CN113327152B - Commodity recommendation method, commodity recommendation device, computer equipment and storage medium - Google Patents
Commodity recommendation method, commodity recommendation device, computer equipment and storage medium Download PDFInfo
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Abstract
The application discloses a commodity recommendation method, a commodity recommendation device, commodity recommendation equipment and commodity recommendation media, wherein the commodity recommendation method comprises the following steps: determining a freshness index, a sales index and a competition index corresponding to each commodity object in a commodity database, and carrying out weighted summation on the freshness index, the sales index and the competition index according to a preset heat calculation formula to obtain a heat index corresponding to the commodity object; and selecting a commodity recommendation list according to the heat index, wherein the commodity recommendation list comprises a plurality of selected commodity objects, and the heat index of the selected commodity objects is higher than that of unselected commodity objects. The method is comprehensive and scientific, is very suitable for carrying out heat index calculation on commodities of different electronic commerce platforms, and can efficiently and quickly carry out unified heat index evaluation on different electronic commerce independent stations; the obtained commodity recommendation list can be used for more effectively recommending hot-selling commodities to the seller user, so that the commodity selecting time is saved, wrong selection and missing selection of the seller user are avoided, and the commodity selecting efficiency of merchants is greatly improved.
Description
Technical Field
The embodiment of the application relates to the technical field of Internet, in particular to a commodity recommendation method, a commodity recommendation device, computer equipment and a storage medium.
Background
With the development of the cross-border e-commerce field in recent years, more and more new merchants join the e-commerce industry, and the merchants often need to spend a long time selecting products to determine the products to be sold, so a method for recommending hot-selling products to the merchants is needed.
Meanwhile, because the cross-border electronic commerce is different from the traditional electronic commerce, the information of the commodities comes from each independent station and the electronic commerce platform, the information is difficult to unify, and the difficulty exists in how to choose a relatively comprehensive standard to evaluate the commodity heat. The prior item selecting method recommends the goods to the merchant based on the ranking of the item list, so that the dimension reflecting the heat of the goods is single.
Disclosure of Invention
The embodiment of the invention provides a commodity recommendation method, a commodity recommendation device, computer equipment and a storage medium.
In order to solve the technical problems, the embodiment of the invention adopts the following technical scheme: provided is a commodity recommendation method, comprising:
determining a freshness index corresponding to each commodity object in a commodity database, wherein the freshness index is obtained by counting inflection point time of long tail distribution of the commodity database according to commodity shelf time and carrying out time attenuation calculation on the commodity shelf time of the commodity object by combining Newton's cooling law and the inflection point time;
Determining sales indexes of all commodity objects in a commodity database, wherein the sales indexes are obtained by weighting calculation according to a plurality of evaluation values reflecting historic or potential sales capacities of the commodity objects;
determining competition indexes corresponding to all commodity objects in a commodity database, wherein the competition indexes are calculated according to ranking change data of the commodity objects in ranking lists corresponding to the classes of the electronic commerce platform to which the commodity objects belong;
for each commodity object, weighting and summing the corresponding freshness index, sales index and competition index according to a preset heat calculation formula to obtain a heat index corresponding to the commodity object;
and selecting a commodity recommendation list according to the heat index of each commodity object, wherein the commodity recommendation list comprises a plurality of selected commodity objects, and the heat index of each selected commodity object is higher than that of each unselected commodity object.
Optionally, the step of determining the freshness index corresponding to each commodity object in the commodity database includes:
acquiring commodity shelving time recorded by first shelving of all commodity objects in a commodity database, classifying and summarizing the commodity objects according to the commodity shelving time, and determining the number of the commodity objects on shelves in each time period;
Determining inflection point time of long-tail distribution according to the long-tail distribution statistical characteristics presented by the number of commodity objects put on the shelf in each time period;
and carrying out time attenuation calculation on the commodity shelf time of each commodity object in the commodity database by applying Newton's cooling law and inflection point time, and taking the calculation result as a corresponding freshness index of each commodity object.
Optionally, the step of determining the sales index of each commodity object in the commodity database includes:
determining any two or more indexes of each commodity object in the commodity database: the method comprises the following steps of scoring advertisement data, scoring list ranking, scoring total distribution network points, scoring evaluation data and scoring sales data, wherein the scores of all indexes are unified into the same total score standard;
and weighting and summarizing the determined multiple indexes for each commodity object to obtain the sales index of the commodity object.
