Commodity push method and system based on big data

Abstract

This embodiment of the application discloses a method and system for product push based on big data. The method includes: obtaining a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time; determining a target push parameter for the target product based on the browsing history; determining a target push content corresponding to the target push parameter; and pushing the target push content. The embodiment of the application can improve user online shopping efficiency.

Description

Technical Field

[0001] The present application relates to the fields of big data and e-commerce technology, and specifically to a commodity push method and system based on big data. Background Art

[0002] With the rapid development of Internet technology, online shopping has become an integral part of users' lives. Furthermore, more and more businesses are now selling their products on various online stores (platforms), creating a dazzling array of products. While users can easily and conveniently obtain the products they need through online shopping, their choices and needs vary, making choices difficult. Therefore, improving online shopping efficiency is an urgent issue that needs to be addressed. Summary of the Invention

[0003] The embodiments of the present application provide a product push method and system based on big data, which can improve users' online shopping efficiency.

[0004] In a first aspect, an embodiment of the present application provides a product push method based on big data, the method comprising:

[0005] Obtaining a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time;

[0006] Determining target push parameters of the target product based on the browsing history;

[0007] Determining target push content corresponding to the target push parameters;

[0008] Push the target push content.

[0009] In a second aspect, an embodiment of the present application provides a commodity push device based on big data, the system comprising: an acquisition unit, a first determination unit, a second determination unit, and a push unit, wherein:

[0010] The acquisition unit is configured to acquire a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time;

[0011] The first determining unit is configured to determine target push parameters of the target product according to the browsing history; the second determining unit is configured to determine target push content corresponding to the target push parameters;

[0012] The pushing unit is configured to push the target push content.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the steps in the first aspect of the embodiment of the present application.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0015] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0016] The implementation of the embodiments of this application has the following beneficial effects:

[0017] It can be seen that the big data-based product push method and system described in the embodiments of the present application obtains the user's browsing history for the target product within a preset time period, and the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time. The target push parameters of the target product are determined based on the browsing history, the target push content corresponding to the target push parameters is determined, and the target push content is pushed. Based on the user's browsing history, the user's shopping needs can be determined to push corresponding product content, thereby improving online shopping efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a method for pushing products based on big data provided by an embodiment of the present application;

[0020] Figure 2 This is a schematic diagram illustrating a method of constructing a coordinate system according to an embodiment of the present application;

[0021] Figure 3This is a flow chart of another method for pushing products based on big data provided by an embodiment of the present application;

[0022] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0023] Figure 5 This is a block diagram of the functional units of a big data-based product push system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0026] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0027] The electronic devices described in the embodiments of the present application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, laptops, mobile Internet devices (MIDs) or wearable devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices. The electronic devices may also include servers, such as cloud servers.

[0028] The following is a detailed introduction to the embodiments of the present application.

[0029] See also Figure 1 , Figure 1 This is a flow chart of a method for pushing products based on big data provided by an embodiment of the present application. As shown in the figure, the method for pushing products based on big data includes:

[0030] 101. Obtain a user's browsing history for a target product within a preset time period, where the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time.

[0031] The preset time period can be pre-set or system default, and can be the last hour, the last 24 hours, or the last week. The target product can be a type of product or a category of products. The browsing history can be browsing history that meets preset conditions, for example, browsing history with a browsing duration greater than a preset time period. The preset time period can be pre-set or system default, thereby preventing accidental browsing. Sometimes a user will open a browser and quickly close it if they find they are not interested.

[0032] In the embodiment of the present application, the commodity can be any commodity that can be sold. For example, the commodity can include at least one of the following: clothes, pants, hats, shoes, mobile phones, watches, etc., which are not limited here.

[0033] In an embodiment of the present application, the browsing history may include at least one of the following: product brand, product specifications, product price, product evaluation parameters, product browsing time, product shelf time, remaining inventory quantity of the product, etc., which are not limited here. Among them, product specifications can be understood as the size of the product, the grade of the product, the weight of the product, etc., which are not limited here. Product evaluation parameters may include at least one of the following: the number of reviews, the rate of positive reviews, the evaluation score, etc., which are not limited here. Among them, the rate of positive reviews can be understood as the ratio between the number of positive reviews and the total number of reviews, wherein the positive reviews can be defined by the user or by the system default. For example, if the full score is five stars, 3.5 stars can be defined as a positive review.

