CN116523594B - Commodity selecting method based on multi-source detection data - Google Patents
Commodity selecting method based on multi-source detection data Download PDFInfo
- Publication number
- CN116523594B CN116523594B CN202310451082.0A CN202310451082A CN116523594B CN 116523594 B CN116523594 B CN 116523594B CN 202310451082 A CN202310451082 A CN 202310451082A CN 116523594 B CN116523594 B CN 116523594B
- Authority
- CN
- China
- Prior art keywords
- preset
- product
- frequency
- recommended
- condition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 83
- 238000001514 detection method Methods 0.000 title claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000006872 improvement Effects 0.000 abstract description 2
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000012216 screening Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004091 panning Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0641—Shopping interfaces
Abstract
The invention relates to the technical field of product recommendation, in particular to a commodity selecting method based on multi-source detection data, which comprises the following steps of S1, initially recommending products according to different user types by a product initial recommending page; step S2, the central control module judges whether the suitability of product recommendation is in an allowable range according to the refresh frequency in the unit browsing time length, and adjusts the product category recommendation quantity ratio of the initial recommendation page to a first corresponding ratio; step S3, the central control module adjusts the product recommendation frequency to the corresponding recommendation frequency according to the interval duration of the product order; step S4, the central control module secondarily adjusts the recommended quantity ratio of the product types to a second corresponding ratio according to the difference value between the quantity ratio of returned goods in the unit period and the preset quantity ratio of returned goods; the invention realizes the improvement of the product recommendation accuracy.
Description
Technical Field
The invention relates to the technical field of product recommendation, in particular to a commodity selecting method based on multi-source detection data.
Background
With the popularization of electronic products, users begin to purchase more commodities on the internet, for example, electronic commerce such as panning and the like purchase the commodities, the pushing modes of the products are pushing target groups through preset advertisement contents and are suitable for pushing physical products, so that the pushing users are also searching for objects with certain will of related vocabulary entries, but the mode is not suitable for pushing virtual products, the favorites of different users on the virtual commodity contents are different, and the pushing propagation of the virtual commodities is poor due to the virtual property that the virtual commodity contents cannot be displayed excessively, so that the traditional pushing modes are often poor in effect and consume more pushing flow to do useless pushing.
Chinese patent publication No.: CN115511576a discloses a product pushing method based on multi-source data, which comprises a content heat module, a content label module, a pushing generation module and a pushing output module; the intelligent virtual commodity screening and pushing device is mainly used for pushing virtual products, can be used for platforms with virtual product selling functions and the like, and can realize intelligent screening and pushing of the virtual commodities through feedback of users in the use and browsing processes of the virtual commodities. It can be seen that the multi-source data based product pushing method has the following problems: the imbalance between the suitability of product recommendation and product quality due to browsing pages has a shadow on product recommendation accuracy and precision.
Disclosure of Invention
Therefore, the invention provides a commodity selecting method based on multi-source detection data, which is used for solving the problem that the product recommending accuracy and precision are influenced by the unbalance of the suitability of the product recommendation of the browsed page and the product quality in the prior art.
In order to achieve the above object, the present invention provides a commodity selecting method based on multi-source detection data, including: step S1, a product initial recommendation page carries out initial recommendation on products according to different user types; step S2, the central control module judges whether the suitability of product recommendation is in an allowable range according to the refresh frequency in the unit browsing time length, and adjusts the product category recommendation quantity ratio of the initial recommendation page to a first corresponding ratio; step S3, the central control module adjusts the product recommendation frequency to the corresponding recommendation frequency according to the interval duration of the product order; and S4, when the central control module finishes the adjustment of the product recommendation frequency, judging whether the product recommendation quality is in an allowable range according to the rate of the quantity of returned goods in the unit period, and secondarily adjusting the rate of the quantity of recommended product types to a second corresponding rate according to the difference between the rate of the quantity of returned goods in the unit period and the preset rate of the quantity of returned goods.
