CN116596638A - Information recommendation method based on numerical processing model - Google Patents
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
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Abstract
The application discloses an information recommendation method based on a numerical processing model, which relates to the technical field of data processing methods applicable to administration, business, finance and management, and is used for splitting available data to obtain a first available data item, a second available data item and a reference data item; the first available data item is a data item of which available data corresponds to a preset data attribute; the second available data item is a data item that characterizes a change made to the content of the first available data item; processing the first available data items by adopting a preset numerical processing model to obtain respective numerical processing results of each first available data item; processing the reference data items to obtain respective numerical processing results of each reference data item; the result of the digitizing process is used to characterize the differences between the contents of the data items, so that the data respectively stored in the different storage sub-modules are not very similar. Conditions are provided for subsequent services providing information presentation to the user.
Description
Technical Field
The application relates to the technical field of data processing methods applicable to administration, business, finance and management, in particular to an information recommendation method based on a numerical processing model.
Background
The rapid development of internet technology has greatly changed the work and life of people, browsing web pages, watching news, watching movies, listening to music, etc. through the internet have become an integral part of many artificial lives, and information recommendation technology is gradually rising with the rapid development of the internet in order for users to find news, movies, or music of interest more rapidly.
In an actual information recommendation scenario, the situation that different merchants need to recommend similar but not identical information to users is likely to be faced, and how to provide higher information recommendation efficiency for users while balancing benefit distribution among merchants becomes a problem to be solved.
Disclosure of Invention
The embodiment of the application provides an information recommendation method based on a numerical processing model, which aims to at least partially solve the technical problems.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides an information recommendation method based on a numeric processing model, where the method is applied to an information recommendation system based on a numeric processing model, the system includes an execution module and a storage module, the method is executed by the execution module, and the method includes:
Splitting the available data to obtain a first available data item, a second available data item and a reference data item; wherein the first available data item is a data item of which the available data corresponds to a preset data attribute; the second available data item is a data item that characterizes historically made changes to the content of the first available data item; the reference data item is a data item other than the first available data item and the second available data item in the available data; the available data is data which can be displayed to a user, and the available data comprises a plurality of data items;
processing the first available data item by adopting a preset numerical processing model to obtain a numerical processing result of the first available data item; processing the reference data item to obtain a numerical processing result of the reference data item; wherein the result of the numerical processing is used to characterize the distinction between the contents of the data items;
determining a plurality of storage sub-modules, wherein the storage sub-modules are obtained by dividing the storage modules;
storing the available data into the plurality of storage sub-modules respectively with the storage sub-modules targeting storing the available data based on the first available data item and the second available data item respectively; any two of the available data stored in the same storage sub-module meet a preset condition;
Based on the available data stored by the storage module, information recommendation is carried out; wherein the information is characterized by the available data.
In an alternative embodiment of the present specification, the preset condition is: the difference value of the comprehensive numerical processing results is larger than a preset third threshold value, the difference value of the numerical processing results of the reference data items is larger than a preset second threshold value, and the difference value of the numerical processing results of the first available data items is larger than a preset first threshold value;
the comprehensive and numerical processing result is obtained by integrating the numerical processing result of the first available data item and the numerical processing result of the reference data item.
In an alternative embodiment of the present specification, the data attribute includes at least one of: the properties of the commodity and the properties of a merchant selling the commodity. Alternatively, the data attributes include at least one of: commodity ID, price, color, material, inventory quantity, time to shelf, merchant ID, merchant location, merchant hold time, merchant business scope.
In an alternative embodiment of the present disclosure, the storage module is a storage device of unitary construction.
In an alternative embodiment of the present specification, the method further comprises:
under the condition that the time length from the last repartition of the storage sub-module at the current moment is larger than a preset time length threshold value, summing a numerical processing result of a first available data item of the first available data and a numerical processing result of the reference data item of the first available data according to first available data to obtain the comprehensive numerical processing result of the first available data; wherein the first available data is any one of the available data.
In an alternative embodiment of the present disclosure, the digitized processing results obtained through the digitized processing model have the same number of bits, and the method further includes:
under the condition that the time length from the last repartition of the storage sub-module at the current moment is not more than a preset time length threshold value, summing each bit of the numerical processing result of the first available data item of the first available data with a bit corresponding to the numerical processing result of the reference data item of the first available data to obtain the comprehensive numerical processing result of the first available data; wherein the first available data is any one of the available data.