[0034] In a specific implementation, the user can enter the target product in the search bar, thereby realizing the search function, and the user can browse the target product, and the user's browsing history for the target product within a preset time period can be obtained.

[0035] The browsing history may be a browsing history of at least one shopping platform.

[0036] In a specific implementation, the number of times a target product is viewed within a preset time period can be counted, and when the number of views reaches the set number, the user's browsing history for the target product within the preset time period is obtained. The set number can be pre-set or system default.

[0037] 102. Determine target push parameters for the target product based on the browsing history.

[0038] In a specific implementation, push parameters may include at least one of the following: push price range, push product specifications, push product rating range, push product sales range, push product delivery time, push product delivery speed, push product brand, etc., without limitation. For example, the product brands browsed by the user can be used as push parameters, or a certain number of product brands with the highest number of views can be used as push parameters.

[0039] Optionally, when the number of browsing records is n, and n is an integer greater than 1, the above step 102 of determining the target push parameters of the target product according to the browsing records may include the following steps:

[0040] 21. Obtain the product prices corresponding to the n browsing records to obtain n product prices;

[0041] 22. Obtain product evaluation parameters corresponding to the n browsing records to obtain n groups of product evaluation parameters;

[0042] 23. Determine a commodity price range adjustment factor based on the n groups of commodity evaluation parameters to obtain a target adjustment factor;

[0043] 24. Determine a target push price range based on the target adjustment factor and the n commodity prices.

[0044] In a specific implementation, each browsing record may correspond to a product price. Then, the product prices corresponding to n browsing records may be obtained to obtain n product prices. The product evaluation parameters corresponding to the n browsing records may also be obtained to obtain n groups of product evaluation parameters. The product price range adjustment factor may also be determined based on the n groups of product evaluation parameters to obtain a target adjustment factor. The product evaluation parameter reflects the price-performance ratio to a certain extent. The value range of the adjustment factor may be between -1 and 1. The target push price range may be determined based on the target adjustment factor and the n product prices. For example, the average of the n product prices may be determined, and then the target push price range may be determined based on the target adjustment factor and the average. For example, the target push price range = (1 + target adjustment factor) * average.

[0045] Optionally, the product evaluation parameters include the number of reviews and the rate of favorable comments. Step 23, determining the product price range adjustment factor based on the n groups of product evaluation parameters to obtain the target adjustment factor, may include the following steps:

[0046] 231. Construct n coordinate points based on the n sets of product evaluation parameters, where the horizontal axis of each coordinate point represents the number of reviews and the vertical axis represents the praise rate;

[0047] 232. Perform fitting based on 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 the minimum number of reviews in the n sets of product evaluation parameters, and the second endpoint corresponds to the maximum number of reviews in the n sets of product evaluation parameters;

[0048] 233. Determine the mean of the number of reviews in the n groups of product evaluation parameters to obtain an average number of reviews;

[0049] 234. Determine the average of the favorable reviews in the n groups of product evaluation parameters to obtain an average favorable review rate;

[0050] 235. Draw a first straight line based on the average number of comments, where the first straight line is parallel to the y-axis and passes through the coordinate point (average number of comments, 0);

[0051] 236. Draw a second straight line based on the average positive review rate, where the second straight line is parallel to the x-axis and passes through the coordinate point (0, average positive review rate);

[0052] 237. Construct a first triangle and a second triangle based on the first straight line, the second straight line, and the first and second endpoints of the fitted straight line. The first triangle includes the second endpoint or one of the first endpoints, the intersection of the first straight line and the second straight line, and the intersection of the line connecting the first and second endpoints with the second straight line. The second triangle includes the first and second endpoints, the intersection of a fourth straight line parallel to the x-axis based on the first endpoint and a third straight line parallel to the y-axis based on the second endpoint.

[0053] 238. Determine the target adjustment factor according to the area of the first triangle and the area of the second triangle.

