CN114331641A - Commodity pushing method and system based on big data - Google Patents
Commodity pushing method and system based on big data Download PDFInfo
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Abstract
The embodiment of the application discloses a commodity pushing method and a commodity pushing system based on big data, wherein the method comprises the following steps: the method comprises the steps of obtaining browsing records of a user aiming at a target commodity in a preset time period, wherein the browsing records comprise at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration; determining a target pushing parameter of the target commodity according to the browsing record; determining target push content corresponding to the target push parameters; and pushing the target push content. By adopting the embodiment of the application, the online shopping efficiency of the user can be improved.
Description
Technical Field
The application relates to the technical field of big data and electronic commerce, in particular to a commodity pushing method and system based on big data.
Background
With the rapid development of internet technology, online shopping is becoming a part of the life content of users. And at present, more and more merchants place the commodities to different online shops (platforms) for selling, the online commodities are full of enamels, and although a user can simply and conveniently obtain the needed commodities through an online shopping mode, the user often has difficulty in selection due to the fact that the user does not have the same selection and requirement, and therefore the problem of how to improve the online shopping efficiency of the user needs to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a commodity pushing method and system based on big data, and the online shopping efficiency of a user can be improved.
In a first aspect, an embodiment of the present application provides a commodity pushing method based on big data, where the method includes:
the method comprises the steps of obtaining browsing records of a user aiming at a target commodity in a preset time period, wherein the browsing records comprise at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration;
determining a target pushing parameter of the target commodity according to the browsing record;
determining target push content corresponding to the target push parameters;
and pushing the target push content.
In a second aspect, an embodiment of the present application provides a commodity pushing device based on big data, where the system includes: an acquisition unit, a first determination unit, a second determination unit and a push unit, wherein,
the acquisition unit is used for acquiring browsing records of a user for a target commodity in a preset time period, wherein the browsing records include at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration;
the first determining unit is used for determining a target pushing parameter of the target commodity according to the browsing record;
the second determining unit is configured to determine a target push content corresponding to the target push parameter;
and the pushing unit is used for pushing the target pushing content.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has the following beneficial effects:
it can be seen that, in the commodity pushing method and system based on big data described in the embodiments of the present application, browsing records of a user for a target commodity within a preset time period are obtained, where the browsing records include at least one of the following: the method comprises the steps of determining target pushing parameters of target commodities according to browsing records, determining target pushing contents corresponding to the target pushing parameters, pushing the target pushing contents, determining shopping demands of users based on the browsing records of the users to push the corresponding commodity contents, and further improving online shopping efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a commodity pushing method based on big data according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a method for constructing a coordinate system according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of another big data-based commodity pushing method provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 5 is a block diagram illustrating functional units of a big data-based product pushing system according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The electronic device described in the embodiment of the present application may include a smart Phone (e.g., an Android Phone, an iOS Phone, a Windows Phone, etc.), a tablet computer, a palm computer, a car data recorder, a notebook computer, a Mobile Internet device (MID, Mobile Internet Devices), or a wearable device (e.g., a smart watch, a bluetooth headset), which are merely examples, but are not exhaustive, and the electronic device may also include a server, for example, a cloud server.
The following describes embodiments of the present application in detail.
Referring to fig. 1, fig. 1 is a schematic flowchart of a commodity pushing method based on big data according to an embodiment of the present application, where as shown in the figure, the commodity pushing method based on big data includes:
101. the method comprises the steps of obtaining browsing records of a user aiming at a target commodity in a preset time period, wherein the browsing records comprise at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameter, commodity browsing duration.
The preset time period may be preset or default to the system, and the preset time period may be the last 1 hour, or the last 24 hours, or the last week. The target item may be an item or a class of items. The browsing record can be a browsing record meeting a preset condition, for example, the browsing record has a browsing duration longer than a preset duration, and the preset duration can be preset or default by the system, so that the wrong browsing can be prevented, a user can open a browsing at some times, and if the user finds that the user is not interested in the browsing record, the user can quickly close the browsing record.
In the embodiment of the present application, the commodity may be any commodity that can be sold, for example, the commodity may include at least one of the following: clothing, pants, hats, shoes, mobile phones, watches, etc., without limitation thereto.