In an alternative embodiment of the present specification, the numerical processing model is a hash model; alternatively, the numerical processing model includes a hash model and a binary conversion model in cascade.
In an alternative embodiment of the present specification, the method further comprises:
when receiving a data acquisition request of an application terminal, if the application terminal does not send a data acquisition request with the similarity larger than a preset third similarity threshold value in history, taking the data storage capacity in the plurality of storage submodules as a target area;
obtaining target data based on the result of searching the target area by the data acquisition request;
and returning the target data to the application end.
In an alternative embodiment of the present specification, the method further comprises:
when receiving a data acquisition request of an application terminal, if the application terminal historically sends a data acquisition request with the similarity to the data acquisition request being larger than a preset third similarity threshold value, taking one of the plurality of storage sub-modules as a target area;
the available data obtained by searching the target area based on the data acquisition request is used as first data;
Searching other storage sub-modules except the target area based on the first data, and taking the available data with the similarity with the first data larger than a preset first similarity threshold value as second data;
respectively matching the second available data items of the first data and the second data with user characteristics shown in the data acquisition request, and taking the data with highest matching degree as target data;
and returning the target data to the application end.
In an alternative embodiment of the present specification, the method further comprises:
if the number of the received data acquisition requests with the similarity larger than a preset second similarity threshold value is larger than the designated number in a historical time period of the designated duration from the current moment, the number of the storage sub-modules is increased as a target, and the storage modules are divided again;
raising the first threshold value and/or the second threshold value to realize threshold value updating;
and storing the available data into the repartitioned storage sub-module again based on the updated threshold value.
In an alternative embodiment of the present description, the specified number is positively correlated with a maximum storage capacity of the storage module.
In a second aspect, an embodiment of the present application further provides an information recommendation apparatus based on a numeric processing model, where the apparatus includes an execution module and a storage module, and the execution module includes:
a splitting unit for: splitting the available data to obtain a first available data item, a second available data item and a reference data item; wherein the first available data item is a data item of which the available data corresponds to a preset data attribute; the second available data item is a data item that characterizes historically made changes to the content of the first available data item; the reference data item is a data item other than the first available data item and the second available data item in the available data; the available data is data which can be displayed to a user, and the available data comprises a plurality of data items;
the digital processing unit is used for: processing the first available data item by adopting a preset numerical processing model to obtain a numerical processing result of the first available data item; processing the reference data item to obtain a numerical processing result of the reference data item; wherein the result of the numerical processing is used to characterize the distinction between the contents of the data items;
A sub-module determining unit configured to: determining a plurality of storage sub-modules, wherein the storage sub-modules are obtained by dividing the storage modules;
a distribution unit for: storing the available data into the plurality of storage sub-modules respectively with the storage sub-modules targeting storing the available data based on the first available data item and the second available data item respectively; any two of the available data stored in the same storage sub-module meet a preset condition;
a recommending unit for: based on the available data stored by the storage module, information recommendation is carried out; wherein the information is characterized by the available data.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method steps of the first aspect.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method steps of the first aspect.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects:
the method in the present specification considers both the similarity between the available data and the difference in blood relationship between the available data, so that the data stored in the different storage sub-modules are not very similar. The application relates to the technical field of data processing methods suitable for administration, business, finance and management. Conditions are provided for subsequent services providing information presentation to the user.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 is a process schematic diagram of an information recommendation method based on a numerical processing model according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The application will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning. The term "coupled" as used herein includes both direct and indirect coupling (coupling), unless otherwise indicated.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
As information technology has evolved, various information has been generated in bursts, resulting in a dramatic increase in the amount of data used to carry the information. If the two pieces of information are identical, one of the two pieces of information can be used as redundant information, and the redundant information is deleted, so that the information maintenance cost can be effectively reduced. However, in practice, the two pieces of information may be quite similar, but there is a slight difference between them in terms of data blood edges. This difference may have a large influence on the use of information.
In the scene of information recommendation, for the same commodity with the same type and the same price, both merchants a and b sell, and both merchants are provided with warehouses in the A city, and the page information provided by both merchants for the commodity is almost consistent. Merchant a is recently adjusting the price of the item down to the current price, while merchant b is always the current price. If the information of the merchant a and the merchant b for the commodity is recommended to the user, in general, the change condition of the historical price is not displayed on the commodity information display page, and the user cannot know the historical change of the commodity price, so that the two pieces of information are redundant information for the user, and the commodity selection time of the user is excessively occupied. However, if only information of one of the two merchants is displayed, it is unfair to the merchant.