[0054] In a specific implementation, the product evaluation parameters may include the number of comments and the praise rate. Specifically, n coordinate points can be constructed based on n groups of product evaluation parameters, the horizontal axis of each coordinate point is the number of comments and the vertical axis is the praise rate, that is, each group of product evaluation parameters in the n groups of product evaluation parameters corresponds to a coordinate point, and the horizontal axis of the coordinate point is the number of comments. For example, the number of comments may be xxx ten thousand. The specific unit can be determined based on the average of the top preset percentage of the number of comments on the link corresponding to the target product. The preset percentage can be pre-set or system default. For example, the average of the top 20% of the number of comments on the link corresponding to the target product is 2w, then the unit corresponding to the x-axis may be ten thousand. The vertical axis of the coordinate point is a percentage, that is, its value is between 0 and 1.

[0055] Furthermore, a fitting line can be obtained by fitting based on n coordinate points. The fitting line includes a first endpoint and a second endpoint. The first endpoint corresponds to the minimum number of comments in the n sets of product evaluation parameters, and the second endpoint corresponds to the maximum number of comments in the n sets of product evaluation parameters. That is, the fitting line corresponds to an interval segment. Figure 2 As shown in the figure, A is the first endpoint and B is the second endpoint. Of course, the more target products a user browses, the more corresponding coordinate points there are, and the more accurate the fitted line is, and the more it can reflect the user's needs.

[0056] Furthermore, the mean of the number of comments in the n groups of product evaluation parameters can be determined to obtain the average number of comments, and the mean of the favorable review rate in the n groups of product evaluation parameters can be determined to obtain the average favorable review rate.

[0057] Next, we can draw a first straight line based on the average number of reviews. The first straight line is parallel to the y-axis and passes through the coordinate point (average number of reviews, 0), which is point G. This coordinate point can also be called the first coordinate point, that is, the coordinate point corresponding to the average number of reviews. We can also draw a second straight line based on the average positive review rate. The second straight line is parallel to the x-axis and passes through the coordinate point (0, average positive review rate), which is point F. This coordinate point can also be called the second coordinate point, that is, the coordinate point corresponding to the average positive review rate.

[0058] Further, such as Figure 2 As shown, a first triangle and a second triangle can be constructed based on the first and second endpoints of the first and second straight lines, respectively. The first triangle can include the second endpoint or one of the first endpoints, the intersection of the first and second straight lines (point C), and the intersection of the line connecting the first and second endpoints and the second line (point D). The second triangle can include the first and second endpoints, and a fourth straight line drawn based on the first endpoint (point A), the fourth straight line being parallel to the x-axis. Furthermore, a third straight line drawn based on the second endpoint (point B), the third straight line being parallel to the y-axis, and the intersection of the third and fourth straight lines being point E. The second triangle can be constructed based on points A, B, and E, while the first triangle can be constructed based on points A, D, and C. Alternatively, the first triangle can be constructed based on points B, D, and C. Finally, a target adjustment factor can be determined based on the areas of the first and second triangles. That is, the area ratio w between the areas of the first and second triangles can be determined. The target adjustment factor is w and -w.

[0059] Since the number of reviews reflects popularity and the positive review rate reflects product quality and user experience, users often pursue cost-effectiveness in practice. This cost-effectiveness not only needs to consider trends but also product quality. A balance between the two is necessary. You get what you pay for, but sometimes some businesses also pursue volume. Therefore, it is necessary to dynamically adjust the fluctuation range of the consumption segment that users are interested in based on the number of reviews and the positive review rate. Users' reviews can be fitted to obtain a relatively reasonable fitting line. The public's psychological expectations can then be comprehensively adjusted based on the browsing behavior and average positive reviews of the products they are interested in to determine a reasonable price range for the price range they are concerned about. This allows for the accurate push of cost-effective products that users are in urgent need of. This is especially true for users who focus on price. This allows them to quickly lock in a price range that meets their psychological expectations, which helps improve push efficiency and accuracy.

[0060] Optionally, the above step 24, determining the target push price range based on the target adjustment factor and the n commodity prices, may include the following steps:

[0061] 241. Determine the product browsing duration corresponding to each of the n product prices to obtain n product browsing durations;

[0062] 242. Determine weights corresponding to the prices of the n products based on the browsing durations of the n products to obtain n weights;

[0063] 243. Perform a weighted operation based on the n commodity prices and the n weights to obtain a reference commodity price;

[0064] 244. Determine the target push price range based on the target adjustment factor and the reference product price.