Further, the central control module determines whether the suitability of the product recommendation is within the allowable range according to the refresh frequency in the unit browsing time length, wherein,
the first type of judging method is that the central control module judges that the suitability of product recommendation is in an allowable range under the condition of presetting a first refreshing frequency;
the second type of judging method is that the central control module judges that the suitability of the product recommendation is lower than the allowable range under the condition of presetting a second refreshing frequency, preliminarily judges that the rationality of the product recommendation is lower than the allowable range, and judges whether the rationality of the product recommendation is lower than the allowable range for the second time according to the interval duration of the product order;
the third type of judging method is that the central control module judges that the suitability of product recommendation is lower than an allowable range under the condition of presetting a third refreshing frequency, and adjusts the product category recommendation quantity ratio of an initial recommended page to a first corresponding ratio according to the difference value of the refreshing frequency in unit browsing time and the preset second refreshing frequency;
the preset first refresh frequency condition is that the refresh frequency in unit browsing time length is smaller than or equal to the preset first refresh frequency; the preset second refresh frequency condition is that the refresh frequency in the unit browsing time length is larger than the preset first refresh frequency and smaller than or equal to the preset second refresh frequency; the preset third refresh frequency condition is that the refresh frequency in the unit browsing time length is larger than the preset second refresh frequency; the preset first refresh frequency is less than the preset second refresh frequency.
Further, the calculation formula of the recommended quantity ratio of the product types is as follows:
wherein P is the recommended quantity ratio of the product types, W 1 The maximum recommended product quantity, W, for the initial recommended page Total (S) The total number of recommended products for the initial recommended page.
Further, the central control module determines three adjustment methods of the recommended quantity ratio of the product types aiming at the initial recommended page according to the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency under the preset third refresh frequency condition, wherein,
the first adjusting method is that the central control module adjusts the product category recommended quantity ratio of the initial recommended page to a preset ratio under the condition of presetting a first refresh frequency difference value;
the second adjusting method is that the central control module adjusts the recommended quantity duty ratio of the product types to a first duty ratio by using a preset first duty ratio adjusting coefficient under the condition of presetting a second refresh frequency difference value;
the third adjusting method is that the central control module adjusts the recommended quantity duty ratio of the product types to a second duty ratio by using a preset second duty ratio adjusting coefficient under the condition of presetting a third refresh frequency difference value;
the preset first frequency difference condition is that the difference between the refresh frequency in the unit browsing time length and the preset second refresh frequency is smaller than or equal to the preset first frequency difference; the preset second frequency difference condition is that the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency is larger than the preset first frequency difference value and smaller than or equal to the preset second frequency difference value; the preset third frequency difference condition is that the difference between the refresh frequency in the unit browsing time length and the preset second refresh frequency is larger than the preset second frequency difference; the preset first frequency difference value is smaller than the preset second frequency difference value, and the preset first duty ratio adjustment coefficient is smaller than the preset second duty ratio adjustment coefficient.
Further, the central control module determines whether the rationality of product recommendation is in two secondary judging methods of the allowable range according to the interval duration of the product order under the condition of presetting a second refreshing frequency, wherein,
the first secondary judging method is that the central control module judges that the rationality of product recommendation is lower than the allowable range for the second time under the condition of a preset first time, and the recommended frequency of the product is adjusted to the corresponding frequency by calculating the difference value between the preset interval duration and the interval duration of the product order;
the second secondary judgment method is that the central control module secondarily judges that the rationality of product recommendation is in an allowable range under the condition of presetting a second duration;
the preset first time length condition is that the interval time length of the product order is smaller than or equal to the preset interval time length; the preset second time length condition is that the interval time length of the product order is longer than the preset interval time length.
