluation model to determine the evaluation value corresponding to each historical element point information through the evaluation model;

[0041] The evaluation model is trained according to the evaluation value corresponding to each historical element point information and the browsing record.

[0042] This specification provides an information recommendation device, including:

[0043] An acquisition module is used to acquire information to be recommended;

[0044] An extraction module, used for inputting the information to be recommended into a pre-trained information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model, wherein the element point information is used to characterize the information content corresponding to the key time node in the information to be recommended;

[0045] An evaluation module, used for inputting the information of each element point into a pre-trained evaluation model, so as to determine the evaluation value corresponding to each element point information through the evaluation model, and for each element point information, the evaluation value corresponding to the element point information is used to represent the contribution degree of the element point information to the expected browsing situation of the information to be recommended;

[0046] The recommendation module is used to adjust the information to be recommended according to the evaluation value, so as to recommend information through the adjusted information to be recommended.

[0047] This specification provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned information recommendation method is implemented.

[0048] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned information recommendation method when executing the program.

[0049] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0050] In the information recommendation method provided in the present specification, information to be recommended is obtained; the information to be recommended is input into a pre-trained information extraction model to extract each element point information contained in the information to be recommended through the information extraction model, and the element point information is used to characterize the information content corresponding to the key time nodes in the information to be recommended; each element point information is input into a pre-trained evaluation model to determine the evaluation value corresponding to each element point information through the evaluation model, and for each element point information, the evaluation value corresponding to the element point information is used to characterize the contribution of the element point information to the expected browsing situation of the recommended information; according to the evaluation value, the recommended information is adjusted to recommend information through the adjusted information to be recommended.

[0051] It can be seen from the above method that in the process of recommending information to be recommended, this scheme can extract the information of each element point contained in the information to be recommended and evaluate each element point information. In this way, the information to be recommended can be optimized based on the evaluation value corresponding to each element point information, so that the information to be recommended can achieve the best recommendation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0053] Figure 1 A flowchart of an information recommendation method provided in this specification;

[0054] Figure 2 A schematic diagram of an optimization method for advertising materials provided in this specification;

[0055] Figure 3 A schematic diagram of an information recommendation device provided in this specification;

[0056] Figure 4 A method corresponding to the Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0058] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0059] Figure 1 A flowchart of an information recommendation method provided in this specification includes the following steps:

[0060] S101: Obtaining information to be recommended.

[0061] With the booming development of the gaming industry, advertising marketing has become an important means to attract players, and the quality of advertising materials directly affects the advertising effect. However, the production of traditional game advertising materials mostly relies on manual labor, which is not only time-consuming and labor-intensive, but also difficult to quantify the effect.

[0062] Based on this, this specification provides an information recommendation method, which extracts key point information in the information to be recommended (advertising material) through a deep learning model, and optimizes the recommended information based on the evaluation results of each key point information to further improve the attractiveness of the information to be recommended to users.

[0063] In this specification, the execution subject used to implement an information recommendation method can be a designated device such as a server, and of course, it can also be a client. For the sake of ease of description, the following will only take the server as an execution subject as an example to illustrate an information recommendation method provided in this specification.

[0064] The server may obtain the information to be recommended, and the information to be recommended may include at least one of video, text and audio. In this specification, the information to be recommended may be an advertisement material in a target application scenario.

[0065] The server can determine the application scenario corresponding to the information content of the information to be recommended. In this specification, the application scenario may include: product recommendation, game recommendation, film and television work recommendation and service item recommendation, etc. The scenario information of the application scenario may include classification information of the application scenario. Taking the game recommendation scenario as an example, its scenario information may include the style type of the game, such as action, strategy, and development, etc. Taking film and television works as an example, its scenario information may include the type of film and television works, such as martial arts, romance, reasoning, science fiction, etc.