The information recommendation method based on the numerical processing model in the specification is applied to an information recommendation system based on the numerical processing model, and the system comprises an execution module and a storage module, wherein the method is executed by the execution module, and each unit contained in the execution module is used for executing the method in the specification.
As shown in fig. 1, the information recommendation method based on the numerical processing model in the present specification includes the steps of:
S100: and splitting the available data to obtain a first available data item, a second available data item and a reference data item.
The technical scheme in the specification is used for a scene of data recommendation, for example, a shopping platform displays commodity information to a user, and a search engine displays search results to the user. The available data is the data that can be presented to the user. The data available in this specification may constitute text, graphics, audio, etc. presented to the user in a page. In the application scenario of the shopping platform, the available data may be merchandise data provided by a merchant to the platform, and there may be a case that two merchants sell the same merchandise, and there may be a case that the similarity of the two available data is higher, but the data of the two merchandise are different in merchant attribute.
The amount of available data acquired may be more or less, as determined by the actual situation.
The preset data attribute in the specification can be determined according to actual application requirements. The data attributes are used to characterize the merchandise or items related to the merchandise. In an alternative embodiment of the present specification, the data attributes include at least one of: the properties of the commodity and the properties of a merchant selling the commodity. The granularity of the data attributes may be divided according to actual requirements. Illustratively, the merchandise data may be further divided into: commodity ID, price, color, material, inventory quantity, time to shelf, etc. The properties of merchants can be further divided into: merchant ID, place of registration, time of establishment, business scope, etc. In practical applications, the commodity can be characterized by other attributes, which are not listed here.
The "splitting" in this step is a process of identifying each data item from available data, and in the related art, a technical means capable of achieving the technical effect is applicable to this specification under the condition of allowing. Taking a web page as an example, the contents in different text boxes in a web page respectively correspond to different text boxes, and there may be a case that the contents in a text box contain a plurality of data items.
The first available data item is a data item matched with a preset data attribute in available data.
The second available data item is then a data item for characterizing content that historically changed the content of the first available data item. For example, for a first available data item "stock quantity", a second available data item represents a change in the historical stock quantity.
The data items that cannot be divided into the first available data item or the second available data item are reference data items, for example, data items that characterize "e-commerce specialization", data items that characterize "clean warehouse end goods", data items that characterize "sales of the commodity does not account for performance of sales personnel", and the like, and data items that are defined by merchants by themselves, not by platforms. These data items, which are defined by the merchant themselves, can distinguish the goods from the subjective point of view of the merchant. Most of the existing commodity sales logics are that a certain commodity is exploded, so that a large amount of merchant heat is drawn. The merchant may obscure the boundary between its own commodity and the explosive commodity from the attribute of the data representing the commodity attribute, for example, the commodity "sparks" is explosive, some merchants can push out the heat of rubbing of "sparks", and the like, and if the commodity is not distinguished from genuine products, the user time is wasted to a certain extent. However, these products may have price and style advantages, and may be of interest to the user who wants to purchase "spark shoes", and may also have recommended value. Typically, a merchant will not sell both genuine and counterfeit products, and information inadvertently expressed from the merchant (contained in the reference data item) can be effectively distinguished.
If the data item corresponding to the first available data item and the second available data item in the available data is missing, the data item may be marked as "null".
S102: processing the first available data item by adopting a preset numerical processing model to obtain a numerical processing result of the first available data item; and processing the reference data item to obtain a numerical processing result of the reference data item.
The numerical processing model in this specification is used to numerically convert data into distinguishable values. This value is used only for distinguishing, not for measuring the content of the data. In an alternative embodiment of the present description, the numerically processed model is a hash model. If the result output by the hash model contains letters, in another alternative embodiment of the present disclosure, the numeric processing model is formed by cascading the hash model and the binary conversion model, and when processing, the hash model firstly converts the data item into a hash value, and then the binary conversion model converts the letters in the hash value into binary values. Based on the nature of the hash model, the numerical processing results obtained by different data items are different, and the data items are distinguished.
