CN113962567A - Information recommendation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides an information recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to a big data technology. The specific implementation scheme comprises the following steps: selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of the user and evaluation data related to the index in response to an investment target set by the user; and outputting the target index as recommendation information. The scheme of the invention realizes the recommendation of the index of the representative asset to the user, thereby ensuring the efficiency of the user in configuring the large class of assets.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a big data technology, and in particular, to an information recommendation method and apparatus, an electronic device, a storage medium, and a computer program product.
Background
Asset Allocation (Asset Allocation) refers to Allocation of investment funds among different Asset classes according to investment requirements, and includes Allocation of funds among different large classes of assets such as stocks, fixed income securities, cash and other assets according to certain investment weights, and Allocation of funds inside different classes of assets.
After the investment is configured and implemented, the performance of the investment portfolio is evaluated based on the asset configuration efficiency, and the method is mainly used for evaluating the investment performance of the corresponding category by using a market index reference so as to assess the difference condition between the income of the investment portfolio and the corresponding market index. It is therefore important to select which indices are representative of the class of assets for which the portfolio results are optimal.
Disclosure of Invention
The disclosure provides an information recommendation method, an information recommendation device, an electronic device, a storage medium and a computer program product.
According to an aspect of the present disclosure, there is provided an information recommendation method including:
selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of a user and evaluation data related to the index in response to an investment target set by the user;
and outputting the target index as recommendation information.
According to an aspect of the present disclosure, there is provided an information recommendation apparatus including:
the index screening module is used for responding to an investment target set by a user, and selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of the user and evaluation data related to the index;
and the recommending module is used for outputting the target index as recommending information.
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 content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the information recommendation method of any of the embodiments 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 information recommendation method of any of the embodiments 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 information recommendation method of any of the embodiments of the present disclosure.
According to the technology disclosed by the invention, the asset index of the representative is recommended to the user, and the efficiency of the user for configuring the large-class assets is further ensured.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flowchart of an information recommendation method provided according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a further information recommendation method provided in accordance with an embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating a further information recommendation method according to an embodiment of the disclosure;
fig. 4 is a schematic structural diagram of an information recommendation device provided according to an embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing an information recommendation method according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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.
In the disclosed embodiments, the index is an index associated with a financial market activity, such as, for example, a stock index, a futures index, and the like. In the theoretical and practical research of asset allocation, since each kind of assets has many indexes to represent, it is meaningless to exhaust all the representatives of a kind of assets, generally accepted and commonly used indexes are used to represent a kind of assets (such as Shanghai level 300, Zhongzhen 50, etc.), when an investment manager uses a professional portfolio management system to allocate a kind of assets, the indexes that can be selected are also only common indexes provided for the system, or thousands of indexes on the market are listed according to the index categories such as A stock index, fund index, bond index, commodity index, etc. and the subdivision categories thereof, and a user can select an index that he or she thinks is suitable in the provided index list according to the object of allocation of a kind of assets. Although such professional systems cover the indexes comprehensively, during asset configuration, a user needs to select the required index from a large number of indexes, which results in higher cost for the user to view and select the index, and thus the asset configuration efficiency is low. Based on the above, an information recommendation method is proposed, which introduces an index recommendation mechanism, and after a user (e.g. an investment manager) determines an asset allocation target, recommends a certain number of target indexes (i.e. indexes representing different types of assets) to the user, so that the user selects a required index from the recommended target indexes to complete asset allocation.
Fig. 1 is a schematic flow chart of an information recommendation method according to an embodiment of the present disclosure, where the embodiment is applicable to a case where an asset representative index is actively recommended to a user after the user determines an asset configuration target. The method can be executed by an information recommendation device which is implemented in software and/or hardware and is integrated on an electronic device, for example, a server device.
Specifically, referring to fig. 1, the information recommendation method is as follows:
s101, responding to an investment target set by a user, and selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of the user and evaluation data related to the index.