In an embodiment of the present application, the browsing history may include at least one of the following: the brand name of the commodity, the specification of the commodity, the price of the commodity, the evaluation parameter of the commodity, the browsing duration of the commodity, the shelf loading duration of the commodity, the remaining stock quantity of the commodity and the like, which are not limited herein. The commodity specification may be understood as a size of the commodity, a grade of the commodity, a weight of the commodity, and the like, and is not limited herein. The commodity evaluation parameters may include at least one of: the number of reviews, the rating score, the evaluation score, etc., are not limited herein, wherein the rating may be understood as the ratio between the number of reviews and the total number of reviews, wherein the rating may be defined by the user or by system default, for example, a five-star full score, and 3.5 stars may be defined as the rating.
In specific implementation, a user can input a target commodity in a search bar, so that a search function is implemented, the user can browse the target commodity, and a browsing record of the user for the target commodity in a preset time period can be acquired.
Wherein, the browsing record can be the browsing record of at least one shopping platform.
In specific implementation, the browsing times of the target commodity in a preset time period can be counted, and when the browsing times reach the set times, the browsing record of the user for the target commodity in the preset time period is obtained. The set number of times can be preset or default to the system.
102. And determining a target pushing parameter of the target commodity according to the browsing record.
In a specific implementation, the push parameter may include at least one of the following: the pushed price interval, the pushed commodity specification, the pushed commodity evaluation interval, the pushed commodity sales interval, the pushed commodity pushing time, the pushed commodity express speed, the pushed commodity brand and the like are not limited herein. For example, the brand of the product browsed by the user may be used as the push parameter, and for example, a certain number of brands of the product with the browsing times before may be used as the push parameter.
Optionally, when the browsing record is n browsing records, where n is an integer greater than 1, in step 102, determining the target push parameter of the target product according to the browsing record may include the following steps:
21. acquiring commodity prices corresponding to the n browsing records to obtain n commodity prices;
22. acquiring commodity evaluation parameters corresponding to the n browsing records to obtain n groups of commodity evaluation parameters;
23. determining commodity price interval regulating factors according to the n groups of commodity evaluation parameters to obtain target regulating factors;
24. and determining a target pushing price interval according to the target adjusting factor and the n commodity prices.
In the specific implementation, each browsing record can correspond to a commodity price, further, commodity prices corresponding to n browsing records can be obtained to obtain n commodity prices, commodity evaluation parameters corresponding to n browsing records can also be obtained to obtain n groups of commodity evaluation parameters, commodity price interval adjusting factors can also be determined according to the n groups of commodity evaluation parameters to obtain target adjusting factors, wherein, the commodity evaluation parameters reflect the cost performance to a certain extent, the value range of the regulating factor can be between-1 and 1, a target push price interval is determined based on the target adjustment factor and the n item prices, e.g., the average value of n commodity prices can be determined, and then a target push price interval is determined according to the target adjustment factor and the average value, for example, the target push price interval = (1 + target adjustment factor) × the average value.
Optionally, the commodity evaluation parameters include the number of the reviews and the rating of the good, and in step 23, the commodity price interval adjustment factor is determined according to the n groups of commodity evaluation parameters to obtain the target adjustment factor, which may include the following steps:
231. constructing n coordinate points according to the n groups of commodity evaluation parameters, wherein the horizontal axis of each coordinate point is the number of the comment items and the vertical axis of each coordinate point is the good evaluation rate;
232. fitting according to the n coordinate points to obtain a fitted straight line, wherein the fitted straight line comprises a first endpoint and a second endpoint, the first endpoint corresponds to the minimum value of the number of the comments in the n groups of commodity evaluation parameters, and the second endpoint corresponds to the maximum value of the number of the comments in the n groups of commodity evaluation parameters;
233. determining the average value of the number of the comments in the n groups of commodity evaluation parameters to obtain the average number of the comments;
234. determining the mean value of the good evaluation rates in the n groups of commodity evaluation parameters to obtain an average good evaluation rate;
235. making a first straight line according to the average number of the comments, wherein the first straight line is parallel to the y axis and passes through a coordinate point (average number of the comments, 0);
236. making a second straight line according to the average good evaluation rate, wherein the second straight line is parallel to the x axis and passes through a coordinate point (0, the average good evaluation rate);
237. constructing a first triangle and a second triangle from the first line, the second line and the first endpoint and the second endpoint of the fitted line, the first triangle including the second endpoint or one of the first endpoints and an intersection of the first line and the second line, the second triangle including the first endpoint and the second endpoint;
238. determining the target adjustment factor according to the area of the first triangle and the area of the second triangle.