Optionally, the step of determining the competition index corresponding to each commodity object in the commodity database includes:
acquiring specific ranking list data of a plurality of time nodes in a certain historical time corresponding to each commodity object;
for each commodity object, calculating the comprehensive change rate of the commodity object according to the plurality of time nodes, wherein the comprehensive change rate is the ratio between the difference value of the score of the specific ranking list obtained by the commodity object at the last time node and the score of the specific ranking list obtained by the commodity object at the earliest time node and the sum of the scores of the specific ranking lists obtained by the commodity object at all time nodes;
For each commodity object, the ratio of the difference between the comprehensive change rate and the minimum comprehensive change rate obtained in all commodity objects to the difference between the maximum comprehensive change rate and the minimum comprehensive change rate in all commodity objects is used as the competition index.
Optionally, for each commodity object, weighting and summing the corresponding freshness index, sales index and competition index which are unified to the same total score standard according to a preset heat calculation formula, and in the step of obtaining the heat index corresponding to the commodity object, the freshness index, sales index and competition index are unified to the same total score standard for metering.
Optionally, the commodity recommendation method comprises the following post steps:
and recommending the commodity recommendation list to seller users registered by each independent transaction site maintained by the electronic commerce platform.
Optionally, the commodity recommendation method comprises the following pre-steps:
the commodity database is preferably selected to only comprise commodity objects of which the historical transaction behavior information only comprises specific physical positions.
In order to solve the above technical problem, an embodiment of the present invention further provides a commodity recommendation device, including:
The freshness index module is used for determining freshness indexes corresponding to all commodity objects in the commodity database, and the freshness indexes are obtained by counting inflection point time of long tail distribution of the commodity database according to commodity shelf time and carrying out time attenuation calculation on the commodity shelf time of the commodity objects by combining Newton's cooling law and the inflection point time;
the sales index module is used for determining sales indexes of all commodity objects in the commodity database, and the sales indexes are obtained by weighting calculation according to a plurality of evaluation scores reflecting historic or potential sales capacities of the commodity objects;
the competition index module is used for determining competition indexes corresponding to all commodity objects in the commodity database, and the competition indexes are obtained by calculating according to ranking change data of the commodity objects in ranking lists corresponding to the classes of the electronic commerce platforms to which the commodity objects belong;
the weighting calculation module is used for carrying out weighting summation on the corresponding freshness index, sales index and competition index according to a preset heat calculation formula aiming at each commodity object to obtain a heat index corresponding to the commodity object;
and the commodity recommendation module is used for selecting a commodity recommendation list according to the heat index of each commodity object, wherein the commodity recommendation list comprises a plurality of selected commodity objects, and the heat index of each selected commodity object is higher than that of each unselected commodity object.
In order to solve the above technical problem, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the processor is caused to execute the steps of the commodity recommendation method.
To solve the above technical problem, an embodiment of the present invention further provides a storage medium storing computer readable instructions, where the computer readable instructions when executed by one or more processors cause the one or more processors to execute the steps of the commodity recommendation method described above.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
1. according to the method, a data source and a statistical mode are optimized, a new index, a sales index and a competition index corresponding to each commodity object in a commodity database are determined, then weighted summation is carried out according to a preset heat calculation formula, and finally an index reflecting commodity heat is obtained. Compared with the existing method for solving the commodity heat based on single dimension of the ranking list, the method for solving the commodity heat index is more comprehensive and scientific, and heat ranking data of trade commodities can be screened accordingly.
2. The numerical value obtained by the method conforms to the unified heat evaluation standard, is not influenced by the evaluation standard of different independent transaction sites in different electronic commerce platforms and cross-border electronic commerce platforms, is quite suitable for carrying out unified heat index calculation on commodities of different electronic commerce platforms and different independent transaction sites, and can be particularly used for carrying out unified heat index evaluation on different electronic commerce independent sites efficiently and rapidly.
