Futures information providing method and device based on user portrait, equipment, medium and product

Abstract

The invention relates to a futures information providing method and device based on a user portrait, equipment, a medium and a product, and the method comprises the following steps: an obtaining step: obtaining behavior data of a user through an APP; an analysis step: processing the acquired behavior data; a calculation step: calculating a user portrait according to the processed behavior data; and a providing step of providing corresponding futures data for the user based on the calculated user portrait. According to the information recommendation method and device, the problems that in the prior art, recommended information is not accurate enough, and consequently information cocoon rooms appear are solved, the accuracy and real-time performance of information recommendation are improved, and more accurate decision support is provided for users.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method, device, equipment, medium, and product for providing futures information based on user portraits. Background Art

[0002] With the increasing maturity of the futures market and the continuous expansion of the investor group, its complexity and volatility have put forward higher requirements for the professional ability and information acquisition ability of investors, and the personalized demand for services by market participants has become increasingly prominent. Investors not only need to obtain timely and accurate market information, but also hope that this information can match their investment styles, risk preferences, and trading habits.

[0003] The acquisition and analysis of dynamic information in the futures market by investors not only concern the success or failure of their investment decisions, but also relate to the safety and appreciation of their assets. However, in the face of a vast amount of market information, it is often difficult for investors to quickly and accurately capture the dynamic content highly relevant to their investment strategies and preferences. Especially in the context of the information explosion era, investors need to spend a lot of time and energy to screen and sort information, which undoubtedly increases the difficulty and risk of their investment decisions.

[0004] However, there are the following defects in the existing futures information providing technologies. One is that the recommended information is not accurate enough, resulting in losses for investors. The other is that the recommended information is sometimes too single, leading to information cocoons for investors.

[0005] Therefore, it has become a technical problem urgently to be solved by those skilled in the art whether an improved method for providing futures information based on user portraits can be provided based on the deficiencies in the existing technologies. Summary of the Invention

[0006] Problems to be Solved by the Invention

[0007] The purpose of the present invention is to overcome the defects of the existing technologies and provide an improved method, device, equipment, and medium for providing futures information based on user portraits. According to the improved method, device, equipment, and medium for providing futures information based on user portraits provided by the present invention, problems such as inaccurate recommended information and information cocoons in the existing technologies are solved, and the accuracy and real-time performance of information recommendation are improved, providing more accurate decision-making support for users.

[0008] Methods for Solving the Problems

[0009] The first aspect of the present invention relates to a method for providing futures information based on user portraits, including the following steps:

[0010] An acquisition step of acquiring the behavior data of a user through an APP;

[0011] Analysis step: Process the obtained behavior data;

[0012] Calculation step: Calculate the user profile based on the processed behavior data;

[0013] Provision step: Provide corresponding futures data to the user based on the calculated user profile.

[0014] Preferably, the behavior data includes browsing records, search records, and position-holding records.

[0015] Preferably, the browsing records include records when browsing the following information: futures recommendation information, preferential recommendation information, futures market information, and futures research report information.

[0016] Preferably, the data structure of the calculated user profile is {type1:{tag1:score11,tag2:score12...}, type2:{tag1:score21,tag2:score22...}...},

[0017] where type is the major category of futures varieties;

[0018] tag is the sub-category of varieties concerned by the user under the major category;

[0019] score is the weight of each sub-category of varieties in the major category.

[0020] Preferably, the calculation method of the user profile is to calculate ∑(behavior type weight value × number of times × popularity × TF-IDF value).

[0021] Preferably, when providing corresponding futures data to the user, in addition to providing data corresponding to the user profile, other data is also provided.

[0022] The second aspect of the present invention relates to a futures information provision device based on a user profile, including:

[0023] An acquisition module, configured to acquire the user's behavior data through an APP;

[0024] An analysis module, configured to process the obtained behavior data;

[0025] A calculation module, configured to calculate the user profile based on the processed behavior data;

[0026] A provision module, configured to provide corresponding futures data to the user based on the calculated user profile.

[0027] The third aspect of the present invention relates to a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the futures information providing method according to the first aspect are implemented.

[0028] The fourth aspect of the present invention relates to a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the futures information providing method according to the first aspect are implemented.

[0029] The fifth aspect of the present invention relates to a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the futures information providing method according to the first aspect are implemented.

[0030] Effects of the Invention

[0031] According to the improved futures information providing method provided by the present invention, by accurately calculating the user portrait, the problems in the prior art such as inaccurate recommended information and the emergence of information cocoons are solved, and the accuracy and real-time performance of information recommendation are improved, providing more accurate decision-making support for users. Description of the Drawings

[0032] Figure 1 It is a flowchart of the code inference method according to the first embodiment of the present invention.

[0033] Figure 2 It is a structural diagram of the computer device according to the third embodiment of the present invention. Detailed Embodiments

[0034] Hereinafter, embodiments of the present invention will be described more fully with reference to the accompanying drawings, in which embodiments of the present invention are shown. However, the present invention can be implemented in many different forms and should not be construed as limited to the embodiments set forth herein.