In a specific implementation, the commodity evaluation parameters may include the number of reviews and the goodness of evaluation. Specifically, n coordinate points may be constructed according to n sets of product evaluation parameters, where the horizontal axis of each coordinate point is the number of reviews and the vertical axis of each coordinate point is the rating, that is, each set of product evaluation parameters in the n sets of product evaluation parameters corresponds to one coordinate point, and the horizontal axis of each coordinate point is the number of reviews, for example, the number of reviews may be xxx ten thousands. The specific units can be determined based on the average value of the top preset percentages of the linked comment numbers corresponding to the target commodities, and the preset percentages can be preset or default by a system. For example, if the average of the top 20% of the number of linked review items corresponding to the target product is 2w, the unit corresponding to the x-axis may be ten thousand. The vertical axis of the coordinate point is percentage, namely the value of the coordinate point is between 0 and 1.
Furthermore, fitting can be performed according to the n coordinate points to obtain a fitted straight line, where the fitted straight line includes a first endpoint and a second endpoint, the first endpoint corresponds to a minimum value of the number of the comments in the n groups of commodity evaluation parameters, and the second endpoint corresponds to a maximum value of the number of the comments in the n groups of commodity evaluation parameters, that is, the fitted straight line corresponds to one section. As shown in FIG. 2, A is the first endpoint and B is the second endpoint. Of course, the more target commodities the user browses, the more corresponding coordinate points are, the more accurate the obtained fitting straight line is, and the more user requirements can be reflected.
Further, the average of the number of reviews in the n sets of product evaluation parameters may be determined to obtain an average number of reviews, and the average of the good ratings in the n sets of product evaluation parameters may be determined to obtain an average good rating.
Next, a first straight line may be drawn according to the average number of comments, the first straight line being parallel to the y-axis and passing through a coordinate point (average number of comments, 0), i.e., a G point, which may also be referred to as a first coordinate point, i.e., a coordinate point corresponding to the average number of comments. A second straight line can be made according to the average good evaluation rate, the second straight line is parallel to the x axis and passes through a coordinate point (0, the average good evaluation rate), namely a point D, and the coordinate point can also be called a second coordinate point, namely a coordinate point corresponding to the average good evaluation rate;
further, a first triangle and a second triangle may be constructed based on the first line, the second line, and the first endpoint and the second endpoint of the fitted line, wherein the first triangle may include the second end point or one of the first end points and an intersection point (point C) of the first line and the second line, and the second triangle may include a first end point, a second end point, and a fourth line may be drawn based on the first end point (point a), the fourth line is parallel to the x-axis, and a third line can be drawn based on the second end point (point B), the third line is parallel to the y-axis, the intersection between the third line and the fourth line is E, a second triangle may be constructed based on point a, point B and point E, and a first triangle may be constructed based on the point a, the point D, and the point C, or a first triangle may be constructed based on the point B, the point D, and the point C. Finally, a target adjustment factor may be determined based on the area of the first triangle and the area of the second triangle, i.e. an area ratio w between the area of the first triangle and the area of the second triangle may be determined, the target adjustment factor then being w and-w.
Because the number of the comments reflects the popularity, the goodness of the comment reflects the product quality and the user experience, in the concrete implementation, the user often pursues the cost performance, the cost performance not only needs to consider the trend tendency, but also needs to consider the product quality, the user pursues a balance between the two, one minute of money and one minute of goods, but sometimes some merchants pursue the quantity of dependence to win the success, therefore, a fluctuation range of the consumption section concerned by the user needs to be dynamically adjusted based on the number of the comments and the goodness of the comment, the comments of the user can be fitted to obtain a relatively reasonable fitting straight line, the psychological expectation of the public is comprehensively adjusted according to the browsing condition concerned by the user and the average goodness of the user to determine a reasonable price interval of the price concerned by the user, and further, the commodity with high cost performance which the user needs urgently can be accurately pushed. Especially, a user who focuses on price can be quickly helped to lock a price interval of psychological expectation, and the pushing efficiency and the pushing accuracy are improved.
Optionally, in the step 24, determining the target push price interval according to the target adjustment factor and the n commodity prices may include the following steps:
241. determining the commodity browsing duration corresponding to each commodity price in the n commodity prices to obtain n commodity browsing durations;
242. determining weights corresponding to the n commodity prices according to the n commodity browsing durations to obtain n weights;
243. performing weighted operation according to the n commodity prices and the n weights to obtain a reference commodity price;
244. and determining the target pushing price interval according to the target adjusting factor and the reference commodity price.