3. Due to the fact that the commodity recommendation list obtained by the method has the beneficial effects that more effective hot-selling commodity recommendation can be conducted on the seller users registered by each independent transaction site maintained by the electronic commerce platform, so that the commodity selection time is saved, wrong selection and missing selection of the seller users are avoided, and the commodity selection efficiency of merchants is greatly improved.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a typical network deployment architecture relevant to implementing the technical solutions of the present application;
FIG. 2 is a basic flow chart of a commodity object recommending method according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating steps performed in the step S100 of FIG. 2;
FIG. 4 is a flowchart illustrating steps performed in the step S200 of FIG. 2;
FIG. 5 is a flowchart illustrating steps performed in step S300 of FIG. 2;
FIG. 6 is a basic flow chart of a method for recommending commodity objects according to another embodiment of the present application;
FIG. 7 is a schematic flow chart of a method for recommending commodity objects according to another embodiment of the present application;
FIG. 8 is a graphical user interface diagram of a method for recommending items according to one embodiment of the present application;
FIG. 9 is a schematic diagram of a basic structure of a commodity recommendation device according to an embodiment of the present application;
fig. 10 is a basic structural block diagram of a computer device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for the purpose of illustrating the present application and are not to be construed as limiting the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As will be appreciated by those skilled in the art, a "terminal" as used herein includes both devices of a wireless signal receiver that have only wireless signal receivers without transmitting capabilities and devices of receiving and transmitting hardware that have devices capable of performing two-way communications over a two-way communications link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; a PCS (Personal Communications Service, personal communication system) that may combine voice, data processing, facsimile and/or data communication capabilities; a PDA (Personal Digital Assistant ) that can include a radio frequency receiver, pager, internet/intranet access, web browser, notepad, calendar and/or GPS (Global Positioning System ) receiver; a conventional laptop and/or palmtop computer or other appliance that has and/or includes a radio frequency receiver. As used herein, a "terminal" may be portable, transportable, installed in a vehicle (aeronautical, maritime, and/or land-based), or adapted and/or configured to operate locally and/or in a distributed fashion, to operate at any other location(s) on earth and/or in space. The "terminal" used herein may also be a communication terminal, a network access terminal, a music/video playing terminal, for example, a PDA, a MID (Mobile Internet Device ) and/or a mobile phone with music/video playing function, and may also be a smart tv, a set-top box, etc.
The hardware referred to by the names "server", "client", "service node" and the like in the present application is essentially an electronic device having the performance of a personal computer, and is a hardware device having necessary components disclosed by von neumann's principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device, and a computer program is stored in the memory, and the central processing unit calls the program stored in the external memory to run in the memory, executes instructions in the program, and interacts with the input/output device, thereby completing a specific function.
It should be noted that the concept of "server" as referred to in this application is equally applicable to the case of a server farm. The servers should be logically partitioned, physically separate from each other but interface-callable, or integrated into a physical computer or group of computers, according to network deployment principles understood by those skilled in the art. Those skilled in the art will appreciate this variation and should not be construed as limiting the implementation of the network deployment approach of the present application. Referring to fig. 1, the hardware base required for implementing the related technical solution of the present application may be deployed according to the architecture shown in the figure. The server 80 is deployed at the cloud as a service server, and may be responsible for further connecting to related data servers and other servers providing related support, so as to form a logically related service cluster, to provide services for related terminal devices, such as a smart phone 81 and a personal computer 82 shown in the figure, or a third party server (not shown). The smart phone and the personal computer can access the internet through a well-known network access mode, and establish a data communication link with the cloud server 80 so as to run a terminal application program related to the service provided by the server.
For the server, the application program is generally constructed as a service process, and a corresponding program interface is opened for remote call of the application program running on various terminal devices.
The application program refers to an application program running on a server or terminal equipment, the application program adopts a programming mode to realize the related technical scheme of the application, the program codes of the application program can be stored in a nonvolatile storage medium which can be identified by a computer in the form of computer executable instructions, and the program codes are called by a central processing unit to run in a memory, and the related device of the application is constructed by the running of the application program on the computer.
For the server, the application program is generally constructed as a service process, and a corresponding program interface is opened for remote call of the application program running on various terminal devices.
Referring to fig. 2, fig. 2 is a basic flow chart of a commodity object recommending method according to the present embodiment.
As shown in fig. 2, a commodity object recommending method disclosed in the present application includes steps S100 to S500, which are specifically as follows:
step 100, determining a freshness index corresponding to each commodity object in a commodity database, wherein the freshness index is obtained by counting inflection point time of long tail distribution of the commodity database according to commodity shelf time and carrying out time attenuation calculation on the commodity shelf time of the commodity object by combining Newton' S cooling law and the inflection point time;
in this embodiment, the commodity database is preferably a relational database, and any one of Oracle, sybase, INFORMIX and INGRES is selected to perform the operation of the method, where the corresponding information of each commodity object included in the commodity database is obtained by performing classification and grabbing according to a corresponding classification system, such as household appliances, clothing, etc., pre-established on an e-commerce platform. The corresponding information of each commodity object comprises commodity names, commodity shelf time, commodity sales quantity and the like. The commodity shelf time is the first shelf time of any commodity.
Each commodity object exists in a plurality of independent commodity transaction stations, which are called independent stations. The independent stations are independent transaction stations supported by cross-border e-commerce platforms known by those skilled in the art, and because the corresponding sales quantity information of commodity objects on each independent station is different, for example, the sales quantity of the commodity with the name A in the X independent station is ranked 1 and the sales quantity in the Y independent station is ranked 4, the judgment of the hot commodity objects and the commodity recommendation become a problem to be solved urgently, and the improvement point of the method is compared with the prior art.