[0065] In specific implementations, based on user experience, longer product browsing times often indicate greater user interest. Specifically, the product browsing time corresponding to each of n product prices can be determined to obtain n product browsing times. Weights corresponding to the n product prices can then be determined based on the n product browsing times to obtain n weights. For example, the total browsing time corresponding to the n product browsing times can be determined. The ratio of each of the n product browsing times to the total browsing time can then be determined to obtain n ratios, i.e., n weights. A weighted calculation is then performed based on the n product prices and n weights to obtain a reference product price. Finally, a target push price range can be determined based on the target adjustment factor and the reference product price: target push price range = (1 + target adjustment factor) * reference product price.

[0066] Optionally, when the number of browsing records is n, and n is an integer greater than 1, the step of determining the target push parameter of the target product according to the browsing records may include the following steps:

[0067] Get the number of comments and evaluation scores of n browsing records, and get n number of comments and n evaluation scores;

[0068] According to a preset mapping relationship between the number of comments and the evaluation value, determine the first evaluation value corresponding to each number of comments in the n number of comments, and obtain n first evaluation values;

[0069] According to the mapping relationship between the preset evaluation scores and evaluation values, determine the value of each evaluation score in the n evaluation scores.

[0070] The second evaluation value corresponding to the number is obtained to obtain n second evaluation values;

[0071] Determine a first mean square deviation of the n number of comments, and determine a second mean square deviation of the n evaluation scores; determine a first weight and a second weight based on the first mean square deviation and the second mean square deviation

[0072] Performing a weighted operation based on the n first evaluation values, the n second evaluation values, the first weight, and the second weight to obtain n reference evaluation values;

[0073] An evaluation interval of the target product is determined according to the n reference evaluation values.

[0074] In a specific implementation, when there are n browsing records, n is an integer greater than 1, each browsing record may correspond to a product, and the mapping relationship between the preset number of comments and the evaluation value, as well as the mapping relationship between the preset number of comments and the evaluation value may be pre-stored.

[0075] Specifically, the number of comments and evaluation scores of n browsing records can be obtained to obtain n numbers of comments and n evaluation scores. According to the preset mapping relationship between the number of comments and the evaluation value, the first evaluation value corresponding to each of the n numbers of comments is determined to obtain n first evaluation values. Then, according to the preset mapping relationship between the evaluation score and the evaluation value, the second evaluation value corresponding to each of the n evaluation scores is determined to obtain n second evaluation values. Then, the first mean square deviation of the n numbers of comments and the second mean square deviation of the n evaluation scores are determined. The number of comments reflects the popularity of the product, or the popularity of the product, while the comment score reflects the user experience of the product. The first mean square deviation reflects the fluctuation of the user's popularity in selecting the product, and the second mean square deviation reflects the fluctuation of the user's experience of paying attention to the product.

[0076] Furthermore, the first and second weights can be determined based on the first and second mean square deviations. For example, the first weight = second mean square deviation / (first mean square deviation + second mean square deviation), and the second weight = first mean square deviation / (first mean square deviation + second mean square deviation). Next, a weighted operation can be performed based on the n first evaluation values, n second evaluation values, the first weight, and the second weight to obtain n reference evaluation values. Each reference evaluation value = first evaluation value * first weight + second evaluation value * second weight. Thus, n reference evaluation values can be obtained. Finally, the evaluation range of the target product can be determined based on the n reference evaluation values. For example, the minimum and maximum values of the n reference evaluation values can be selected, and the range between them can be used as the evaluation range of the target product. In this way, a corresponding positive evaluation range can be determined based on user preferences and product attributes, helping users quickly identify the evaluation range of their preferred products. This helps improve push notification efficiency and accuracy.

[0077] 103. Determine target push content corresponding to the target push parameter.

[0078] In a specific implementation, the links corresponding to the target products can be filtered based on the target push parameters, and the filtered content can be used as the target push content. The target push content can correspond to a link to at least one target product.

[0079] Optionally, the above step 103, determining the target push content corresponding to the target push parameter, may include the following steps:

[0080] 31. Determine P push content corresponding to the target product, where P is an integer greater than 1;

[0081] 32. Filter the P push contents according to the target push parameter to obtain Q push contents, and use the Q push contents as the target push contents, where Q is an integer less than or equal to P.