In the embodiment of the disclosure, a user needs to set an investment goal when configuring assets, and then selects a corresponding index based on the investment goal, where the investment goal includes at least one of an asset type (e.g., a fixed income class, a large commodity class, etc.), an income target, and a risk target, which are input when the user configures assets. After the investment target set by the user is determined, at least one target index meeting the all-round requirements of the user can be selected from a preset index pool based on information of different dimensions so as to be recommended to the user, wherein the target index can be an asset representative index capable of representing a certain type of assets.
In an optional implementation mode, at least one index is selected from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of a user and evaluation data related to the index, wherein the attribute information of the index can be determined according to an attribute label of the index, and comprises at least one of asset classification, industry category, scale, earning rate, sharp proportion, annual fluctuation rate, winning rate performance, market popularity and product quantity tracking the index; the portrait attribute information of the user includes at least one of information of an index that the user prefers to use and an index attribute that the user focuses on; the index-related assessment data is optionally expert assessment data, including macro expert opinion data, industry expert opinion data, marketing company performance prediction opinion data. During specific implementation, the investment target can be matched with attribute information of each index in the index pool, and at least one index is selected according to a matching result; selecting at least one index which is preferred to be used by a user in a historical asset configuration stage from an index pool based on the portrait attribute information of the user; and selecting an index with positive evaluation according to expert evaluation data, such as selecting an index which is good for an expert; and then the index selected based on the three dimensional information is used as a target index.
It should be noted that the preset index pool is optionally constructed based on the credibility screened from the index data source and the index with the breadth satisfying the preset condition, wherein the index data source includes the index and the basic information thereof which are still used in the market finance field. When the preset index pool is constructed, the purpose of reducing the number of the indexes in the index pool is achieved due to the fact that part of the indexes with low quality are deleted, so that when a user wants to actively check the indexes in the index pool through the preset index pool, the user is prevented from checking some indexes with low quality, and checking cost is reduced.
And S102, outputting the target index as recommendation information.
In the embodiment of the present disclosure, after the target index (i.e., the asset representative index) is selected, all asset representative indexes may be recommended to the user as recommendation information, or a part of the asset representative indexes may be selected as recommendation information and recommended to the user, which is not specifically limited herein. In an alternative implementation mode, the asset representative index can be displayed as recommendation information according to the recommendation display parameters set by the user, so that the user can select a required index according to the display result. The recommended display parameters are used for limiting the number of the asset representative indexes displayed each time and the displayed style, and the user can dynamically adjust the recommended display parameters according to the self requirements. For example, if the user sets 10 asset representative indexes to be displayed, 10 recommendations can be selected from the determined asset representative indexes to be recommended to the user; further, if the user sets and displays 30 asset representative indexes, 30 recommendations are selected from the determined asset representative indexes to the user. It should be noted that, when selecting the asset representative index recommended to the user, the asset representative index may be randomly selected, or the asset representative indexes may be sorted in a preset sorting manner, and then selected according to the sorting result. And displaying the corresponding asset representative index according to the recommended display parameter set by the user, so that the selection effect and efficiency of the user can be greatly improved.
In the embodiment of the disclosure, the asset representative index is selected based on the information of three dimensions and recommended to the user as the recommendation information, so that the asset representative index recommended to the user can meet the all-around requirements of the user, and the asset representative index is recommended to the user, so that the user does not need to manually check and select in a large number of indexes during asset configuration, thereby ensuring the efficiency of the user in configuring the large-class assets.
Fig. 2 is a schematic flow chart of another information recommendation method according to an embodiment of the present disclosure, which is based on the above embodiment, and this embodiment optimizes a process of selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of a user, and evaluation data related to the index, where referring to fig. 2, the information recommendation method is specifically as follows:
in the embodiment of the disclosure, in response to the investment target set by the user, the target index (i.e. the asset representative index) is selected based on the information of three dimensions, and the specific selection process is shown in S201-S204. It should be noted that steps S201, S202, and S203 may be executed in parallel or sequentially, and are not limited herein.
S201, responding to an investment target set by a user, matching the investment target with attribute information of each index in a preset index pool, and giving a weight to the matched index according to a matching result.