[0066] After obtaining the information to be recommended, the server can pre-process the information to be recommended, wherein the server can process the information to be recommended to obtain a data sequence of a specified number of frames. For example, for a 30-frame video, the server can extract an image every 3 frames to obtain a data sequence containing 10 frames of images, thereby achieving data compression and improving overall efficiency.

[0067] In addition, the server may also divide the information to be recommended to obtain at least one type of sub-data, and the sub-data type may include text, image, and audio. Taking the information to be recommended composed of video, text, and audio as an example, the server may divide it into pure image data, pure audio data, and pure text data, and the data sequences of different types of data are aligned with each other in time.

[0068] It should be pointed out that the server can first determine the data sequence of the specified number of frames, and then divide the data sequence of the specified number of frames into different types of sub-data based on the data sequence of the specified number of frames as the processed data. The server can also first divide the information to be recommended into different types of sub-data based on the data sequence of the specified number of frames, and then determine the data sequence of the specified number of frames corresponding to the different types of sub-data. Of course, the server can also use any of these methods to pre-process the recommended information to obtain processed data.

[0069] S102: Inputting the information to be recommended into a pre-trained information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model, wherein the element point information is used to characterize the information content corresponding to the key time node in the information to be recommended.

[0070] The server may input the processed data into a pre-trained information extraction model, and extract information of each feature point contained in the processed data through the information extraction model.

[0071] In this specification, a time node may refer to a frame or a moment in the time series of the information to be recommended. Of course, it may also refer to a specific time period. The length of each time period may be determined based on actual conditions, and this specification does not make any specific limitations on this.

[0072] The key point information is used to characterize the information content corresponding to the key time node in the information to be recommended. The key time node can be understood as the time point or time period when the user's browsing and clicking situation may change significantly, such as the time node when the user's click rate or browsing rate changes significantly, or the time node when the user stays for a long time.

[0073] In addition, the above information content includes not only images, audio and text content, but also special effects added to the video.

[0074] Specifically, when the processed data is sub-data of different types, the server may input at least one type of sub-data into the information extraction model, so as to extract information of each element point included in the information to be recommended through the information extraction model.

[0075] For each time node in the information to be recommended, the information extraction model can determine the richness value corresponding to each type of sub-data at the time node. For text data, its richness can be determined based on the number of words in the text. The more words there are, the greater the richness value, and vice versa. For image data, its richness can be used to express the diversity of elements in the image (such as people, buildings, objects, etc.) and the vividness of colors. For audio data, its richness can be used to express the diversity of melody and tone changes.

[0076] Afterwards, the information of each element point included in the information to be recommended can be determined according to the richness value corresponding to each type of sub-data at each time node.

[0077] Among them, for each time node in the information to be recommended, a weighted sum can be performed according to the richness value corresponding to each type of sub-data at the time node and the preset weight corresponding to each type of sub-data, so as to determine the criticality corresponding to the time node.

[0078] If the criticality corresponding to the time node is greater than the preset criticality, it means that the time node is a critical time node in the information to be recommended, and the server can determine the key point information according to the information content corresponding to the time node.

[0079] For example, the server may use the feature vector of the information content corresponding to the time node as the feature point information. Of course, the server may also directly use the information content corresponding to the time node as the feature point information.

[0080] Of course, the server may not pre-process the information to be recommended, but directly input the information to be recommended into the information extraction model, so as to extract the information of each element point contained in the information to be recommended through the information extraction model.

[0081] Before using the above information extraction model, the server may first train the information extraction model.

[0082] Among them, the server can obtain historical recommendation information and determine the historical browsing records of each historical user for the historical recommendation information. The browsing records are used to indicate the browsing and clicking situations (such as click-through rate, browsing rate, etc.) of each historical user on the information content of each time node in the historical recommendation information.

[0083] In other words, the historical recommendation information may be a time series of audio and video data, each frame of which records browsing records, and the time series of browsing records and the time series of audio and video data are aligned in time scale.