[0082] In the specific implementation, P push content corresponding to the target product can be determined, where P is an integer greater than 1. Then, the P push content is filtered according to the target push parameters to obtain Q push content. The Q push content is used as the target push content, where Q is an integer less than or equal to P. In this way, the push content that meets user needs can be accurately found.

[0083] 104. Push the target push content.

[0084] In a specific implementation, when the push content includes multiple items, the items can be pushed all at once or in a certain order. The certain order can include at least one of the following: price from high to low, price from low to high, sales from low to high, sales from high to low, positive review rate from high to low, positive review rate from low to high, etc., which is not limited here.

[0085] It can be seen that the big data-based product push method described in the embodiment of the present application obtains the user's browsing history for the target product within a preset time period, and the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time. The target push parameters of the target product are determined based on the browsing history, and the target push content corresponding to the target push parameters is determined. The target push content is pushed. Based on the user's browsing history, the user's shopping needs can be determined to push corresponding product content, thereby improving online shopping efficiency.

[0086] With the above Figure 1 For details on the embodiments shown, please refer to Figure 3 , Figure 3 This is a flow chart of another method for pushing products based on big data provided by an embodiment of the present application, which is applied to an electronic device. As shown in the figure, this method for pushing products based on big data includes:

[0087] 301. Obtain a user's browsing history for a target product within a preset time period, where the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time.

[0088] 302. Filter the browsing history according to the product browsing time, and determine the target push parameters of the target product according to the filtered browsing history.

[0089] Among them, in the specific implementation, the user may accidentally touch the link, or, due to not understanding the attributes of the product, the user opens the link, and then quickly closes the link if the user is not interested in the product. Therefore, the browsing history can be filtered according to the browsing time of the product, and then the browsing history that is strongly related to the user's intention can be filtered out. These browsing records are used to determine the target push parameters of the target product, which can better reflect the user's true intention, improve the push accuracy, and enhance the user experience.

[0090] 303. Determine target push content corresponding to the target push parameter.

[0091] 304. Push the target push content.

[0092] The detailed description of steps 301 to 304 can refer to the above Figure 1The corresponding steps of the described method for pushing products based on big data will not be repeated here.

[0093] It can be seen that the big data-based product push method described in the embodiment of the present application obtains the user's browsing history for the target product within a preset time period, and the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time. The browsing history is filtered according to the product browsing time, and the target push parameters of the target product are determined according to the filtered browsing history, and the target push content corresponding to the target push parameters is determined. The target push content is pushed, and the user's shopping needs can be determined based on the user's browsing history to push the corresponding product content, thereby improving the efficiency of online shopping.

[0094] In accordance with the above embodiment, please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in the figure, the electronic device includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for performing the following steps:

[0095] Obtaining a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time;

[0096] Determining target push parameters of the target product based on the browsing history;

[0097] Determining target push content corresponding to the target push parameters;

[0098] Push the target push content.

[0099] Optionally, when the number of browsing records is n, n is an integer greater than 1, and in determining the target push parameters of the target product based on the browsing records, the program includes instructions for executing the following steps:

[0100] Obtaining the product prices corresponding to the n browsing records to obtain n product prices;

[0101] Obtain product evaluation parameters corresponding to the n browsing records to obtain n groups of product evaluation parameters;

[0102] Determine a commodity price range adjustment factor based on the n groups of commodity evaluation parameters to obtain a target adjustment factor;

[0103] A target push price range is determined based on the target adjustment factor and the n commodity prices.

[0104] Optionally, the product evaluation parameters include the number of reviews and the rate of favorable comments. In determining the product price range adjustment factor based on the n sets of product evaluation parameters to obtain the target adjustment factor, the program includes instructions for executing the following steps:

[0105] Construct n coordinate points based on the n sets of product evaluation parameters, where the horizontal axis of each coordinate point represents the number of reviews and the vertical axis represents the praise rate;

[0106] Performing fitting based on the n coordinate points to obtain a fitted straight line, the fitted straight line including a first endpoint and a second endpoint, the first endpoint corresponding to the minimum number of reviews in the n sets of product evaluation parameters, and the second endpoint corresponding to the maximum number of reviews in the n sets of product evaluation parameters;