The index attributes optionally include asset classification, industry category, scale, profitability, sharp rate, annual fluctuation rate, win rate performance, market popularity, product quantity tracking the index, and the like, wherein the market popularity and the product quantity tracking the index can measure the risk degree of the index, for example, if a certain index is more popular, the risk of the index is smaller. During specific screening, optionally, according to the type, income and risk targets of the large-class assets included in the investment targets, the indexes meeting the investment targets are selected by combining the index attributes of each index in the preset index pool, and the selected indexes are given with weights, wherein the weights can be used for representing the priority of the indexes serving as asset representative indexes, and can also represent the occupation ratio of the indexes in an investment portfolio when the indexes are selected for asset configuration. For example, if the yield set by the user is 10%, an index with a yield exceeding 10% is selected from the index pool, and different weights are given according to the yield of the selected index.
S202, based on the portrait attribute information of the user, the index which is preferentially used by the user in the historical asset configuration stage in the preset index pool is given weight.
Because the portrait attribute information of the user comprises the information of the index which is preferred to be used by the user, the index which is preferred to be used by the user can be determined from the preset index pool based on the portrait attribute information of the user, and the index which is preferred to be used by the user in the historical asset configuration stage in the preset index pool is given a weight which can represent the priority which is recommended to the user as the asset representative index. It should be noted that, after a user (e.g., an investment manager) has rich industrial experience, there are often precipitation and preference embodiments of personal experience in an adept field, so that an index preferred by the user is recommended to the user, so that the user does not need to search for a preferred index among a plurality of indexes in an index pool, that is, the search operation of the user is reduced, and further, the user experience can be improved and the subsequent asset configuration efficiency can be ensured.
And S203, giving different weights to the indexes in the preset index pool according to the evaluation data.
The evaluation data can be selected from evaluation data of an expert on the index, the evaluation data comprises lifting trend data of related indexes estimated by the expert according to policies or company performance, and optionally, the evaluation data comprises macroscopic expert viewpoint data, industry expert viewpoint data and marketing company performance prediction viewpoint data. And further giving different weights to the indexes in the preset index pool based on expert evaluation data. For example, if an expert analyzes that a certain industry is intensively developed in the future according to the national policy, the weight of the index of the industry is increased according to the evaluation data; and if the experts predict that the risks of certain companies are increased according to the performances of the companies, the weights of the related indexes of the companies are reduced.
S204, selecting at least one index as a target index according to the weight of each index in the preset index pool.
In the embodiment of the disclosure, the weight of each index in the preset index pool can represent the priority of each index recommended to a user as a target index (i.e., an asset representative index), and further, the indexes are ranked according to the weight of each index, and the asset representative index is determined according to the ranking result. For example, the weight is rearranged to the top 100 as the target index.
And S205, outputting the target index as recommendation information.
Optionally, the target index is displayed as recommendation information according to the recommendation display parameter set by the user, so that the user can select a required index according to the display result. Illustratively, if the recommendation display parameter set by the user is 10, the target index with the top 10 bits of weight ranking is displayed to the user as recommendation information.
In the embodiment of the disclosure, after the indexes selected based on the three dimensional information are respectively weighted, the target indexes can be selected quickly and accurately according to the weight values and recommended to the user, so that the user is prevented from manually checking and selecting the indexes required by the user from a plurality of indexes, and the efficiency of asset configuration is ensured.
Fig. 3 is a schematic flow chart of another information recommendation method according to an embodiment of the present disclosure, which is a detailed process for constructing a preset index pool based on the above embodiment, and referring to fig. 3, the information recommendation method is specifically as follows:
s301, obtaining the indexes under the asset types and the basic information of the indexes, and generating corresponding attribute tags according to the basic information of the indexes.
Optionally, the index and the basic information of the index under each asset type are obtained from an index data source, where the basic information of the index includes a code, a base period, a base point, a component number, a trading currency, a country, an index classification, a tracking target fund number, and the like. On the basis of the obtained basic information, if the information is missing, the basic information is supplemented and perfected, and the improved basic information is processed into an attribute label of the index, so that the attribute information of the index can be directly determined according to the attribute label of the index in the following.
In the embodiment of the present disclosure, in order to reduce the number of indexes in the preset index pool, the indexes obtained from the data source need to be screened, for example, the indexes with low quality are deleted. Optionally, the model screening may be performed by expert screening, and the specific process is shown in steps S302-S303.