[0084] The server may input the historical recommendation information into the information extraction model to determine the historical element point information included in the historical information to be recommended through the information extraction model.

[0085] At the same time, the server may determine the key time nodes in the historical information to be recommended according to the browsing history.

[0086] Taking the browsing history including the user's click rate and browsing rate as an example, the key time node may be the time node when the change in the browsing rate and / or click rate in the browsing history exceeds a preset change amount.

[0087] Of course, the key time node may also be a time node when the browsing rate and / or click rate exceeds the first preset threshold (i.e., a time node when the browsing rate and / or click rate is relatively high), and a time node when the browsing rate and / or click rate is lower than the second preset threshold (i.e., a time node when the browsing rate and / or click rate is relatively low). The above-mentioned preset change amount, the first preset threshold, and the second preset threshold may be set according to actual conditions, and this specification does not specifically limit this.

[0088] After determining the key time node in the historical recommendation information, the server can determine the information content corresponding to the key time node, and train the information extraction model with the optimization goal of minimizing the deviation between the information content and the historical feature point information, so that the feature point information extracted by the trained information extraction model is as close as possible to the information content corresponding to the key time node that has the greatest impact on the user's browsing situation.

[0089] S103: Input the information of each element point into a pre-trained evaluation model to determine the evaluation value corresponding to each element point information through the evaluation model. For each element point information, the evaluation value corresponding to the element point information is used to characterize the contribution of the element point information to the expected browsing situation of the information to be recommended.

[0090] After determining the information of each element point included in the information to be recommended, the server can input the information of each element point into a pre-trained evaluation model, and then evaluate each element point information through the evaluation model to determine the evaluation value corresponding to each element point information. The evaluation value is used to characterize the contribution of each element point information to the expected browsing situation (such as click-through rate and browsing rate) of the recommended information.

[0091] Among them, the greater the contribution of feature point information to the browsing situation, the greater the corresponding evaluation value, and vice versa.

[0092] In addition, for different business scenarios, different information content contributes differently to browsing situations. For example, in game scenarios, for action games, users may prefer tense and exciting fighting scenes and dynamic music, while for development games, what attracts users may be warm and comfortable game scenes and soothing music.

[0093] Therefore, in the process of evaluating the element point information through the evaluation model, the server can input each element point information and the scene information of the corresponding application scene into the evaluation model, so as to determine the evaluation value of each element point information in its corresponding application scene through the evaluation model, so as to ensure that the determined evaluation value matches the scene information of the application scene.

[0094] Before using the above evaluation model, the server may train the evaluation model.

[0095] Specifically, the server may obtain the historical recommendation information, and determine the browsing records of each historical user for the historical recommendation information, and then input the historical recommendation information into the above-mentioned information extraction model, so as to extract each element point information contained in the recommendation information as the historical element point information through the information extraction model;

[0096] The server can then input the historical feature point information into the evaluation model to determine the evaluation value corresponding to each historical feature point information through the evaluation model, and then train the evaluation model according to the evaluation value corresponding to each historical feature point information and the browsing history.

[0097] For example, the server can sort the historical element point information according to the evaluation value corresponding to each element point information to obtain the predicted sorting result. At the same time, based on the actual click rate and / or browsing rate at the time node where each historical element point information is located, the server can sort the historical element point information to obtain the actual sorting result, and then train the evaluation model with the optimization goal of minimizing the deviation between the predicted sorting result and the actual sorting result.

[0098] For another example, the server can select a specified number of element point information in descending order of evaluation values ​​as the first element point information, and select the same number of element point information in descending order of actual click rate and / or browsing rate as the second element point information, and then train the evaluation model with minimizing the deviation between the first element point information and the second element point information as the optimization goal.

[0099] S104: adjusting the information to be recommended according to the evaluation value, so as to recommend information through the adjusted information to be recommended.

[0100] After determining the evaluation value corresponding to each element point information, the server may optimize the recommended information according to the evaluation value corresponding to each element point information.