[0107] Determine the mean of the number of reviews in the n groups of product evaluation parameters to obtain an average number of reviews;

[0108] Determine the average of the favorable reviews among the n groups of product evaluation parameters to obtain an average favorable review rate;

[0109] Draw a first straight line according to the average number of comments, where the first straight line is parallel to the y-axis and passes through the coordinate point (average number of comments, 0);

[0110] Draw a second straight line based on the average positive review rate, where the second straight line is parallel to the x-axis and passes through the coordinate point (0, average positive review rate);

[0111] Constructing a first triangle and a second triangle based on the first straight line, the second straight line, and the first endpoint and the second endpoint of the fitted straight line, wherein the first triangle includes the second endpoint or one of the first endpoints and the intersection of the first straight line and the second straight line, and the second triangle includes the first endpoint and the second endpoint;

[0112] The target adjustment factor is determined according to the area of the first triangle and the area of the second triangle.

[0113] Optionally, in determining the target push price range based on the target adjustment factor and the n commodity prices, the program includes instructions for executing the following steps:

[0114] Determine the product browsing time corresponding to each of the n product prices to obtain n product browsing times;

[0115] Determining weights corresponding to the prices of the n products according to the browsing time of the n products to obtain n weights;

[0116] Performing a weighted calculation based on the n commodity prices and the n weights to obtain a reference commodity price;

[0117] The target push price range is determined according to the target adjustment factor and the reference commodity price.

[0118] Optionally, in determining the target push content corresponding to the target push parameter, the program includes instructions for executing the following steps:

[0119] Determine P push content corresponding to the target product, where P is an integer greater than 1;

[0120] The P pieces of pushed content are screened according to the target push parameter to obtain Q pieces of pushed content, and the Q pieces of pushed content are used as the target pushed content, where Q is an integer less than or equal to P.

[0121] It can be seen that the electronic device described in the embodiment of the present application obtains the user's browsing history for the target product within a preset time period, and the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time. The browsing history is filtered according to the product browsing time, and the target push parameters of the target product are determined according to the filtered browsing history, and the target push content corresponding to the target push parameters is determined. The target push content is pushed, and the user's shopping needs can be determined based on the user's browsing history to push the corresponding product content, thereby improving the efficiency of online shopping.

[0122] Figure 5 This is a block diagram of the functional units of the big data-based product push system 500 involved in the embodiment of the present application. The big data-based product push system 500 is applied to electronic devices, and the system 500 includes: an acquisition unit

[0123] 501, a first determining unit 502, a second determining unit 503 and a pushing unit 504, wherein:

[0124] The acquisition unit 501 is configured to acquire a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time;

[0125] The first determining unit 502 is configured to determine target push parameters of the target product according to the browsing history;

[0126] The second determining unit 503 is configured to determine target push content corresponding to the target push parameter;

[0127] The pushing unit 504 is configured to push the target push content.

[0128] Optionally, when the number of browsing records is n, n is an integer greater than 1. In determining the target push parameter of the target product according to the browsing records, the first determining unit 502 is specifically configured to:

[0129] Obtaining the product prices corresponding to the n browsing records to obtain n product prices;

[0130] Obtain product evaluation parameters corresponding to the n browsing records to obtain n groups of product evaluation parameters;

[0131] Determine a commodity price range adjustment factor based on the n groups of commodity evaluation parameters to obtain a target adjustment factor;

[0132] A target push price range is determined based on the target adjustment factor and the n commodity prices.

[0133] Optionally, the product evaluation parameters include the number of reviews and the rate of favorable comments. In determining the product price range adjustment factor based on the n groups of product evaluation parameters to obtain the target adjustment factor, the first determining unit 502 is specifically configured to:

[0134] Construct n coordinate points based on the n sets of product evaluation parameters, where the horizontal axis of each coordinate point represents the number of reviews and the vertical axis represents the praise rate;

[0135] Performing fitting based on the n coordinate points to obtain a fitted straight line, the fitted straight line including a first endpoint and a second endpoint, the first endpoint corresponding to the minimum number of reviews in the n sets of product evaluation parameters, and the second endpoint corresponding to the maximum number of reviews in the n sets of product evaluation parameters;