S104: a number of storage sub-modules are determined.
And dividing the storage modules to obtain a plurality of storage sub-modules.
The storage module may be a storage device of unitary construction, such as a hard disk; distributed storage devices, such as distributed storage clusters, are also possible. The storage sub-modules are logically divided, and different storage sub-modules have different storage addresses. The execution module may access the storage module from the stored data.
S106: storing the available data into the plurality of storage sub-modules respectively with the storage sub-modules targeting storing the available data based on the first available data item and the second available data item respectively; any two of the available data stored in the same storage sub-module meet a preset condition.
The method comprises the steps that the difference value of the numerical processing results of the first available data items of any two available data stored in the same storage sub-module is larger than a preset first threshold value, the difference value of the numerical processing results of the reference data items is larger than a preset second threshold value, and the available data with the difference value of the comprehensive numerical processing results larger than a preset third threshold value are stored in different storage sub-modules; the storage sub-module is caused to store the available data based on the first available data item and the second available data item.
Furthermore, in an alternative embodiment of the present specification, if the available data 1 contains 3 first available data items, 2 reference data items; whereas the available data 2 contains 1 first available data item and 1 reference data item. The difference between the two available data may be: the combination of the differences respectively contrasted with the 3 first available data items of the available data 1 and the one first available data item of the available data 2, and the combination of the differences respectively contrasted with the 2 reference data items of the available data 1 and the one reference data item of the available data 2. The difference between the numeric data item and the null data item is noted as no difference.
The result of the integrated quantization processing in the present specification is obtained by integrating the result of the quantization processing of the first available data item and the result of the quantization processing of the reference data item.
In the related art, the technical means that can be used for the similarity between numerical values such as the amount and the value can be applied to the present specification, if the conditions allow.
S108: and recommending information based on the available data stored by the storage module.
The information in this specification is characterized by the available data.
In an optional embodiment of the present disclosure, for a first available data, summing a result of the digitizing process of a first available data item of the first available data and a result of the digitizing process of a reference data item of the first available data to obtain a comprehensive result of the digitizing process of the first available data; wherein the first available data is any one of the available data. The embodiment has the characteristics of simplicity, convenience and rapidness. In the case where the time length from the last repartition of the storage sub-module is greater than the time length threshold value, it indicates that the growth speed of the available data is slower, and the embodiment may be adopted. The contents of the repartitioning will be described below. The duration threshold value may be an empirically determined value.
In another optional embodiment of the present disclosure, for a first available data, summing, for each bit of a result of the digitizing process of a first available data item of the first available data, a bit corresponding to a result of the digitizing process of a reference data item of the first available data, to obtain a comprehensive result of the digitizing process of the first available data; wherein the first available data is any one of the available data. Under the condition that the time length from the last repartitioning of the storage sub-module at the current moment is not greater than a preset time length threshold value, the method and the device indicate that the growth speed of the available data is high, and the method and the device can be adopted. This embodiment is more sensitive to subtle differences between data, although the overhead of data processing is greater.
Since the hash algorithm is more efficient in the algorithm for processing, in order to avoid errors caused by the fact that the same data item is in different data positions in the hash calculation process, the comprehensive numeric processing result is introduced in the method for avoiding the errors.
The method in the present specification considers both the similarity between the available data and the difference in blood relationship between the available data, so that the data stored in the different storage sub-modules are similar or not very similar. Conditions are provided for subsequent services providing information presentation to the user. That is, based on the system in this specification, no matter which one of the storage sub-modules is searched for, the user will not be affected with respect to the comprehensiveness of the information that he knows. In the information display process realized based on the method in the specification, users who have no special requirements on the blood edges of available data can improve the efficiency of information display to a certain extent; in addition, the method in the specification does not delete the available data which is apparent to be redundant, in the information display process, the storage submodules are properly selected to enable the information to be displayed, and for users with special requirements on the blood edges of the available data, the method in the specification can improve the matching degree of the searched information and the user requirements, and is beneficial to improving the user experience.
The system in the specification interacts with the application end, and the application end displays the available data in a mode of sending the available data to the application end. And the user can obtain the target data through watching the application end and interacting with the application end. The application end connected with the system in communication can be not the only one, and any one of the application ends is taken as an example in the specification.
The interaction process between the system and the application end in this specification will now be described.