In specific implementation, by combining with the life experience of the user, the longer the commodity browsing time is, the more the user is concerned. Specifically, the commodity browsing time corresponding to each commodity price in n commodity prices may be determined to obtain n commodity browsing times, then the weight corresponding to n commodity prices is determined according to the n commodity browsing times to obtain n weights, for example, the total time corresponding to the n commodity browsing times may be determined, then the ratio between each commodity browsing time and the total time in the n commodity browsing times is respectively determined to obtain n ratios, that is, n weights, then the weighting operation is performed according to the n commodity prices and the n weights to obtain a reference commodity price, and finally, the target push price interval may be determined according to the target adjustment factor and the reference commodity price, that is, the target push price interval = (1 + target adjustment factor) = reference commodity price.
Optionally, when the browsing record is n browsing records, n is an integer greater than 1, and determining the target push parameter of the target product according to the browsing record may include the following steps:
obtaining the number of comments and the evaluation scores of the n browsing records to obtain n numbers of comments and n evaluation scores;
determining a first evaluation value corresponding to each comment number in the n comment numbers according to a mapping relation between the preset comment numbers and the evaluation values to obtain n first evaluation values;
determining a second evaluation value corresponding to each evaluation score in the n evaluation scores according to a preset mapping relation between the evaluation scores and the evaluation values to obtain n second evaluation values;
determining a first mean square error of the n number of reviews and determining a second mean square error of the n rating scores;
determining a first weight and a second weight according to the first mean square error and the second mean square error
Performing weighted operation according to the n first evaluation values, the n second evaluation values, the first weight and the second weight to obtain n reference evaluation values;
and determining an evaluation interval of the target commodity according to the n reference evaluation values.
In a specific implementation, when the browsing record is n browsing records, n is an integer greater than 1, each browsing record may correspond to one commodity, and a mapping relationship between a preset number of comments and an evaluation value and a mapping relationship between the preset number of comments and the evaluation value may be stored in advance.
Specifically, the number of reviews and evaluation scores of n browsing records may be acquired to obtain n review numbers and n evaluation scores, a first evaluation value corresponding to each review number of the n review numbers is determined according to a preset mapping relationship between the review numbers and the evaluation values to obtain n first evaluation values, a second evaluation value corresponding to each evaluation score of the n evaluation scores is determined according to a preset mapping relationship between the evaluation scores and the evaluation values to obtain n second evaluation values, a first mean square error of the n review numbers is determined, and a second mean square error of the n evaluation scores is determined. The number of reviews reflects the popularity of the good, or the popularity of the good, while the score of reviews reflects the experience of the good. The first mean square error reflects a fluctuation condition of the commodity selection heat of the user, and the second mean square error reflects a fluctuation condition of the experience feeling of the user concerning the commodity.
Further, the first weight and the second weight may be determined according to a first mean square error and a second mean square error, for example, the first weight = the second mean square error/(the first mean square error + the second mean square error), and the second weight = the first mean square error/(the first mean square error + the second mean square error). Next, n reference evaluation values may be obtained by performing a weighted operation based on the n first evaluation values, the n second evaluation values, the first weight, and the second weight, each of the n reference evaluation values = the first evaluation value + the first weight + the second evaluation value + the second weight, and finally, an evaluation section of the target commodity may be determined based on the n reference evaluation values. Therefore, the corresponding favorable evaluation interval can be determined according to the preference of the user and the attribute of the product, and the user is helped to quickly lock the evaluation interval of the commodity preferred by the user. The pushing efficiency and the pushing accuracy are improved.
103. And determining target push content corresponding to the target push parameters.
In specific implementation, the links corresponding to the target commodities can be screened based on the target push parameters, and the screened contents are used as target push contents. The target push content may correspond to a link to at least one target item.
Optionally, in step 103, determining the target push content corresponding to the target push parameter may include the following steps:
31. determining P pieces of push content corresponding to the target commodity, wherein P is an integer larger than 1;
32. and screening the P pieces of push contents according to the target push parameters to obtain Q pieces of push contents, and taking the Q pieces of push contents as the target push contents, wherein Q is an integer less than or equal to P.