[0035] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the present disclosure. As used herein, the singular forms "a", "this" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that when used herein, the term "comprising" specifies the presence of the stated features, wholes, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their groups.

[0036] Unless otherwise defined, the terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and the relevant art, and should not be interpreted in an idealized or overly formal sense unless specifically defined herein.

[0037] Hereinafter, the code inference method according to the present invention will be described in detail first.

[0038] Figure 1 It is a flowchart of the code inference method according to the first embodiment of the present invention. As Figure 1 shown, the specific process of this futures information providing method is as follows. First, a construction step (step S100) is performed to obtain the user's behavior data through the APP. Then, an analysis step (step S101) is performed to process the obtained behavior data. Then, a calculation step (step S102) is performed to calculate the user profile based on the processed behavior data. Finally, a providing step (step S103) is performed to provide the corresponding futures data to the user based on the calculated user profile.

[0039] The description of step S100 is as follows. Specifically, to construct the user profile, various behavior data of the user need to be collected first. Preferably, the behavior data includes browsing records, search records, position-holding records, etc. A data collection system is established to store the obtained user data in a suitable data storage system, such as a relational database, a NoSQL database, etc. For example, the user's behavior data is obtained through the APP. After the user performs an operation on the APP, relevant behavior events will be triggered, and the APP will report these events, and then these events will be stored in the database. Every predetermined time, for example, every 1 minute, the newly added behavior events are retrieved from the database for processing. Preferably, the browsing records include the records when browsing the following information: futures recommendation information, preferential recommendation information, futures market information, and futures research report information. Some of this information provides some preferential information for the user, and some provides analysis information and reports on the current futures market for the user.

[0040] The description of step S101 is as follows. Specifically, the collected original various behavior data of the user may have problems such as noise and missing values, and data cleaning and processing are required. Operations such as data deduplication, data normalization, and data conversion are included to ensure the quality and consistency of the data.

[0041] A description is given for step S102. Specifically, based on various processed behavioral data of users, a user profile model can be constructed. Preferably, the data structure of the user profile is {type1:{tag1:score11,tag2:score12...}, type2:{tag1:score21,tag2:score22...}...},

[0042] where type is the major category of futures;

[0043] tag is the sub-category of the variety that the user is concerned about under the major category;

[0044] score is the weight that each sub-category occupies in the major category.

[0045] type can be a natural number, for example. Each natural number represents a major category. For example: 1 is black building materials, 2 is macro and major assets, 3 is chemicals, 4 is quantitative options, 5 is energy, 6 is agricultural products, 7 is new energy and non-ferrous metals.

[0046] The weight represented by score, for a single weight, the maximum value does not exceed 3, for example.

[0047] Preferably, the following method is used to continue the calculation of the user profile.

[0048] The user profile, that is, the user preference degree = ∑(behavior type weight value × number of times × popularity × TF-IDF value).

[0049] The behavior type weight value is the assignment given to the user's behavior. For example: click = 1, favorite = 2, share = 3, position = 3, etc.

[0050] The number of times is the number of times the behavior type occurs.

[0051] The popularity decays over time according to a certain decay coefficient. The decay is calculated on a daily basis. The decay coefficient can be 0.1556, for example. Then the formula for calculating the popularity at this time is: popularity = 1×exp(-0.1556×number of days). Calculated according to this decay coefficient, the popularity decays to 0.1 after 15 days.

[0052] The TF-IDF (term frequency–inverse document frequency) value represents the information label value, which is used to evaluate the importance of a word for a document in a document set or corpus. The importance of a word increases in direct proportion to the number of times it appears in the document, but at the same time decreases in inverse proportion to the frequency of its appearance in the corpus.

[0053] When performing calculations, the maximum weight of a single tag does not exceed 3. Moreover, when the weight of a single tag is less than 0.01, it is excluded.

[0054] The description of step S103 is as follows. Specifically, according to the constructed user portrait, the information that matches the user portrait is retrieved and recommended to the user, providing the information that the user wants to view. Preferably, when providing the corresponding futures data to the user, in addition to providing the data corresponding to the user portrait, other data is also provided. For example, if the user portrait includes soybean meal and soybeans, then the relevant information recommended to this user is mainly soybean meal, supplemented by soybeans, plus a small amount of other content information. The reason for not providing all information about soybean meal and soybeans is to prevent the user from falling into an information cocoon.

[0055] The following uses an embodiment to illustrate the calculation of the user portrait in more detail.

[0056] Embodiment

[0057] First, set the initial user portrait of a user, that is, userID, to be empty.

[0058] Then, if the user conducts a holding behavior, update the holding user portrait: userID: 3 * {6: {"soybean meal": 1}} = {6: {"soybean meal": 3}}.