The newton's law of cooling and the acquisition of inflection point times and corresponding calculation methods are disclosed below:
referring to fig. 3, fig. 3 is a step of obtaining a freshness index corresponding to each commodity object in the commodity database, which specifically includes:
step S110: and acquiring commodity shelving time recorded by first shelving of all commodity objects in a commodity database, classifying and summarizing the commodity objects according to the commodity shelving time, and determining the number of the commodity objects on shelves in each time period.
The time to shelf can be set by those skilled in the art to an accuracy, for example to date or time minutes and seconds, depending on the actual requirements. Similarly, the respective time periods may be set to a certain number of days or a certain number of hours of a day, respectively.
Step S120: determining inflection point time of long-tail distribution according to the long-tail distribution statistical characteristics presented by the number of commodity objects put on the shelf in each time period;
the long tail distribution statistics are the preferred statistical model known to those skilled in the art, and those skilled in the art can also calculate from other statistical models: by using statistical knowledge, the total amount of the commodity objects on shelves is counted year by year or month by taking the first shelf time of the commodity objects as an index item, so as to form a change curve or a bar graph, and accordingly, the inflection point time in the whole curve can be calculated according to the long tail distribution principle.
Step S130: and carrying out time attenuation calculation on the commodity shelf time of each commodity object in the commodity database by applying Newton's cooling law and inflection point time, and taking the calculation result as a corresponding freshness index of each commodity object.
The newton law of cooling is:
T^′＝-α(T-H)
in the present embodiment, T (T) is a function of time (T) of heat (T) of any commodity object. The rate of change of heat (cooling), i.e. decay rate, is the derivative T' (T) of the heat function. H represents the average heat of the commodity object, and T (T) -H is the difference between the current heat and the average heat. This is a positive value since the current heat is higher than the average heat. The constant α (α > 0) represents the proportional relationship between the average heat and the rate of decrease in temperature. The preceding negative sign indicates cooling. The alpha can be adjusted by a person skilled in the art according to specific data distribution, so that the attenuation rate meets the service requirement. An exemplary formula for its integral form in combination with newton's law of cooling is as follows:
wherein:
saleindex represents the freshness index;
g represents an attenuation coefficient, and a person skilled in the art can adjust parameters according to specific data distribution so that the attenuation rate meets the service requirement;
diff_days represents the difference between the time of the goods being put on and the current day;
Wherein 7 is a period of self-definition, which can be set by a person skilled in the art according to actual requirements.
That is, the freshness index decays according to a preset decay factor over a period of 7 days.
The method can quickly obtain the corresponding freshness index of each commodity object in the commodity database for calculation in the subsequent step of the application.
Step 200, determining sales indexes of all commodity objects in a commodity database, wherein the sales indexes are obtained by weighting calculation according to a plurality of evaluation scores reflecting historic or potential sales capacities of the commodity objects.
In one embodiment, referring to fig. 3, the specific obtaining step of the sales index corresponding to each commodity object in the commodity database includes:
step S210, determining any two or more indexes of the commodity objects in the commodity database: the method comprises the following steps of scoring advertisement data, scoring list ranking, scoring total distribution network points, scoring evaluation data and scoring sales data, wherein the scores of all indexes are unified into the same total score standard;
in some embodiments, the advertisement data score is obtained according to the change condition of the commodity object in the last several time periods of the advertisement quantity and the position information, for example, the change condition of the commodity advertisement in different time periods is obtained in the last 3 days, the last 7 days and the last 14 days, and then the commodity advertisement data score is added by multiplying different weight values, wherein the weight values can be adjusted according to actual requirements, so that the commodity object advertisement data score is obtained. An exemplary formula is as follows:
Wherein: ad_score represents the ad data score;
min (ad_hot) represents the minimum advertisement weight;
max (ad_hot) represents the maximum advertisement weight;
ad_hot represents the advertisement weight, and its calculation formula is as follows:
ad_hot＝sum(index_weight×time_weight)
wherein:
index_weight represents advertisement position weight, for example, when advertisement position is located in front of advertisement list, weight is 4, and then is 3;
time_weight represents advertisement time weight, for example, when advertisement put on shelf time is 2 for 3 days, 3 for 7 days, and 4 for 14 days;
that is, the above formula shows that the advertisement data score of the commodity object is calculated by dividing the difference between the advertisement weight of the commodity object minus the minimum advertisement weight of all commodity objects by the difference between the maximum advertisement weight of all commodity objects and the minimum advertisement weight of all commodity objects, and multiplying the ratio by 100.
In order to prevent the denominator from being meaningless, 1 is added, and the ratio is multiplied by 100, so that the scoring result is set within 100, so that the numerical value can be intuitively obtained, the subsequent calculation is convenient, the functions of adding 1 to the denominator and multiplying 100 to the ratio are the same in the formula disclosed below, and the description is omitted hereinafter.