In the specific implementation, P pieces of push content corresponding to the target commodity can be determined, wherein P is an integer larger than 1, the P pieces of push content are screened according to the target push parameters to obtain Q pieces of push content, the Q pieces of push content serve as the target push content, and Q is an integer smaller than or equal to P, so that the push content meeting the user requirements can be accurately found.
104. And pushing the target push content.
In a specific implementation, when the push content includes multiple items of push content, the push content may be pushed at a time, or the multiple items of push content may be pushed in a certain order. The certain order may include at least one of: the price is from high to low, the price is from low to high, the sales volume is from high to low, the good rating is from low to high, and the like, and the price is not limited herein.
It can be seen that, in the commodity pushing method based on big data described in the embodiment of the present application, browsing records of a user for a target commodity in a preset time period are obtained, where the browsing records include at least one of the following: the method comprises the steps of determining target pushing parameters of target commodities according to browsing records, determining target pushing contents corresponding to the target pushing parameters, pushing the target pushing contents, determining shopping demands of users based on the browsing records of the users to push the corresponding commodity contents, and further improving online shopping efficiency.
Referring to fig. 3, in accordance with the embodiment shown in fig. 1, fig. 3 is a schematic flowchart of another big data-based product pushing method provided in the embodiment of the present application, and is applied to an electronic device, where as shown in the diagram, the big data-based product pushing method includes:
301. the method comprises the steps of obtaining browsing records of a user aiming at a target commodity in a preset time period, wherein the browsing records comprise at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameter, commodity browsing duration.
302. And screening the browsing records according to the commodity browsing duration, and determining target pushing parameters of the target commodity according to the screened browsing records.
Wherein, in concrete the realization, the user probably has the mistake condition of touching, perhaps, because do not know the attribute of commodity, and then, open the link, and the user is not interested in this commodity, then the phenomenon of closing fast, consequently, then can be through the long screening of browsing record of duration of commodity browsing, and then, select the browsing record that is relevant with user's will intensity, utilize these browsing record to confirm the target propelling movement parameter of target commodity, can reflect user's true wish more, promote the propelling movement precision, and promote user experience.
303. And determining target push content corresponding to the target push parameters.
304. And pushing the target push content.
For the detailed description of steps 301 to 304, reference may be made to corresponding steps of the commodity pushing method based on big data described in fig. 1, and details are not described herein again.
It can be seen that, in the commodity pushing method based on big data described in the embodiment of the present application, browsing records of a user for a target commodity in a preset time period are obtained, where the browsing records include at least one of the following: the method comprises the steps of screening browsing records according to the commodity browsing time, determining target pushing parameters of target commodities according to the screened browsing records, determining target pushing contents corresponding to the target pushing parameters, pushing the target pushing contents, determining shopping demands of users based on the browsing records of the users to push corresponding commodity contents, and further improving online shopping efficiency.
In accordance with the foregoing embodiments, please refer to fig. 4, where fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, and as shown in the drawing, the electronic device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the programs include instructions for performing the following steps:
the method comprises the steps of obtaining browsing records of a user aiming at a target commodity in a preset time period, wherein the browsing records comprise at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration;
determining a target pushing parameter of the target commodity according to the browsing record;
determining target push content corresponding to the target push parameters;
and pushing the target push content.
Optionally, when the browsing record is n browsing records, n is an integer greater than 1, and in the aspect of determining the target push parameter of the target product according to the browsing record, the program includes an instruction for executing the following steps:
acquiring commodity prices corresponding to the n browsing records to obtain n commodity prices;
acquiring commodity evaluation parameters corresponding to the n browsing records to obtain n groups of commodity evaluation parameters;
determining commodity price interval regulating factors according to the n groups of commodity evaluation parameters to obtain target regulating factors;
and determining a target pushing price interval according to the target adjusting factor and the n commodity prices.