Further, the central control module determines three adjustment methods for the recommended frequency of the product according to the difference value between the preset interval duration and the interval duration of the product order under the preset first time strip condition, wherein,
the first frequency adjusting method is that the central control module adjusts the recommended frequency of the product to a preset frequency under the condition of a preset first time length difference value;
The second frequency adjusting method is that the central control module adjusts the recommended frequency of the product to the first recommended frequency by using a preset second frequency adjusting coefficient under the condition of presetting a second duration difference value;
the third frequency adjusting method is that the central control module adjusts the recommended frequency of the product to a second recommended frequency by using a preset first frequency adjusting coefficient under the condition of presetting a third duration difference value;
the preset first time length difference condition is that the difference value between the preset interval time length and the interval time length of the product order is smaller than or equal to the preset first time length difference value; the preset second time length difference condition is that the difference between the preset interval time length and the interval time length of the product order is larger than the preset first time length difference and smaller than the preset second time length difference; the preset third time length difference condition is that the difference between the preset interval time length and the interval time length of the product order is larger than the preset second time length difference; the preset first time length difference value is smaller than the preset second time length difference value, and the preset first frequency adjustment coefficient is smaller than the preset second frequency adjustment coefficient.
Further, the central control module determines two types of judging modes of whether the recommended quality of the product is in an allowable range according to the rate of the returned quantity in a unit period when the adjustment of the recommended frequency of the product is completed, wherein,
The first quality judgment mode is that the central control module judges that the recommended quality is in an allowable range under the condition of presetting a first return quantity ratio;
the second type quality judging mode is that the central control module judges that the recommended quality is lower than the allowable range under the condition of the preset second returned quantity proportion, and secondarily adjusts the recommended quantity proportion of the product types according to the difference value of the returned quantity proportion in the unit period and the preset returned quantity proportion;
the first return quantity ratio is preset, wherein the return quantity ratio in the unit period is smaller than or equal to the preset return quantity ratio; the preset second return quantity ratio condition is that the return quantity ratio in the unit period is larger than the preset return quantity ratio.
Further, the central control module recommends three types of secondary adjustment modes of the quantity ratio according to the difference value of the quantity ratio of returned goods in the unit period and the preset quantity ratio of returned goods for the product types, wherein,
the first secondary adjustment mode is that the central control module adjusts the recommended quantity duty ratio of the product types to a preset duty ratio under the condition of presetting a first return duty ratio difference value;
the second type of secondary adjustment mode is that the central control module uses a preset third duty ratio adjustment coefficient to secondarily adjust the recommended quantity duty ratio of the product types to a third duty ratio under the condition of presetting a second return duty ratio difference value;
The third type of secondary adjustment mode is that the central control module uses a preset fourth duty ratio adjustment coefficient to secondarily adjust the recommended quantity duty ratio of the product types to the fourth duty ratio under the condition of presetting a third return duty ratio difference value;
the difference value between the number of returned goods in the unit period and the preset number of returned goods is smaller than or equal to the difference value of the preset first returned goods ratio; the preset second return duty ratio difference condition is that the difference between the return number duty ratio and the preset return number duty ratio in the unit period is larger than the preset first return duty ratio difference and smaller than or equal to the preset second return duty ratio difference; the preset third return duty ratio difference condition is that the difference between the return quantity duty ratio in the unit period and the preset return quantity duty ratio is larger than the preset second return duty ratio difference; the preset first return duty ratio difference is smaller than the preset second return duty ratio difference, and the preset third duty ratio adjustment coefficient is smaller than the preset fourth duty ratio adjustment coefficient.
Further, the calculation formula of the return quantity in the unit period is as follows:
wherein A is the rate of the returned goods quantity in the unit period, M is the total returned goods quantity in the unit period, W Total (S) The total number of recommended products for the initial recommended page.
Further, if the non-registered user enters the browsing page in a link clicking mode, the central control module judges that the product type recommendation proportion of the linked product is increased.
Compared with the prior art, the commodity selecting method has the beneficial effects that the users entering the browsing module are classified after being judged by setting the steps S1 to S4, the users are divided into purchased users, registered non-purchased users and non-registered users through the registration time, different recommended product proportions are provided for different types of users, and the success rate of product orders is increased to a certain extent; the central control module adjusts the product recommendation types of the recommended pages according to the refresh frequency in the unit browsing time length, judges the rationality of product recommendation according to the interval time length of the product orders, adjusts the occurrence frequency of the corresponding products to a reasonable frequency, and provides various possibilities for the selection of users; the central control module performs secondary adjustment on the recommended quantity ratio of the product types according to the returned quantity ratio in the unit period; the accuracy of product recommendation is improved.