In an optional embodiment of the present disclosure, when a data acquisition request sent by an application end is received (the data acquisition request carries a unique identifier of the application end), if the application end has historically not sent a data acquisition request with a similarity greater than a preset third similarity threshold value (may be an experience value obtained based on expert experience), which indicates that the application end intends to acquire data of a commodity for the first time, at this time, a user may not be able to acquire data related to the commodity, and its target intention may not yet be completely formed, a data storage amount in the plurality of storage submodules is maximized as a target area; the data volume of the determined target area is the largest, the data is the most abundant, and the probability of the commodity containing the target intention of the user is high. And then, based on the result of searching the target area by the data acquisition request, obtaining a first number (which can be a preset value) of available data with the maximum matching degree with the data acquisition request, namely the target data. And then, returning the target data to the application end.
The embodiment can combine the historical behaviors of the user to determine target data for the user. Because the available data in each storage sub-module is the same to a certain extent and different to a certain extent, the target data provided to the application end is matched with the intention of the user on one hand, and can meet the requirement of the user, on the other hand, the target data is fair to merchants providing the available data, each merchant has a certain probability of storing the available data provided by the merchant into the storage sub-module with the largest data volume, and each merchant has the opportunity of providing the available data provided by the merchant to the user. For the system, since it only needs to search one of several storage sub-modules (with the largest data volume), the resources consumed in providing data to the user are also minimal.
In another optional embodiment of the present disclosure, when a data acquisition request of an application side is received, if the application side historically sends a data acquisition request with a similarity to the data acquisition request greater than a preset third similarity threshold (may be a preset value based on expert experience), one of the several storage sub-modules is taken as a target area (the target area may be randomly determined). The available data obtained by searching the target area based on the data acquisition request is used as first data; searching other storage sub-modules except the target area based on the first data, and taking available data with the obtained similarity with the first data being larger than a preset first similarity threshold value (which can be a preset value based on expert experience) as second data; and respectively matching the second available data items of the first data and the second data with user characteristics (which may be user images carried in the data acquisition request) shown in the data acquisition request, wherein the user characteristics have the highest matching degree (or may be the first number with the highest matching degree) as the target data. And returning the target data to the application end.
Because the first data can correspond to the second data in format and content compared with the data acquisition request, the searching based on the first data also consumes lower calculation power during data processing, and the obtained result is more accurate. Since the user has searched for the commodity of the target historically, the user has a certain idea at the moment, the target is more definite, and the data recommendation is performed at the moment, and the comprehensiveness and the accuracy are mainly adopted.
The repartitioning process will now be described. In an alternative embodiment of the present disclosure, if the received similarity is greater than a preset second similarity threshold (may be a preset value based on expert experience) by more than a specified amount (may be a preset value based on expert experience) in a historical time period of a specified duration from the current time, this indicates that an exploded commodity appears, and the market demand for the commodity increases dramatically, so that the commodity needs to be more strictly distinguished from other commodities to meet the demands of users. The storage modules are re-partitioned with the goal of increasing the number of storage sub-modules. And improving the first threshold value and/or the second threshold value to realize threshold value updating, and storing the available data into the repartitioned storage sub-module again based on the updated threshold value.
In an alternative embodiment of the present description, the specified number is positively correlated with a maximum storage capacity of the storage module.
Further, the present disclosure also provides an information recommendation device based on a numerical processing model, where the device includes an execution module and a storage module, and the execution module includes:
a splitting unit for: splitting the available data to obtain a first available data item, a second available data item and a reference data item; wherein the first available data item is a data item of which the available data corresponds to a preset data attribute; the second available data item is a data item that characterizes historically made changes to the content of the first available data item; the reference data item is a data item other than the first available data item and the second available data item in the available data; the available data is data which can be displayed to a user, and the available data comprises a plurality of data items;
the digital processing unit is used for: processing the first available data item by adopting a preset numerical processing model to obtain a numerical processing result of the first available data item; processing the reference data item to obtain a numerical processing result of the reference data item; wherein the result of the numerical processing is used to characterize the distinction between the contents of the data items;
A sub-module determining unit configured to: determining a plurality of storage sub-modules, wherein the storage sub-modules are obtained by dividing the storage modules;
a distribution unit for: storing the available data into the plurality of storage sub-modules respectively with the storage sub-modules targeting storing the available data based on the first available data item and the second available data item respectively; any two of the available data stored in the same storage sub-module meet a preset condition;
a recommending unit for: based on the available data stored by the storage module, information recommendation is carried out; wherein the information is characterized by the available data.