Optionally, the commodity evaluation parameters include the number of reviews and the rating of good, and in the aspect of determining a commodity price interval adjustment factor according to the n groups of commodity evaluation parameters to obtain a target adjustment factor, the program includes instructions for executing the following steps:
constructing n coordinate points according to the n groups of commodity evaluation parameters, wherein the horizontal axis of each coordinate point is the number of the comment items and the vertical axis of each coordinate point is the good evaluation rate;
fitting according to the n coordinate points to obtain a fitted straight line, wherein the fitted straight line comprises a first endpoint and a second endpoint, the first endpoint corresponds to the minimum value of the number of the comments in the n groups of commodity evaluation parameters, and the second endpoint corresponds to the maximum value of the number of the comments in the n groups of commodity evaluation parameters;
determining the average value of the number of the comments in the n groups of commodity evaluation parameters to obtain the average number of the comments;
determining the mean value of the good evaluation rates in the n groups of commodity evaluation parameters to obtain an average good evaluation rate;
making a first straight line according to the average number of the comments, wherein the first straight line is parallel to the y axis and passes through a coordinate point (average number of the comments, 0);
making a second straight line according to the average good evaluation rate, wherein the second straight line is parallel to the x axis and passes through a coordinate point (0, the average good evaluation rate);
constructing a first triangle and a second triangle from the first line, the second line and the first endpoint and the second endpoint of the fitted line, the first triangle including the second endpoint or one of the first endpoints and an intersection of the first line and the second line, the second triangle including the first endpoint and the second endpoint;
determining the target adjustment factor according to the area of the first triangle and the area of the second triangle.
Optionally, in the aspect of determining a target push price interval according to the target adjustment factor and the n commodity prices, the program includes instructions for executing the following steps:
determining the commodity browsing duration corresponding to each commodity price in the n commodity prices to obtain n commodity browsing durations;
determining weights corresponding to the n commodity prices according to the n commodity browsing durations to obtain n weights;
performing weighted operation according to the n commodity prices and the n weights to obtain a reference commodity price;
and determining the target pushing price interval according to the target adjusting factor and the reference commodity price.
Optionally, in the aspect of determining the target push content corresponding to the target push parameter, the program includes instructions for performing the following steps:
determining P pieces of push content corresponding to the target commodity, wherein P is an integer larger than 1;
and screening the P pieces of push contents according to the target push parameters to obtain Q pieces of push contents, and taking the Q pieces of push contents as the target push contents, wherein Q is an integer less than or equal to P.
It can be seen that, in the electronic device described in this embodiment of the present application, browsing records of a user for a target product within a preset time period are obtained, where the browsing records include at least one of the following: the method comprises the steps of screening browsing records according to the commodity browsing time, determining target pushing parameters of target commodities according to the screened browsing records, determining target pushing contents corresponding to the target pushing parameters, pushing the target pushing contents, determining shopping demands of users based on the browsing records of the users to push corresponding commodity contents, and further improving online shopping efficiency.
Fig. 5 is a block diagram of functional units of a big data based merchandise push system 500 according to an embodiment of the present application. The big data based commodity pushing system 500 is applied to an electronic device, and the system 500 includes: an acquisition unit 501, a first determination unit 502, a second determination unit 503, and a pushing unit 504, wherein,
the obtaining unit 501 is configured to obtain a browsing record of a user for a target product in a preset time period, where the browsing record includes at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration;
the first determining unit 502 is configured to determine a target pushing parameter of the target product according to the browsing record;
the second determining unit 503 is configured to determine a target push content corresponding to the target push parameter;
the pushing unit 504 is configured to push the target push content.
Optionally, when the browsing record is n browsing records, n is an integer greater than 1, and in the aspect of determining the target push parameter of the target product according to the browsing record, the first determining unit 502 is specifically configured to:
acquiring commodity prices corresponding to the n browsing records to obtain n commodity prices;
acquiring commodity evaluation parameters corresponding to the n browsing records to obtain n groups of commodity evaluation parameters;
determining commodity price interval regulating factors according to the n groups of commodity evaluation parameters to obtain target regulating factors;
and determining a target pushing price interval according to the target adjusting factor and the n commodity prices.
Optionally, the commodity evaluation parameters include the number of reviews and the rating, and in the aspect of determining a commodity price interval adjustment factor according to the n groups of commodity evaluation parameters to obtain a target adjustment factor, the first determining unit 502 is specifically configured to:
constructing n coordinate points according to the n groups of commodity evaluation parameters, wherein the horizontal axis of each coordinate point is the number of the comment items and the vertical axis of each coordinate point is the good evaluation rate;
fitting according to the n coordinate points to obtain a fitted straight line, wherein the fitted straight line comprises a first endpoint and a second endpoint, the first endpoint corresponds to the minimum value of the number of the comments in the n groups of commodity evaluation parameters, and the second endpoint corresponds to the maximum value of the number of the comments in the n groups of commodity evaluation parameters;
determining the average value of the number of the comments in the n groups of commodity evaluation parameters to obtain the average number of the comments;
determining the mean value of the good evaluation rates in the n groups of commodity evaluation parameters to obtain an average good evaluation rate;
making a first straight line according to the average number of the comments, wherein the first straight line is parallel to the y axis and passes through a coordinate point (average number of the comments, 0);
making a second straight line according to the average good evaluation rate, wherein the second straight line is parallel to the x axis and passes through a coordinate point (0, the average good evaluation rate);
constructing a first triangle and a second triangle from the first line, the second line and the first endpoint and the second endpoint of the fitted line, the first triangle including the second endpoint or one of the first endpoints and an intersection of the first line and the second line, the second triangle including the first endpoint and the second endpoint;
determining the target adjustment factor according to the area of the first triangle and the area of the second triangle.