Further, according to the commodity selecting method, the preset first refreshing frequency and the preset second refreshing frequency are set, the central control module judges the suitability of product recommendation according to the refreshing frequency in unit browsing time, and the increase of the refreshing frequency caused by low suitability of product recommendation is reduced. The accuracy of product recommendation is further improved.
Further, according to the commodity selecting method, the proportion of products is adjusted through the frequency difference value of the middle module, the proportion of products which are not needed is reduced, and the proportion of products which are needed by customers is increased by setting the preset first frequency difference value, the preset second frequency difference value, the preset first duty ratio adjusting coefficient and the preset second duty ratio adjusting coefficient. The accuracy of product recommendation is further improved.
Further, according to the commodity selecting method, the preset interval duration is set, the central control module judges the rationality of product recommendation through the preset interval duration, and the problem of poor rationality of product recommendation caused by too short interval duration is solved. The accuracy of product recommendation is further improved.
Further, in the commodity selecting method, the central control module adjusts the recommended frequency of the product according to the difference value of the purchasing time length by setting a preset first time length difference value, a preset second time length difference value, a preset first frequency adjusting coefficient and a preset second frequency adjusting coefficient. The dissatisfaction degree of the consumers on the recommended content caused by the recommendation with too short purchase time is reduced, and the accuracy of product recommendation is further improved.
Further, according to the commodity selecting method, the central control module judges the recommended quality of the product by setting the preset return quantity ratio, so that the problem of poor user experience caused by the fact that the quality of the product does not reach the standard is solved, and the accuracy of product recommendation is further improved.
Further, according to the commodity selecting method, the preset first goods returning ratio difference value, the preset second goods returning ratio difference value, the preset third duty ratio adjusting coefficient and the preset fourth duty ratio adjusting coefficient are set, the central control module carries out secondary adjustment on the recommended quantity duty ratio of the types of the products through the goods returning ratio difference value, the condition that the recommended quality of the products is lower than the allowable range is reduced, and the accuracy of product recommendation is further improved.
Furthermore, the commodity selecting method increases the product category recommending proportion of the linked products through the central control module, increases the browsing time of non-registered users, and further improves the accuracy of product recommending.
Drawings
FIG. 1 is an overall flow chart of a commodity selecting method based on multi-source detection data according to an embodiment of the present invention;
FIG. 2 is a flowchart showing a method for selecting a commodity based on multi-source detection data according to an embodiment of the present invention;
FIG. 3 is a flowchart showing a method for selecting a commodity based on multi-source detection data according to an embodiment of the present invention;
fig. 4 is a specific flowchart of step S4 of the commodity selecting method based on multi-source detection data according to the embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
Fig. 1, fig. 2, fig. 3, and fig. 4 show an overall flowchart of a commodity selecting method based on multi-source detection data, a specific flowchart of step S2, a specific flowchart of step S3, and a specific flowchart of step S4 according to an embodiment of the present invention. The embodiment of the invention discloses a commodity selecting method based on multi-source detection data, which comprises the following steps:
step S1, a product initial recommendation page carries out initial recommendation on products according to different user types;
Step S2, the central control module judges whether the suitability of product recommendation is in an allowable range according to the refresh frequency in the unit browsing time length, and adjusts the product category recommendation quantity ratio of the initial recommendation page to a first corresponding ratio;
step S3, the central control module adjusts the product recommendation frequency to the corresponding recommendation frequency according to the interval duration of the product order;
and S4, when the central control module finishes the adjustment of the product recommendation frequency, judging whether the product recommendation quality is in an allowable range according to the rate of the quantity of returned goods in the unit period, and secondarily adjusting the rate of the quantity of recommended product types to a second corresponding rate according to the difference between the rate of the quantity of returned goods in the unit period and the preset rate of the quantity of returned goods.
Specifically, the step S2 includes:
step S21, the central control module judges whether the suitability of product recommendation is in an allowable range or not according to the refresh frequency in the unit browsing time length;
step S22, the central control module adjusts the product category recommended quantity ratio of the initial recommended page to a first corresponding ratio by calculating the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency.