S302, indexes meeting preset conditions screened out based on the attribute labels of the indexes are obtained.
In the embodiment of the present disclosure, the corresponding index may be screened by a machine based on the attribute tag of the index, or may be selected manually, which is not limited specifically herein. In order to ensure the quality of the selected index, optionally screening by an expert in combination with an attribute tag of the index, for example, selecting a public reliability and adopting an index with a breadth meeting a preset condition, wherein the preset condition is that the public reliability of the index and the adoption breadth are greater than a threshold value as an example; and if the experts can also screen corresponding indexes according to the number of the tracked target fund.
S303, calculating the evaluation index value of each index based on a preset evaluation index model, and screening indexes of which the evaluation index values meet the conditions.
In the embodiment of the disclosure, the evaluation index model may be an operational model of the evaluation index, and the evaluation index may optionally include at least one of an annual rate of return, a sharp proportion, an annual fluctuation rate, and a maximum withdrawal. And taking each index as an investment portfolio, calculating the value of the evaluation index of each index by using the evaluation index model, and further screening the indexes of which the values meet the conditions. For example, the operational formula of the annual profitability included in the annual profitability model is: the annual profitability is [ (return in investment/principal fund)/investment days ]. 365 × 100%, and the annual profitability of the index is calculated according to parameters required by the calculation model. And then selecting an index with the annual profitability greater than a threshold value. It should be noted that the calculated evaluation index value may be used as an attribute tag of the index, so that the index attribute may be determined according to the attribute tag during subsequent recommendation.
S304, constructing a preset index pool based on the screened indexes.
In the embodiment of the disclosure, the established preset index pool can be optionally listed through index categories such as the stock index A, the fund index, the bond index, the commodity index and the like and the subdivision categories thereof so as to meet the conventional experience of the user.
S305, responding to an investment target set by a user, and selecting at least one index from a preset index pool as a target index based on the attribute information of each index in the preset index pool, the portrait attribute information of the user and expert evaluation data related to the index.
And S306, outputting the target index as recommendation information.
In the embodiment of the disclosure, the obtained indexes are subjected to expert and model screening, so that the low-quality indexes can be deleted, the quality of the constructed preset index pool is further ensured, and the number of the indexes in the preset index pool is reduced. And guarantees are provided for subsequently recommending the asset representative index based on the preset index pool.
Furthermore, the preset index pool can be configured to display corresponding indexes according to index attribute search terms input by the user, and/or display corresponding indexes according to index attributes required to be displayed and configured by the user. That is, when the user views the index pool, the user can determine which attribute columns of the index are displayed through the user-defined configuration, and/or the corresponding attribute columns of the index are displayed according to the index attribute search term input by the user. Besides, the user can arrange and display the indexes in the index pool by setting a sorting condition. Therefore, compared with the method that multi-level classification is only carried out according to the types of the major assets, and the common indexes are provided under the corresponding classification, the user-defined configuration and retrieval are supported, and the user can know the latest index information according to the self requirement.
Further, according to the index attribute search terms input by the user in the historical asset configuration stage and the index attributes to be displayed configured by the user, the index attributes concerned by the user are determined, for example, the index attribute with the highest user search frequency is used as the index attribute concerned by the user. It should be noted that the index attribute concerned by the user indicates the use habit of the user in asset configuration, and the target index can be recommended according to the use habit of the user. For example, a preset number of indexes are screened out based on the index attribute concerned by the user and given with weights, and then the target index is determined by combining the weights of the indexes screened out based on other dimension information. The target index is determined by combining the use habits of the user, so that the target index recommended to the user is more in line with the user requirements, and the user experience can be ensured.
Fig. 4 is a schematic structural diagram of an information recommendation apparatus according to an embodiment of the present disclosure, which is applicable to a case where an asset representative index is actively recommended to a user after the user determines an asset configuration target. As shown in fig. 4, the apparatus specifically includes:
an index screening module 401, configured to respond to an investment target set by a user, select at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of the user, and evaluation data related to the index;
and a recommending module 402, configured to output the target index as recommendation information.