Optionally, in the aspect of determining a target push price interval according to the target adjustment factor and the n commodity prices, the first determining unit 502 is specifically configured to:
determining the commodity browsing duration corresponding to each commodity price in the n commodity prices to obtain n commodity browsing durations;
determining weights corresponding to the n commodity prices according to the n commodity browsing durations to obtain n weights;
performing weighted operation according to the n commodity prices and the n weights to obtain a reference commodity price;
and determining the target pushing price interval according to the target adjusting factor and the reference commodity price.
Optionally, in terms of determining the target push content corresponding to the target push parameter, the second determining unit 503 is specifically configured to:
determining P pieces of push content corresponding to the target commodity, wherein P is an integer larger than 1;
and screening the P pieces of push contents according to the target push parameters to obtain Q pieces of push contents, and taking the Q pieces of push contents as the target push contents, wherein Q is an integer less than or equal to P.
It can be seen that, in the commodity pushing system based on big data described in this embodiment of the present application, a browsing record of a user for a target commodity in a preset time period is obtained, where the browsing record includes at least one of the following: the method comprises the steps of screening browsing records according to the commodity browsing time, determining target pushing parameters of target commodities according to the screened browsing records, determining target pushing contents corresponding to the target pushing parameters, pushing the target pushing contents, determining shopping demands of users based on the browsing records of the users to push corresponding commodity contents, and further improving online shopping efficiency.
It can be understood that the functions of each program module of the big data based product push system according to this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the related description of the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, the computer comprising an electronic device.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A commodity pushing method based on big data is characterized by comprising the following steps:
the method comprises the steps of obtaining browsing records of a user aiming at a target commodity in a preset time period, wherein the browsing records comprise at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration;
determining a target pushing parameter of the target commodity according to the browsing record;
determining target push content corresponding to the target push parameters;
and pushing the target push content.
2. The method according to claim 1, wherein when the browsing record is n browsing records, where n is an integer greater than 1, the determining the target push parameter of the target product according to the browsing record includes:
acquiring commodity prices corresponding to the n browsing records to obtain n commodity prices;
acquiring commodity evaluation parameters corresponding to the n browsing records to obtain n groups of commodity evaluation parameters;
determining commodity price interval regulating factors according to the n groups of commodity evaluation parameters to obtain target regulating factors;
and determining a target pushing price interval according to the target adjusting factor and the n commodity prices.
3. The method according to claim 2, wherein the commodity evaluation parameters comprise the number of reviews and the rating, and the determining a commodity price interval adjustment factor according to the n groups of commodity evaluation parameters to obtain a target adjustment factor comprises:
constructing n coordinate points according to the n groups of commodity evaluation parameters, wherein the horizontal axis of each coordinate point is the number of the comment items and the vertical axis of each coordinate point is the good evaluation rate;
fitting according to the n coordinate points to obtain a fitted straight line, wherein the fitted straight line comprises a first endpoint and a second endpoint, the first endpoint corresponds to the minimum value of the number of the comments in the n groups of commodity evaluation parameters, and the second endpoint corresponds to the maximum value of the number of the comments in the n groups of commodity evaluation parameters;
determining the average value of the number of the comments in the n groups of commodity evaluation parameters to obtain the average number of the comments;
determining the mean value of the good evaluation rates in the n groups of commodity evaluation parameters to obtain an average good evaluation rate;
making a first straight line according to the average number of the comments, wherein the first straight line is parallel to the y axis and passes through a coordinate point (average number of the comments, 0);
making a second straight line according to the average good evaluation rate, wherein the second straight line is parallel to the x axis and passes through a coordinate point (0, the average good evaluation rate);
constructing a first triangle and a second triangle from the first line, the second line and the first endpoint and the second endpoint of the fitted line, the first triangle including the second endpoint or one of the first endpoints and an intersection of the first line and the second line, the second triangle including the first endpoint and the second endpoint;
determining the target adjustment factor according to the area of the first triangle and the area of the second triangle.