The advertisement data scoring comprehensively reflects the browsed condition of the commodity object in a certain time period recently, and also reflects the potential sales capacity of the commodity object to a certain extent.
In some embodiments, the ranking score of the list is based on a ranking value and a score of a list of a classification system established in advance on an e-commerce platform where the commodity object is located, for example, a television of a certain model of a certain brand ranks third in a hot ranking list of a current household appliance of the e-commerce platform, ranks second in a current household appliance of the e-commerce platform, and the like, and the ranking score of the current household appliance of the e-commerce platform can be refined to each spu specifically, and the ranking score of the commodity is obtained by comprehensive calculation. An exemplary formula is as follows:
wherein: rank score represents a ranking score of the list;
rank represents the ranking of the list of commodities;
min (rank) represents the minimum list ranking score;
max (rank) represents the maximum list ranking score.
That is, the ranking score is obtained by subtracting the smallest ranking of all commodity objects from the ranking of the commodity object, dividing the difference between the largest ranking of all commodity objects and the smallest ranking of all commodity objects, and multiplying the ratio by 100.
The ranking score of the list comprehensively reflects the relative popularity of the commodity object in the commodity object of the same class, and the historical sales capacity of the commodity object is reflected.
In some embodiments, the total distribution point score is calculated comprehensively according to the number of shops in the category of the classification system pre-established on the electronic commerce platform where the commodity object is located, so as to obtain the total distribution point score of the commodity object, and an exemplary formula is as follows:
Wherein: the shop_fire represents the total amount score of the distributed network points;
shop_num represents the number of sales shops;
min (shop_num) represents the minimum number of sales shops;
max (shop_num) represents the maximum number of shops sold.
That is, the distribution point total score is obtained by dividing the difference between the number of sales shops of the commodity object minus the minimum number of sales shops of all commodity objects by the difference between the maximum number of sales shops of all commodity objects and the minimum number of sales shops of all commodity objects, and multiplying the ratio by 100.
It will be appreciated that the distribution point total score comprehensively reflects the potential sales potential of the commodity object.
In some embodiments, the evaluation data score is comprehensively calculated according to evaluation and scoring information on an e-commerce platform where the commodity object is located, and an exemplary formula is as follows:
wherein: comment_fire represents the rating data score;
score represents the total score of the good;
min (score) represents the minimum commodity total score;
max (score) represents the maximum total commodity score.
That is, the evaluation data score is obtained by dividing the difference between the commodity total score of the commodity object minus the minimum commodity total score in all commodity objects by the difference between the maximum commodity total score in all commodity objects and the minimum commodity total score in all commodity objects, and multiplying the ratio by 100.
The evaluation data score comprehensively reflects the popularity of the commodity object and reflects the historical sales capacity of the commodity object.
In some embodiments, the sales data score is comprehensively calculated according to the sales score on the e-commerce platform where the commodity object is located, and an exemplary formula is as follows:
wherein: sal_bore represents sales data scoring;
the calculation formula of sal_info is as follows:
sal_info=c history sales + d increase in sales by one month
Where c and d represent weight values for historical sales and the last month sales increments, respectively. That is, the sales data score is obtained by subtracting the difference between the minimum value of the historical sales calculated by a certain parameter and the increase in the current month from the sum of the historical sales calculated by a certain parameter and the increase in the current month for all the commodity objects, dividing the difference between the maximum value of the historical sales calculated by a certain parameter and the increase in the current month for all the commodity objects, and multiplying the ratio by 100. The sales volume data scoring comprehensively reflects the comprehensive condition of the historical sales volume of the commodity object, and can intuitively reflect the historical sales capacity of the commodity object.
And S220, weighting and summarizing the determined multiple indexes for each commodity object to obtain the sales index of the commodity object. The calculation formula is exemplified as follows:
Sales index = α_score+β_rank_core+γ_shop_core+δ_comment_core+epsilon_sample_core
Where α, β, γ, δ, ε all represent weights of different scores, and one skilled in the art can adjust this parameter according to the actual business.
The sales index corresponding to each commodity object in the commodity database can be obtained scientifically and intuitively according to a unified evaluation standard through the method.
Step S300, determining competition indexes corresponding to all commodity objects in a commodity database, wherein the competition indexes are calculated according to ranking change data of the commodity objects in ranking lists corresponding to the classes of the electronic commerce platforms to which the commodity objects belong.
In one embodiment, the specific step of obtaining the competition index corresponding to each commodity object in the commodity database is shown in fig. 4, and includes:
step S310, acquiring specific ranking list data of a plurality of time nodes in a certain historical time corresponding to each commodity object;
in this embodiment, a plurality of time nodes in a certain historical time may be obtained according to practical situations, for example, 12 pm to 2 pm and 7 pm to 10 pm in three days before a certain day.