On the basis of the foregoing embodiment, optionally, the screening module includes:
the first screening unit is used for matching the investment target with the attribute information of each index in the preset index pool and giving weight to the matched index according to the matching result; and/or
The second screening unit is used for giving weight to the index which is used by the user in the preset index pool in preference at the historical asset configuration stage based on the portrait attribute information of the user; and/or
The third screening unit is used for endowing indexes in the preset index pool with different weights according to the evaluation data; and the number of the first and second groups,
and the fourth screening unit is used for selecting at least one index as a target index according to the weight of each index in the preset index pool.
On the basis of the above embodiment, optionally, the apparatus further includes:
the data acquisition and processing module is used for acquiring the indexes under all asset types and the basic information of the indexes and generating corresponding attribute tags according to the basic information of the indexes;
the first screening module is used for acquiring indexes meeting preset conditions screened out based on the attribute labels of the indexes;
the second screening module is used for calculating the evaluation index value of each index based on a preset evaluation index model and screening indexes of which the evaluation index values meet the conditions;
and the preset index pool generation module is used for constructing a preset index pool based on the screened indexes.
On the basis of the above embodiment, optionally, the preset index pool is configured to show the corresponding index according to the index attribute search term input by the user, and/or show the corresponding index according to the index attribute required to be shown and configured by the user.
On the basis of the above embodiment, optionally, the apparatus further includes:
the user concern point determining module is used for determining the index attribute concerned by the user according to the index attribute search word input by the user in the historical asset configuration stage and the index attribute required to be displayed and configured by the user;
and the recommendation screening module is used for screening out a preset number of indexes and giving weights to the indexes based on the index attributes concerned by the user.
On the basis of the foregoing embodiment, optionally, the recommending module is further configured to:
and recommending the target index to the user according to the recommendation display parameters set by the user.
On the basis of the above embodiment, optionally, the investment target includes at least one of an asset type, an income target and a risk target input by the user before asset allocation; and/or
The attribute information of the index comprises at least one item of asset classification, industry category, scale, earning rate, sharp proportion, annual fluctuation rate, victory performance, market popularity and product quantity tracking the index; and/or
The portrait attribute information of the user includes at least one of information of an index that the user prefers to use and an index attribute that the user focuses on.
The information recommendation device provided by the embodiment of the disclosure can execute the information recommendation method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the disclosure for a matter not explicitly described in this embodiment.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with 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 calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, 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 through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods described above, such as the information recommendation method. For example, in some embodiments, the information recommendation method may be implemented as a computer software program tangibly embodied in 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 the RAM 503 and executed by the computing unit 501, one or more steps of the information recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the information recommendation method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (17)
1. An information recommendation method, comprising:
selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of the user and evaluation data related to the index in response to an investment target set by the user;
and outputting the target index as recommendation information.
2. The method of claim 1, wherein selecting at least one index from a preset pool of indices as a target index based on attribute information for each index in the preset pool of indices, portrait attribute information for the user, and evaluation data related to indices comprises:
matching the investment target with attribute information of each index in the preset index pool, and giving weight to the matched index according to the matching result; and/or
Giving weight to the index which is in the preset index pool and is preferred to be used by the user in the historical asset configuration stage based on the portrait attribute information of the user; and/or
According to the evaluation data, giving different weights to the indexes in the preset index pool; and the number of the first and second groups,
and selecting at least one index as the target index according to the weight of each index in the preset index pool.
3. The method of claim 1, further comprising:
acquiring indexes under various asset types and basic information of the indexes, and generating corresponding attribute tags according to the basic information of the indexes;
acquiring indexes meeting preset conditions screened out based on the attribute labels of the indexes;
calculating the value of the evaluation index of each index based on a preset evaluation index model, and screening indexes of which the values meet the conditions;
and constructing a preset index pool based on the screened indexes.
4. The method according to claim 3, wherein the preset index pool is configured to display the corresponding index according to the index attribute search term input by the user, and/or display the corresponding index according to the index attribute required to be displayed and configured by the user.
5. The method of claim 4, further comprising:
determining the index attribute concerned by the user according to the index attribute search word input by the user in the historical asset configuration stage and the index attribute required to be displayed and configured by the user;
and screening out a preset number of indexes based on the index attribute concerned by the user and giving a weight to the indexes.