[0101] Specifically, for each element point information, if the evaluation value corresponding to the element point information is lower than a preset threshold, the server may adjust the information content at the time node corresponding to the element point information to optimize the recommended information.

[0102] Among them, for each element point information, if the evaluation value corresponding to the element point information is lower than the preset threshold, several element point information located before the time node corresponding to the element point information and several element point information located after the time node corresponding to the element point information will be used as the context information corresponding to the element point information.

[0103] Thereafter, the information content at the time node corresponding to the element point information may be adjusted according to the context information.

[0104] Of course, the server may also push the information of the feature points whose evaluation values ​​are lower than the preset threshold to the production staff in the form of information prompts, so that the production staff can adjust the recommended information based on the information prompts.

[0105] In this specification, the information to be recommended may be advertising materials in a specified application scenario. For ease of understanding, this specification provides an optimization method for advertising materials, such as Figure 2 shown.

[0106] Figure 2 A schematic diagram of an optimization method for advertising materials provided in this specification.

[0107] Among them, after obtaining the advertising material, the server can first pre-process the advertising material, such as data cleaning and standardization, and then input the pre-processed advertising material into the information extraction model to determine the element point information therein, and then input the element point information into the evaluation model, and perform quantitative analysis on each element point information through the evaluation model to determine the quantitative analysis of each element point information, and then optimize the advertising material based on the evaluation value corresponding to each element point information.

[0108] It can be seen from the above method that in the process of recommending information to be recommended, this scheme can extract the information of each element point contained in the information to be recommended and evaluate each element point information. In this way, the information to be recommended can be optimized based on the evaluation value corresponding to each element point information, so that the information to be recommended can achieve the best recommendation effect.

[0109] For example, game company A needs to create a new advertising material, but their team is not clear about which factors may affect the effectiveness of the advertisement. By using this technology, they can input the existing advertising material into the system, and the system will automatically extract the key points of the advertising material, conduct quantitative analysis, and finally output the analysis results. Through the analysis results, the team of company A can clearly identify which elements are the key factors affecting the advertising effect, so as to optimize these key factors when creating new advertising materials and improve the advertising effect.

[0110] The above are one or more implementation information recommendation methods of this specification. Based on the same idea, this specification also provides a corresponding information recommendation device, such as Figure 3 shown.

[0111] Figure 3 A schematic diagram of an information recommendation device provided in this specification includes:

[0112] An acquisition module 301 is used to acquire information to be recommended;

[0113] An extraction module 302 is used to input the information to be recommended into a pre-trained information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model, wherein the element point information is used to represent the information content corresponding to the key time node in the information to be recommended;

[0114] Evaluation module 303, used for inputting each element point information into a pre-trained evaluation model, so as to determine the evaluation value corresponding to each element point information through the evaluation model, and for each element point information, the evaluation value corresponding to the element point information is used to represent the contribution degree of the element point information to the expected browsing situation of the information to be recommended;

[0115] The recommendation module 304 is used to adjust the information to be recommended according to the evaluation value, so as to recommend information through the adjusted information to be recommended.

[0116] Optionally, before the step of inputting the information to be recommended into the pre-trained information extraction model, the acquisition module 301 is further used to divide the information to be recommended to obtain at least one type of sub-data, the type including: text, image, audio;

[0117] The extraction module 302 is specifically configured to input the at least one type of sub-data into the information extraction model, so as to extract information of each element point contained in the information to be recommended through the information extraction model.

[0118] Optionally, the extraction module 302 is specifically used to determine, for each time node in the information to be recommended, a richness value corresponding to each type of sub-data at the time node; and determine each element point information contained in the information to be recommended based on the richness value corresponding to each type of sub-data at each time node.