Step S320, calculating the comprehensive change rate of each commodity object according to the plurality of time nodes, wherein the comprehensive change rate is the ratio between the difference value of the score of the specific ranking list obtained by the commodity object at the last time node and the score of the specific ranking list obtained by the commodity object at the earliest time node and the sum of the scores of the specific ranking lists obtained by the commodity object at all time nodes;
In this embodiment, the specific ranking list is derived from a self-ranking list in the electronic commerce platform, and is specifically formed by mapping external website information such as amazon bsr or google tree to internal commodities of the electronic commerce. The time node can be set to be accurate to a time minute second or a certain date according to actual requirements.
The general change rate calculation formula of the commodity object is exemplified as follows:
rank list represents a near three-leaderboard variation score, an exemplary formula of which is as follows:
wherein:
rank_1t_spec represents the list score of the day before the current date;
rank_2t_spec represents the two-day-old list score for the current date;
rank_3t_spec represents the current date three days ago list score.
Step S330, regarding each commodity object, the ratio of the difference between the comprehensive change rate and the minimum comprehensive change rate obtained in all commodity objects to the difference between the maximum comprehensive change rate and the minimum comprehensive change rate in all commodity objects is used as the competition index. An exemplary formula is as follows:
wherein: combet_score represents the integrated rate of change;
min (rank_list) represents the score of the smallest particular ranking list;
max (rank list) represents the score of the largest particular ranking list.
That is, the overall change rate is obtained by subtracting the score of the smallest specific ranking list in all commodity objects from the commodity total score of the commodity object, dividing the difference by the difference between the score of the largest specific ranking list in all commodity objects and the score of the smallest specific ranking list in all commodity objects, and multiplying the ratio by 100.
And step 400, for each commodity object, weighting and summing the corresponding freshness index, sales index and competition index according to a preset heat calculation formula to obtain the heat index corresponding to the commodity object.
The calculation formula is exemplified as follows:
heat index = α sales index + β competition index + γ freshness index
Where α, β, γ represent weights of different scores, and a person skilled in the art may adjust the parameters according to the actual traffic, or even give the relevant users their own adjustments, and increase the weights of the different aspects of interest accordingly.
The heat index calculated by the formula can form a relatively uniform and comprehensive standard for commodity objects of a plurality of commodity information sources of different independent stations.
And S500, selecting a commodity recommendation list according to the heat index of each commodity object, wherein the commodity recommendation list comprises a plurality of selected commodity objects, and the heat index of each selected commodity object is higher than that of each unselected commodity object.
In this embodiment, the relevant commodity object information included in the commodity pushing list selected according to the heat index of each commodity object may be set by a person skilled in the art according to actual requirements, for example, ID name of the commodity object, picture of the commodity object, price of the commodity object, etc., and the display effect of the display page on the user device is shown in fig. 8.
In this embodiment, a person skilled in the art may sort the commodity objects according to the actual demands, and set the class of the commodity recommendation list and the corresponding number of each class, for example, the commodity recommendation list includes the first 50 home appliances with the highest heat.
The sales index, the competition index and the freshness index of each commodity object are weighted and summed according to a certain formula, and the heat index is obtained according to a unified evaluation standard, so that the method is more scientific and comprehensive compared with a method for counting by ranking only a list as a single dimension; in addition, the commodity objects with a plurality of commodity information sources at different independent stations can be evaluated by using a relatively uniform and comprehensive standard, so that even different e-commerce platforms with different commodity sales conditions, commodity heat conditions and the like can obtain effective commodity heat ranking, and the method has wide applicability; due to the fact that the commodity recommendation list obtained by the method has the beneficial effects that more effective hot-selling commodity recommendation can be conducted on the seller users registered by each independent transaction site maintained by the electronic commerce platform, so that the commodity selection time is saved, wrong selection and missing selection of the seller users are avoided, and the commodity selection efficiency of merchants is greatly improved.
Referring to fig. 6, in some embodiments, in addition to the steps S100 to S500, the method for recommending the applied commodity further includes a step S600 of recommending the commodity recommendation list to the seller users registered at each independent transaction site maintained by the e-commerce platform.
Referring to FIG. 7, in some embodiments, step S010 filters the merchandise database such that the merchandise object therein has historical transaction behavior information including location information pointing to a specific physical location.
By carrying out historical transaction behavior information on the commodity database in advance and only containing the optimization of a specific physical position, the commodity recommendation list obtained by the commodity recommendation method can be more aimed at the commodity heat of a specific physical position, and the commodity recommendation method is beneficial for seller users to carry out commodity sales in a specific area.