[0059] Next, statistically analyze the user behavior data on a daily basis. For example, the user clicks on a research report: articleID: {5: {"sulfur-containing crude oil": 0.25, "crude oil": 0.25, "daily report": 0.25, "energy": 0.25}}. Then, according to the above calculation method of ∑(behavior type weight value × number of times × popularity × TF-IDF value), the new user portrait is as follows

[0060] userID: {6: {"soybean meal": 3}, 5: {"sulfur-containing crude oil": 0.25, "crude oil": 0.25, "daily report": 0.25, "energy": 0.25}}.

[0061] After that, attenuate and merge the user portrait the next day. For example, on the second day, the user maintains the holding and clicks on a news flash: articleID: {6: {"soybeans": 1}}. Then, according to the above calculation method, the new user portrait is as follows

[0062] userID: {6: {"Soybean Meal": 3}, 5: {"Medium Sulfur Crude Oil": 0.25, "Crude Oil": 0.25, "Daily Report": 0.25, "Energy": 0.25}} * exp(-0.1556×1) + 3 * {6: {"Soybean Meal": 1}} + {6: {"Soybean": 1}} = {6: {"Soybean Meal": 3, "Soybean": 1}, 5: {"Medium Sulfur Crude Oil": 0.2, "Crude Oil": 0.2, "Daily Report": 0.2, "Energy": 0.2}}.

[0063] And so on, the user profile is updated every day.

[0064] As can be seen, according to the futures information providing method of the first embodiment of the present invention, by accurately calculating the user profile, the problems such as inaccurate information recommended by the prior art and the resulting information cocoons are solved, and the accuracy and real-time performance of information recommendation are improved, providing more accurate decision-making support for users.

[0065] The futures information providing device of the second embodiment of the present invention includes: an acquisition module for acquiring the behavior data of a user through an APP; an analysis module for processing the acquired behavior data; a calculation module for calculating the user profile according to the processed behavior data; and a providing module for providing corresponding futures data to the user based on the calculated user profile. The futures information providing device corresponds to the futures information providing method of the first embodiment, so various deformation methods in the first embodiment are also applicable to the second embodiment and will not be elaborated here.

[0066] As described above, according to the futures information providing device of the second embodiment of the present invention, by accurately calculating the user profile, the problems such as inaccurate information recommended by the prior art and the resulting information cocoons are solved, and the accuracy and real-time performance of information recommendation are improved, providing more accurate decision-making support for users.

[0067] The third embodiment of the present invention provides a computer device, and the internal structure diagram of the computer device can be as Figure 2 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to connect to external devices for data interaction with external devices. When the computer program is executed by the processor, it implements the futures information providing method related to the first embodiment of the present invention.

[0068] Those skilled in the art can understand that Figure 2 the structure shown in Figure 2 is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0069] The fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the processor executes the computer program, the futures information providing method related to the first embodiment of the present invention is implemented.

[0070] The fifth embodiment of the present invention provides a computer program product, including a computer program, and when the processor executes the computer program, the futures information providing method related to the first embodiment of the present invention is implemented.

[0071] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0072] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for illustration and not for limiting the scope of the present invention. Those skilled in the art should understand that the above embodiments can be modified or some technical features can be equivalently replaced without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for providing futures information based on user portraits, characterized in that, It includes the following steps: An acquisition step of obtaining the user's behavior data through the APP; An analysis step of processing the obtained behavior data; A calculation step of calculating the user portrait based on the processed behavior data; A provision step of providing the corresponding futures data to the user based on the calculated user portrait.

2. The futures information provision method according to claim 1, wherein the behavior data includes browsing records, search records, and position-holding records.

3. The futures information provision method according to claim 2, wherein the browsing records include the records when browsing the following information: futures recommendation information, preferential recommendation information, futures market information, and futures research report information.

4. The futures information provision method according to claim 1, wherein the data structure of the calculated user portrait is {type1:{tag1:score11,tag2:score12...}, type2:{tag1:score21,tag2:score22...}...}, where type is the major category of futures varieties; tag is the sub-category of varieties concerned by the user under the major category; score is the weight of each sub-category of varieties in the major category.

5. The futures information provision method according to claim 1, wherein the calculation method of the user portrait is to calculate ∑(behavior type weight value × number of times × popularity × TF-IDF value).

6. The futures information provision method according to claim 1, wherein when providing the corresponding futures data to the user, in addition to providing the data corresponding to the user portrait, other data is also provided.

7. A futures information providing device based on a user profile, characterized in that, It includes: An acquisition module for obtaining the user's behavior data through the APP; An analysis module for processing the obtained behavior data; A calculation module for calculating the user portrait based on the processed behavior data; A provision module for providing the corresponding futures data to the user based on the calculated user portrait.

8. A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the futures information provision method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium has a computer program stored thereon, wherein when the computer program is executed by the processor, the steps of the futures information provision method according to any one of claims 1 to 6 are implemented.

10. A computer program product includes a computer program, wherein when the computer program is executed by the processor, the steps of the futures information provision method according to any one of claims 1 to 6 are implemented.

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