[0119] Optionally, the extraction module 302 is specifically used to determine, for each time node in the information to be recommended, the criticality corresponding to the time node according to the richness value corresponding to each type of sub-data at the time node, and the preset weight corresponding to each type of sub-data; if the criticality corresponding to the time node is greater than the preset criticality, then determine the element point information according to the information content corresponding to the time node.

[0120] Optionally, before the step of inputting the information to be recommended into the pre-trained information extraction model, the acquisition module 301 is further used to process the information to be recommended to obtain a data sequence of a specified number of frames;

[0121] The extraction module 302 is specifically used to input the data sequence of the specified number of frames into the information extraction model, so as to extract the information of each element point contained in the data sequence through the information extraction model.

[0122] Optionally, before the step of inputting the information to be recommended into the pre-trained information extraction model, the acquisition module 301 is further used to determine the application scenario corresponding to the information content of the information to be recommended;

[0123] The evaluation module 303 is specifically used to input the information of each element point and the scene information of the application scene into the evaluation model, so as to determine the evaluation value corresponding to each element point information in the application scene through the evaluation model.

[0124] Optionally, the recommendation module 304 is specifically used to, for each element point information, if the evaluation value corresponding to the element point information is lower than a preset threshold, use several element point information located before the time node corresponding to the element point information and several element point information located after the time node corresponding to the element point information as context information corresponding to the element point information; and adjust the information content at the time node corresponding to the element point information according to the context information.

[0125] Optionally, the device further comprises:

[0126] The training module 305 is used to obtain historical recommendation information and determine the browsing records of each user for the historical recommendation information in history, wherein the browsing records are used to indicate the browsing and clicking of each user on the information content of each time node in the historical recommendation information; input the historical recommendation information into the information extraction model to determine the historical element point information contained in the historical recommendation information through the information extraction model, and determine the key time nodes in the historical recommendation information based on the browsing records; and train the information extraction model with the optimization goal of minimizing the deviation between the historical element point information and the information content corresponding to the key time nodes in the historical recommendation information.

[0127] Optionally, the device further comprises:

[0128] The training module 305 is used to obtain historical recommendation information and determine the browsing records of each user for the historical recommendation information in history, wherein the browsing records are used to indicate the browsing and clicking of each user on the information content of each time node in the historical recommendation information; input the historical recommendation information into a pre-trained information extraction model to extract each element point information contained in the recommendation information as historical element point information through the information extraction model; input the historical element point information into the evaluation model to determine the evaluation value corresponding to each historical element point information through the evaluation model; and train the evaluation model according to the evaluation value corresponding to each historical element point information and the browsing records.

[0129] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 An information recommendation method is provided.

[0130] This manual also provides Figure 4 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 4 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to the software implementation, this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0131] For the improvement of a technology, it can be clearly distinguished whether it is a hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0132] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0133] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0134] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0135] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0136] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0141] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0142] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0143] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0145] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0146] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. An information recommendation method, characterized in that: include: Get the information to be recommended; Inputting the information to be recommended into a pre-trained information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model, wherein the element point information is used to characterize the information content corresponding to the key time node in the information to be recommended; Inputting each element point information into a pre-trained evaluation model to determine an evaluation value corresponding to each element point information through the evaluation model, wherein for each element point information, the evaluation value corresponding to the element point information is used to represent the contribution of the element point information to the expected browsing situation of the information to be recommended; The information to be recommended is adjusted according to the evaluation value, so as to perform information recommendation through the adjusted information to be recommended.

2. The method according to claim 1, characterized in that Before the step of inputting the information to be recommended into a pre-trained information extraction model, the method further includes: Dividing the information to be recommended to obtain at least one type of sub-data, the type including: text, image, audio; The step of inputting the information to be recommended into a pre-trained information extraction model to extract information of each element point contained in the information to be recommended through the information extraction model specifically includes: The at least one type of sub-data is input into the information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model.

3. The method according to claim 2, characterized in that The step of extracting information of each element point contained in the information to be recommended by the information extraction model specifically includes: For each time node in the information to be recommended, determining a richness value corresponding to each type of sub-data at the time node; The information of each element point included in the information to be recommended is determined according to the richness value corresponding to each type of sub-data at each time node.