Further, by performing functionalization on each embodiment of the above-mentioned commodity object recommending method, a commodity recommending apparatus of the present application may be constructed, and according to this concept, please refer to fig. 9, in an exemplary embodiment, the apparatus includes:
the freshness index module 11 is used for determining freshness indexes corresponding to all commodity objects in the commodity database, wherein the freshness indexes are obtained by counting inflection point time of long tail distribution of the commodity database according to commodity shelf time and carrying out time attenuation calculation on the commodity shelf time of the commodity objects by combining Newton's cooling law and the inflection point time;
A sales index module 12 for determining sales indexes of the respective commodity objects in the commodity database, the sales indexes being calculated by weighting a plurality of evaluation values reflecting historic or potential sales capacities of the commodity objects;
the competition index module 13 is configured to determine a competition index corresponding to each commodity object in the commodity database, where the competition index is calculated according to ranking change data of the commodity object in a ranking list corresponding to a class of an electronic commerce platform to which the commodity object belongs;
the weighting calculation module 14 is configured to, for each commodity object, perform weighted summation on the corresponding freshness index, sales index, and competition index according to a preset heat calculation formula, and obtain a heat index corresponding to the commodity object;
the commodity recommendation module 15 is configured to select a commodity recommendation list according to a heat index of each commodity object, where the commodity recommendation list includes a plurality of selected commodity objects, and the heat index of the selected commodity objects is higher than that of unselected commodity objects.
In order to solve the technical problems, the embodiment of the invention also provides computer equipment. Comprises a central processing unit and a memory, wherein the central processing unit is used for calling and running a computer program stored in the memory to execute the steps of the commodity recommendation method. Referring specifically to fig. 10, fig. 10 is a basic structural block diagram of a computer device according to the present embodiment.
As shown in fig. 10, the internal structure of the computer device is schematically shown. The computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected by a system bus. The nonvolatile storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store a control information sequence, and the computer readable instructions can enable the processor to realize a commodity object recommending method when the computer readable instructions are executed by the processor. The processor of the computer device is used to provide computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, cause the processor to perform a merchandise object recommendation method. The network interface of the computer device is for communicating with a terminal connection. It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In this embodiment, the processor is configured to execute specific functions of the freshness index module 11, the sales index module 12, the competition index module 13, the weight calculation module 14 and the commodity recommendation module 15 in the figure, and the memory stores program codes and various data required for executing the above modules. The network interface is used for data transmission between the user terminal or the server. The memory in the present embodiment stores program codes and data required for executing all the sub-modules in the commodity object recommending apparatus, and the server can call the program codes and data of the server to execute the functions of all the sub-modules.
The present invention also provides a storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of any of the article object recommendation methods of the embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored in a computer-readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
Those of skill in the art will appreciate that the various operations, methods, steps in the flow, actions, schemes, and alternatives discussed in the present application may be alternated, altered, combined, or eliminated. Further, other steps, means, or steps in a process having various operations, methods, or procedures discussed in this application may be alternated, altered, rearranged, split, combined, or eliminated. Further, steps, measures, schemes in the prior art with various operations, methods, flows disclosed in the present application may also be alternated, altered, rearranged, decomposed, combined, or deleted.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for a person skilled in the art, several improvements and modifications can be made without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (8)
1. The commodity recommending method is characterized by comprising the following steps of:
determining a freshness index corresponding to each commodity object in a commodity database, wherein the freshness index is obtained by counting inflection point time of long tail distribution of the commodity database according to commodity shelf time and carrying out time attenuation calculation on the commodity shelf time of the commodity object by combining Newton's cooling law and the inflection point time;
Determining sales indexes of all commodity objects in a commodity database, wherein the sales indexes are obtained by weighting calculation according to a plurality of evaluation values reflecting historic or potential sales capacities of the commodity objects;
determining competition indexes corresponding to all commodity objects in a commodity database, wherein the competition indexes are calculated according to ranking change data of the commodity objects in ranking lists corresponding to the classes of the electronic commerce platform to which the commodity objects belong;
for each commodity object, weighting and summing the corresponding freshness index, sales index and competition index according to a preset heat calculation formula to obtain a heat index corresponding to the commodity object;
selecting a commodity recommendation list according to the heat index of each commodity object, wherein the commodity recommendation list comprises a plurality of selected commodity objects, and the heat index of each selected commodity object is higher than that of each unselected commodity object;
the step of determining the freshness index corresponding to each commodity object in the commodity database comprises the following steps:
acquiring commodity shelving time recorded by first shelving of all commodity objects in a commodity database, classifying and summarizing the commodity objects according to the commodity shelving time, and determining the number of the commodity objects on shelves in each time period;
Determining inflection point time of long-tail distribution according to the long-tail distribution statistical characteristics presented by the number of commodity objects put on the shelf in each time period;
carrying out time attenuation calculation on the commodity shelf time of each commodity object in the commodity database by applying Newton's cooling law and inflection point time, and taking the calculation result as a corresponding freshness index of each commodity object;
the step of determining the competition index corresponding to each commodity object in the commodity database comprises the following steps:
acquiring specific ranking list data of a plurality of time nodes in a certain historical time corresponding to each commodity object;
for each commodity object, calculating the comprehensive change rate of the commodity object according to the plurality of time nodes, wherein the comprehensive change rate is the ratio between the difference value of the score of the specific ranking list obtained by the commodity object at the last time node and the score of the specific ranking list obtained by the commodity object at the earliest time node and the sum of the scores of the specific ranking lists obtained by the commodity object at all time nodes;
for each commodity object, the ratio of the difference between the comprehensive change rate and the minimum comprehensive change rate obtained in all commodity objects to the difference between the maximum comprehensive change rate and the minimum comprehensive change rate in all commodity objects is used as the competition index.