[0136] Determine the mean of the number of reviews in the n groups of product evaluation parameters to obtain an average number of reviews;

[0137] Determine the average of the favorable reviews among the n groups of product evaluation parameters to obtain an average favorable review rate;

[0138] Draw a first straight line according to the average number of comments, where the first straight line is parallel to the y-axis and passes through the coordinate point (average number of comments, 0);

[0139] Draw a second straight line based on the average positive review rate, where the second straight line is parallel to the x-axis and passes through the coordinate point (0, average positive review rate);

[0140] Constructing a first triangle and a second triangle based on the first straight line, the second straight line, and the first endpoint and the second endpoint of the fitted straight line, wherein the first triangle includes the second endpoint or one of the first endpoints and the intersection of the first straight line and the second straight line, and the second triangle includes the first endpoint and the second endpoint;

[0141] The target adjustment factor is determined according to the area of the first triangle and the area of the second triangle.

[0142] Optionally, in determining the target push price range according to the target adjustment factor and the n commodity prices, the first determining unit 502 is specifically configured to:

[0143] Determine the product browsing time corresponding to each of the n product prices to obtain n product browsing times;

[0144] Determining weights corresponding to the prices of the n products according to the browsing time of the n products to obtain n weights;

[0145] Performing a weighted calculation based on the n commodity prices and the n weights to obtain a reference commodity price;

[0146] The target push price range is determined according to the target adjustment factor and the reference commodity price.

[0147] Optionally, in determining the target push content corresponding to the target push parameter, the second determining unit 503 is specifically configured to:

[0148] Determine P push content corresponding to the target product, where P is an integer greater than 1;

[0149] The P pieces of pushed content are screened according to the target push parameter to obtain Q pieces of pushed content, and the Q pieces of pushed content are used as the target pushed content, where Q is an integer less than or equal to P.

[0150] It can be seen that the big data-based product push system described in the embodiment of the present application obtains the user's browsing history for the target product within a preset time period, and the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time. The browsing history is filtered according to the product browsing time, and the target push parameters of the target product are determined according to the filtered browsing history, and the target push content corresponding to the target push parameters is determined. The target push content is pushed, and the user's shopping needs can be determined based on the user's browsing history to push the corresponding product content, thereby improving the efficiency of online shopping.

[0151] It can be understood that the functions of each program module of the big data-based product push system of this embodiment can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0152] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0153] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0154] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0155] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0157] The units described above as separate components may or may not be physically separate.

[0158] The components shown may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0160] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0161] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0162] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of the present application. At the same time, for those skilled in the art, according to the idea of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A commodity push method based on big data, characterized in that: The method comprises: Obtaining a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time; Determining target push parameters of the target product based on the browsing history; Determining target push content corresponding to the target push parameters; Pushing the target push content; When the number of browsing records is n, where n is an integer greater than 1, determining the target push parameter of the target product according to the browsing records includes: Obtaining the product prices corresponding to the n browsing records to obtain n product prices; Obtain product evaluation parameters corresponding to the n browsing records to obtain n groups of product evaluation parameters; Determine a commodity price range adjustment factor based on the n groups of commodity evaluation parameters to obtain a target adjustment factor; Determining a target push price range based on the target adjustment factor and the n commodity prices; The product evaluation parameters include the number of reviews and the rate of favorable comments. Determining the product price range adjustment factor based on the n groups of product evaluation parameters to obtain the target adjustment factor includes: Construct n coordinate points based on the n sets of product evaluation parameters, where the horizontal axis of each coordinate point represents the number of reviews and the vertical axis represents the praise rate; Performing fitting based on the n coordinate points to obtain a fitted straight line, the fitted straight line including a first endpoint and a second endpoint, the first endpoint corresponding to the minimum number of reviews in the n sets of product evaluation parameters, and the second endpoint corresponding to the maximum number of reviews in the n sets of product evaluation parameters; Determine the mean of the number of reviews in the n groups of product evaluation parameters to obtain an average number of reviews; Determine the average of the favorable reviews among the n groups of product evaluation parameters to obtain an average favorable review rate; Draw a first straight line according to the average number of comments, where the first straight line is parallel to the y-axis and passes through the coordinate point (average number of comments, 0); Draw a second straight line based on the average positive review rate, wherein the second straight line is parallel to the x-axis and passes through the coordinate point (0, average positive review rate); Constructing a first triangle and a second triangle based on the first straight line, the second straight line, and the first endpoint and the second endpoint of the fitted straight line, wherein the first triangle includes the second endpoint or one of the first endpoints, the intersection of the first straight line and the second straight line, and the intersection of the line connecting the first endpoint and the second endpoint and the second straight line; and the second triangle includes the first endpoint and the second endpoint, and the intersection of a fourth straight line drawn from the first endpoint and parallel to the x-axis and a third straight line drawn from the second endpoint and parallel to the y-axis; determining the target adjustment factor according to the area of the first triangle and the area of the second triangle; The determining a target push price range according to the target adjustment factor and the n commodity prices includes: Determining a product browsing duration corresponding to each of the n product prices to obtain n product browsing durations; determining weights corresponding to the n product prices based on the n product browsing durations to obtain n weights; Performing a weighted calculation based on the n commodity prices and the n weights to obtain a reference commodity price; The target push price range is determined according to the target adjustment factor and the reference commodity price.