4. The method according to claim 3, characterized in that The step of determining each element point information included in the information to be recommended according to the richness value corresponding to each type of sub-data at each time node specifically includes: For each time node in the information to be recommended, determine the criticality corresponding to the time node according to the richness value corresponding to each type of sub-data at the time node and the preset weight corresponding to each type of sub-data; If the criticality corresponding to the time node is greater than the preset criticality, the element point information is determined according to the information content corresponding to the time node.

5. The method according to claim 1, characterized in that Before the step of inputting the information to be recommended into a pre-trained information extraction model, the method further includes: Processing the information to be recommended to obtain a data sequence of a specified number of frames; The step of inputting the information to be recommended into a pre-trained information extraction model to extract information of each element point contained in the information to be recommended through the information extraction model specifically includes: The data sequence of the designated number of frames is input into the information extraction model, so as to extract information of each element point contained in the data sequence through the information extraction model.

6. The method according to claim 1, characterized in that The step before inputting the information to be recommended into the pre-trained information extraction model also includes: Determining the application scenario corresponding to the information content of the information to be recommended; The step of inputting the information of each element point into a pre-trained evaluation model to determine the evaluation value corresponding to each element point information through the evaluation model specifically includes: The information of each element point and the scene information of the application scene are input into the evaluation model, so as to determine the evaluation value corresponding to each element point information in the application scene through the evaluation model.

7. The method according to claim 1, characterized in that The step of optimizing the information to be recommended according to the evaluation value specifically includes: For each element point information, if the evaluation value corresponding to the element point information is lower than the preset threshold, several element point information located before the time node corresponding to the element point information and several element point information located after the time node corresponding to the element point information are used as the context information corresponding to the element point information; According to the context information, the information content at the time node corresponding to the element point information is adjusted.

8. The method according to claim 1, characterized in that The step of training the information extraction model comprises: Acquire historical recommendation information, and determine each user's browsing record for the historical recommendation information in history, wherein the browsing record is used to indicate each user's browsing and clicking on the information content at each time point in the historical recommendation information; Inputting the historical recommendation information into the information extraction model, so as to determine the historical element point information contained in the historical recommendation information through the information extraction model, and determining the key time node in the historical recommendation information according to the browsing history; The information extraction model is trained with the optimization goal of minimizing the deviation between the historical element point information and the information content corresponding to the key time nodes in the historical recommendation information.

9. The method according to claim 1, characterized in that The step of training the evaluation model comprises: Acquire historical recommendation information, and determine each user's browsing record for the historical recommendation information in history, wherein the browsing record is used to indicate each user's browsing and clicking on the information content at each time point in the historical recommendation information; Inputting the historical recommendation information into a pre-trained information extraction model, so as to extract each element point information contained in the recommendation information as the historical element point information through the information extraction model; Inputting the historical element point information into the evaluation model to determine the evaluation value corresponding to each historical element point information through the evaluation model; The evaluation model is trained according to the evaluation value corresponding to each historical element point information and the browsing record.

10. An information recommendation device, characterized in that: include: An acquisition module is used to acquire information to be recommended; An extraction module, used for inputting the information to be recommended into a pre-trained information extraction model, so as to extract each element point information contained in the information to be recommended through the information extraction model, wherein the element point information is used to characterize the information content corresponding to the key time node in the information to be recommended; An evaluation module, used for inputting the information of each element point into a pre-trained evaluation model, so as to determine the evaluation value corresponding to each element point information through the evaluation model, and for each element point information, the evaluation value corresponding to the element point information is used to represent the contribution degree of the element point information to the expected browsing situation of the information to be recommended; The recommendation module is used to adjust the information to be recommended according to the evaluation value, so as to recommend information through the adjusted information to be recommended.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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