6. The method of claim 1, the outputting the target index as recommendation information, comprising:
and displaying the target index as recommendation information according to the recommendation display parameters set by the user.
7. The method of claim 1, wherein the investment goals include at least one of asset type, revenue and risk goals entered by a user when configuring an asset; and/or
The attribute information of the index comprises at least one item of asset classification, industry category, scale, earning rate, sharp proportion, annual fluctuation rate, victory performance, market popularity and product quantity tracking the index; and/or
The portrait attribute information of the user includes at least one of information of an index that the user prefers to use and an index attribute that the user focuses on.
8. An information recommendation apparatus comprising:
the index screening module is used for responding to an investment target set by a user, and selecting at least one index from a preset index pool as a target index based on attribute information of each index in the preset index pool, portrait attribute information of the user and evaluation data related to the index;
and the recommending module is used for outputting the target index as recommending information.
9. The apparatus of claim 8, wherein the index screening module comprises:
the first screening unit is used for matching the investment target with the attribute information of each index in the preset index pool and giving weight to the matched index according to the matching result; and/or the presence of a gas in the gas,
the second screening unit is used for giving weight to the index which is used by the user in the preset index pool in preference at the historical asset configuration stage based on the portrait attribute information of the user; and/or the presence of a gas in the gas,
the third screening unit is used for endowing indexes in the preset index pool with different weights according to the evaluation data; and the number of the first and second groups,
and the fourth screening unit is used for selecting at least one index as the target index according to the weight of each index in the preset index pool.
10. The apparatus of claim 8, further comprising:
the data acquisition and processing module is used for acquiring indexes under various asset types and basic information of the indexes and generating corresponding attribute tags according to the basic information of the indexes;
the first screening module is used for acquiring indexes meeting preset conditions screened out based on the attribute labels of the indexes;
the second screening module is used for calculating the evaluation index value of each index based on a preset evaluation index model and screening indexes of which the evaluation index values meet the conditions;
and the preset index pool generation module is used for constructing a preset index pool based on the screened indexes.
11. The apparatus according to claim 10, wherein the preset index pool is configured to present the corresponding index according to the index attribute search term input by the user, and/or present the corresponding index according to the index attribute required to be presented and configured by the user.
12. The apparatus of claim 11, further comprising:
the user concern point determining module is used for determining the index attribute concerned by the user according to the index attribute search word input by the user in the historical asset configuration stage and the index attribute required to be displayed and configured by the user;
and the recommendation screening module is used for screening out a preset number of indexes and giving weights to the indexes based on the index attributes concerned by the user.
13. The apparatus of claim 8, the recommendation module further to:
and displaying the target index as recommendation information according to the recommendation display parameters set by the user.
14. The apparatus of claim 8, wherein the investment goals comprise at least one of asset types, benefits, and risk goals entered by a user when configuring an asset; and/or
The attribute information of the index comprises at least one item of asset classification, industry category, scale, earning rate, sharp proportion, annual fluctuation rate, victory performance, market popularity and product quantity tracking the index; and/or
The portrait attribute information of the user includes at least one of information of an index that the user prefers to use and an index attribute that the user focuses on.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115482116A (en) * | 2022-09-30 | 2022-12-16 | 北京百度网讯科技有限公司 | Asset investment strategy information recommendation method, device, equipment and medium |
| CN117708437A (en) * | 2024-02-05 | 2024-03-15 | 四川日报网络传媒发展有限公司 | Recommendation method and device for personalized content, electronic equipment and storage medium |
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2021
- 2021-10-26 CN CN202111248499.4A patent/CN113962567A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115482116A (en) * | 2022-09-30 | 2022-12-16 | 北京百度网讯科技有限公司 | Asset investment strategy information recommendation method, device, equipment and medium |
| CN117708437A (en) * | 2024-02-05 | 2024-03-15 | 四川日报网络传媒发展有限公司 | Recommendation method and device for personalized content, electronic equipment and storage medium |
| CN117708437B (en) * | 2024-02-05 | 2024-04-16 | 四川日报网络传媒发展有限公司 | Recommendation method and device for personalized content, electronic equipment and storage medium |
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