2. The commodity recommendation method according to claim 1, wherein: the step of determining sales indexes of the commodity objects in the commodity database comprises the following steps:
determining any two or more indexes of each commodity object in the commodity database: the scoring of advertisement data, ranking score of a list, scoring of total quantity of distribution network points, scoring of evaluation data and scoring of sales data, wherein the scoring of each index is unified into the same total scoring standard;
and weighting and summarizing the determined multiple indexes for each commodity object to obtain the sales index of the commodity object.
3. The commodity recommendation method according to claim 1, wherein: and for each commodity object, weighting and summing the corresponding freshness index, sales index and competition index which are unified to the same total score standard according to a preset heat calculation formula, and metering the freshness index, sales index and competition index which are unified to the same total score standard in the step of obtaining the heat index corresponding to the commodity object.
4. A commodity recommendation method according to any one of claims 1 to 3, including the post-step of: and recommending the commodity recommendation list to seller users registered by each independent transaction site maintained by the electronic commerce platform.
5. A commodity recommendation method according to any one of claims 1 to 3, including the preliminary steps of: the commodity database is preferably selected to only comprise commodity objects of which the historical transaction behavior information only comprises specific physical positions.
6. A commodity recommendation device, characterized in that it implements the commodity recommendation method according to any one of claims 1 to 5, comprising:
the freshness index module is used for determining freshness indexes corresponding to all commodity objects in the commodity database, and the freshness indexes are obtained by counting inflection point time of long tail distribution of the commodity database according to commodity shelf time and carrying out time attenuation calculation on the commodity shelf time of the commodity objects by combining Newton's cooling law and the inflection point time;
the sales index module is used for determining sales indexes of all commodity objects in the commodity database, and the sales indexes are obtained by weighting calculation according to a plurality of evaluation scores reflecting historic or potential sales capacities of the commodity objects;
the competition index module is used for determining competition indexes corresponding to all commodity objects in the commodity database, and the competition indexes are obtained by calculating according to ranking change data of the commodity objects in ranking lists corresponding to the classes of the electronic commerce platforms to which the commodity objects belong;
The weighting calculation module is used for carrying out weighting summation on the corresponding freshness index, sales index and competition index according to a preset heat calculation formula aiming at each commodity object to obtain a heat index corresponding to the commodity object;
and the commodity recommendation module is used for selecting a commodity recommendation list according to the heat index of each commodity object, wherein the commodity recommendation list comprises a plurality of selected commodity objects, and the heat index of each selected commodity object is higher than that of each unselected commodity object.
7. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the merchandise recommendation method of any one of claims 1 to 5.
8. A storage medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the merchandise recommendation method of any one of claims 1 to 5.
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| CN114169959A (en) * | 2021-11-18 | 2022-03-11 | 数贸科技（北京）有限公司 | Commodity recommendation method and device, computing equipment and computer storage medium |
| CN114549117A (en) * | 2022-01-14 | 2022-05-27 | 百芯智能制造科技(深圳)有限公司 | Electronic component heat calculation method and electronic equipment |
| CN114663155A (en) * | 2022-04-01 | 2022-06-24 | 广州华多网络科技有限公司 | Advertisement putting and selecting method and device, equipment, medium and product thereof |
| CN116109336B (en) * | 2023-04-13 | 2023-07-18 | 厦门蝉羽网络科技有限公司 | Method, system and storage medium for analysis and management of popularization data of electronic shop |
| CN117391811B (en) * | 2023-11-01 | 2024-07-02 | 深圳市沃客非凡科技有限公司 | Self-defined weight commodity selection system based on commodity feature engineering |
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