4. The method of claim 2, wherein determining a target push price interval based on the target adjustment factor and the n commodity prices comprises:
determining the commodity browsing duration corresponding to each commodity price in the n commodity prices to obtain n commodity browsing durations;
determining weights corresponding to the n commodity prices according to the n commodity browsing durations to obtain n weights;
performing weighted operation according to the n commodity prices and the n weights to obtain a reference commodity price;
and determining the target pushing price interval according to the target adjusting factor and the reference commodity price.
5. The method according to any of claims 1-4, wherein the determining the targeted push content corresponding to the targeted push parameters comprises:
determining P pieces of push content corresponding to the target commodity, wherein P is an integer larger than 1;
and screening the P pieces of push contents according to the target push parameters to obtain Q pieces of push contents, and taking the Q pieces of push contents as the target push contents, wherein Q is an integer less than or equal to P.
6. A big data based item pushing system, the system comprising: an acquisition unit, a first determination unit, a second determination unit and a push unit, wherein,
the acquisition unit is used for acquiring browsing records of a user for a target commodity in a preset time period, wherein the browsing records include at least one of the following: commodity brand, commodity specification, commodity price, commodity evaluation parameters and commodity browsing duration;
the first determining unit is used for determining a target pushing parameter of the target commodity according to the browsing record;
the second determining unit is configured to determine a target push content corresponding to the target push parameter;
and the pushing unit is used for pushing the target pushing content.
7. The system according to claim 6, wherein when the browsing record is n browsing records, n is an integer greater than 1, and in the aspect of determining the target push parameter of the target product according to the browsing record, the first determining unit is specifically configured to:
acquiring commodity prices corresponding to the n browsing records to obtain n commodity prices;
acquiring commodity evaluation parameters corresponding to the n browsing records to obtain n groups of commodity evaluation parameters;
determining commodity price interval regulating factors according to the n groups of commodity evaluation parameters to obtain target regulating factors;
and determining a target pushing price interval according to the target adjusting factor and the n commodity prices.
8. The system according to claim 7, wherein the commodity evaluation parameters include a number of reviews and a rating score, and in the aspect of determining the commodity price interval adjustment factor according to the n groups of commodity evaluation parameters to obtain the target adjustment factor, the first determining unit is specifically configured to:
constructing n coordinate points according to the n groups of commodity evaluation parameters, wherein the horizontal axis of each coordinate point is the number of the comment items and the vertical axis of each coordinate point is the good evaluation rate;
fitting according to the n coordinate points to obtain a fitted straight line, wherein the fitted straight line comprises a first endpoint and a second endpoint, the first endpoint corresponds to the minimum value of the number of the comments in the n groups of commodity evaluation parameters, and the second endpoint corresponds to the maximum value of the number of the comments in the n groups of commodity evaluation parameters;
determining the average value of the number of the comments in the n groups of commodity evaluation parameters to obtain the average number of the comments;
determining the mean value of the good evaluation rates in the n groups of commodity evaluation parameters to obtain an average good evaluation rate;
making a first straight line according to the average number of the comments, wherein the first straight line is parallel to the y axis and passes through a coordinate point (average number of the comments, 0);
making a second straight line according to the average good evaluation rate, wherein the second straight line is parallel to the x axis and passes through a coordinate point (0, the average good evaluation rate);
constructing a first triangle and a second triangle from the first line, the second line and the first endpoint and the second endpoint of the fitted line, the first triangle including the second endpoint or one of the first endpoints and an intersection of the first line and the second line, the second triangle including the first endpoint and the second endpoint;
determining the target adjustment factor according to the area of the first triangle and the area of the second triangle.
9. An electronic device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-5.
10. An electronic device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-5.
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| CN117035948A (en) * | 2023-10-10 | 2023-11-10 | 山东唐和智能科技有限公司 | Task intelligent processing method and system based on big data |
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| CN117035948A (en) * | 2023-10-10 | 2023-11-10 | 山东唐和智能科技有限公司 | Task intelligent processing method and system based on big data |
| CN117035948B (en) * | 2023-10-10 | 2024-01-09 | 山东唐和智能科技有限公司 | Task intelligent processing method and system based on big data |
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