The apparatus can perform the method in any of the foregoing embodiments, and can obtain the same or similar technical effects, which are not described herein.
Fig. 2 is a schematic structural view of an electronic device according to an embodiment of the present application. Referring to fig. 2, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 2, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form an information recommendation device based on the numerical processing model on a logic level. And the processor is used for executing the program stored in the memory and particularly used for executing any information recommendation method based on the numerical processing model.
The information recommendation method based on the numerical processing model disclosed in the embodiment of fig. 1 of the present application can be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may also execute an information recommendation method based on the numerical processing model in fig. 1, and implement the functions of the embodiment shown in fig. 1, which is not described herein.
The embodiment of the application also provides a computer readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device comprising a plurality of application programs, perform any of the foregoing information recommendation methods based on a numerical processing model.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. An information recommendation method based on a numerical processing model, the method being applied to an information recommendation device based on a numerical processing model including an execution module and a storage module, the method being executed by the execution module, the method comprising:
splitting the available data to obtain a first available data item, a second available data item and a reference data item; wherein the first available data item is a data item of which the available data corresponds to a preset data attribute; the second available data item is a data item that characterizes historically made changes to the content of the first available data item; the reference data item is a data item other than the first available data item and the second available data item in the available data; the available data is data which can be displayed to a user, and the available data comprises a plurality of data items;
Processing the first available data item by adopting a preset numerical processing model to obtain a numerical processing result of the first available data item; processing the reference data item to obtain a numerical processing result of the reference data item; wherein the result of the numerical processing is used to characterize the distinction between the contents of the data items;
determining a plurality of storage sub-modules, wherein the storage sub-modules are obtained by dividing the storage modules;
storing the available data into the plurality of storage sub-modules respectively with the storage sub-modules targeting storing the available data based on the first available data item and the second available data item respectively; any two of the available data stored in the same storage sub-module meet a preset condition;
based on the available data stored by the storage module, information recommendation is carried out; wherein the information is characterized by the available data.
2. The method of claim 1, wherein the data attributes comprise at least one of: commodity ID, price, color, material, inventory quantity, time to shelf, merchant ID, merchant location, merchant hold time, merchant business scope.
3. The method of claim 1, wherein the storage module is a storage device of unitary construction.
4. The method of claim 1, wherein the numerically processed model is a hash model.
5. The method of claim 1, wherein the numerically processed model comprises a concatenated hash model and binary conversion model.
6. The method of claim 1, wherein the predetermined condition is: the difference value of the comprehensive numerical processing results is larger than a preset third threshold value, the difference value of the numerical processing results of the reference data items is larger than a preset second threshold value, and the difference value of the numerical processing results of the first available data items is larger than a preset first threshold value;
the comprehensive and numerical processing result is obtained by integrating the numerical processing result of the first available data item and the numerical processing result of the reference data item.
7. An information recommendation device based on a numerical processing model, which is characterized by comprising an execution module and a storage module, wherein the execution module comprises:
a splitting unit for: splitting the available data to obtain a first available data item, a second available data item and a reference data item; wherein the first available data item is a data item of which the available data corresponds to a preset data attribute; the second available data item is a data item that characterizes historically made changes to the content of the first available data item; the reference data item is a data item other than the first available data item and the second available data item in the available data; the available data is data which can be displayed to a user, and the available data comprises a plurality of data items;
The digital processing unit is used for: processing the first available data item by adopting a preset numerical processing model to obtain a numerical processing result of the first available data item; processing the reference data item to obtain a numerical processing result of the reference data item; wherein the result of the numerical processing is used to characterize the distinction between the contents of the data items;
a sub-module determining unit configured to: determining a plurality of storage sub-modules, wherein the storage sub-modules are obtained by dividing the storage modules;
a distribution unit for: storing the available data into the plurality of storage sub-modules respectively with the storage sub-modules targeting storing the available data based on the first available data item and the second available data item respectively; any two of the available data stored in the same storage sub-module meet a preset condition;
a recommending unit for: based on the available data stored by the storage module, information recommendation is carried out; wherein the information is characterized by the available data.
8. An electronic device, comprising:
A processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 6.
9. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-6.
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