2. The method for pushing products based on big data according to claim 1, characterized in that: The determining the target push content corresponding to the target push parameter includes: Determine P push content corresponding to the target product, where P is an integer greater than 1; The P pieces of pushed content are screened according to the target push parameter to obtain Q pieces of pushed content, and the Q pieces of pushed content are used as the target pushed content, where Q is an integer less than or equal to P.

3. A commodity push system based on big data, characterized in that: The system includes: an acquisition unit, a first determination unit, a second determination unit and a push unit, wherein: The acquisition unit is configured to acquire a user's browsing history for a target product within a preset time period, wherein the browsing history includes at least one of the following: product brand, product specifications, product price, product evaluation parameters, and product browsing time; The first determining unit is configured to determine target push parameters of the target product based on the browsing history; The second determining unit is configured to determine target push content corresponding to the target push parameter; The pushing unit is used to push the target push content; When the number of browsing records is n, where n is an integer greater than 1, in determining the target push parameter of the target product according to the browsing records, the first determining unit is specifically configured to: Obtaining the product prices corresponding to the n browsing records to obtain n product prices; Obtain product evaluation parameters corresponding to the n browsing records to obtain n groups of product evaluation parameters; Determine a commodity price range adjustment factor based on the n groups of commodity evaluation parameters to obtain a target adjustment factor; Determining a target push price range based on the target adjustment factor and the n commodity prices; The product evaluation parameters include the number of reviews and the rate of favorable comments. In determining the product price range adjustment factor based on the n groups of product evaluation parameters to obtain the target adjustment factor, the first determining unit is specifically configured to: Construct n coordinate points based on the n sets of product evaluation parameters, where the horizontal axis of each coordinate point represents the number of reviews and the vertical axis represents the praise rate; Performing fitting based on the n coordinate points to obtain a fitted straight line, the fitted straight line including a first endpoint and a second endpoint, the first endpoint corresponding to the minimum number of reviews in the n sets of product evaluation parameters, and the second endpoint corresponding to the maximum number of reviews in the n sets of product evaluation parameters; Determine the mean of the number of reviews in the n groups of product evaluation parameters to obtain an average number of reviews; Determine the average of the favorable reviews among the n groups of product evaluation parameters to obtain an average favorable review rate; Draw a first straight line according to the average number of comments, where the first straight line is parallel to the y-axis and passes through the coordinate point (average number of comments, 0); Draw a second straight line based on the average positive review rate, wherein the second straight line is parallel to the x-axis and passes through the coordinate point (0, average positive review rate); Constructing a first triangle and a second triangle based on the first straight line, the second straight line, and the first endpoint and the second endpoint of the fitted straight line, wherein the first triangle includes the second endpoint or one of the first endpoints, the intersection of the first straight line and the second straight line, and the intersection of the line connecting the first endpoint and the second endpoint and the second straight line; and the second triangle includes the first endpoint and the second endpoint, and the intersection of a fourth straight line drawn from the first endpoint and parallel to the x-axis and a third straight line drawn from the second endpoint and parallel to the y-axis; The target adjustment factor is determined according to the area of the first triangle and the area of the second triangle.

4. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 2.

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