Specifically, the step S3 includes:
Step S31, the central control module judges whether the rationality of product recommendation is in an allowable range or not for the second time according to the interval time of the product order;
in step S32, the central control module calculates a difference between the preset interval duration and the interval duration of the product order to adjust the product recommendation frequency to the corresponding frequency.
Specifically, the step S4 includes:
step S41, the central control module judges whether the recommended quality of the product is in an allowable range according to the rate of the returned quantity in the unit period;
in step S42, the central control module secondarily adjusts the product category recommended quantity ratio to a second corresponding ratio by calculating a difference between the return quantity ratio in the unit period and the preset return quantity ratio.
According to the commodity selecting method, through setting the steps S1 to S4, the users entering the browsing module are classified after being judged, the users are classified into purchased users, registered non-purchased users and unregistered users through registration time, different recommended product proportions are provided for different types of users, the possibility that the users are interested in products is increased, and the yield of the products is increased to a certain extent; the central control module adjusts the product types of the recommended pages according to the refreshing frequency in the unit browsing time length, judges the rationality of product recommendation according to the interval time length of the product orders, adjusts the occurrence frequency of the corresponding products to a reasonable frequency, and provides various possibilities for the selection of users; the central control module performs secondary adjustment on the product category recommended quantity ratio according to the quantity ratio of returned products in a unit period, so that the condition that customers purchase products for returning and exchanging products is reduced; the accuracy of product recommendation is improved.
Further, the central control module determines whether the suitability of the product recommendation is within the allowable range according to the refresh frequency in the unit browsing time length, wherein,
the first type of judging method is that the central control module judges that the suitability of product recommendation is in an allowable range under the condition of presetting a first refreshing frequency;
the second type of judging method is that the central control module judges that the suitability of the product recommendation is lower than the allowable range under the condition of presetting a second refreshing frequency, preliminarily judges that the rationality of the product recommendation is lower than the allowable range, and judges whether the rationality of the product recommendation is lower than the allowable range for the second time according to the interval duration of the product order;
the third type of judging method is that the central control module judges that the suitability of product recommendation is lower than an allowable range under the condition of presetting a third refreshing frequency, and adjusts the product category recommendation quantity ratio of an initial recommended page to a first corresponding ratio according to the difference value of the refreshing frequency in unit browsing time and the preset second refreshing frequency;
the preset first refresh frequency condition is that the refresh frequency in unit browsing time length is smaller than or equal to the preset first refresh frequency; the preset second refresh frequency condition is that the refresh frequency in the unit browsing time length is larger than the preset first refresh frequency and smaller than or equal to the preset second refresh frequency; the preset third refresh frequency condition is that the refresh frequency in the unit browsing time length is larger than the preset second refresh frequency; the preset first refresh frequency is less than the preset second refresh frequency.
Specifically, the refresh frequency in the unit browsing duration is denoted as Q, the preset first refresh frequency is denoted as Q1, the preset second refresh frequency is denoted as Q2, where Q1 < Q2, the difference between the refresh frequency in the unit browsing duration and the preset second refresh frequency is denoted as Δq, and Δq=q-Q2 is set.
According to the commodity selecting method, the preset first refreshing frequency and the preset second refreshing frequency are set, the central control module judges the suitability of product recommendation according to the refreshing frequency in unit browsing time, and the increase of the refreshing frequency caused by low suitability of product recommendation is reduced. The accuracy of product recommendation is further improved.
Further, the calculation formula of the recommended quantity ratio of the product types is as follows:
wherein, P is the recommended quantity ratio of the product types, W 1 The maximum recommended product quantity, W, for the initial recommended page Total (S) The total number of recommended products for the initial recommended page.
Further, the central control module determines three adjustment methods of the recommended quantity ratio of the product types aiming at the initial recommended page according to the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency under the preset third refresh frequency condition, wherein,
The first adjusting method is that the central control module adjusts the product category recommended quantity ratio of the initial recommended page to a preset ratio under the condition of presetting a first refresh frequency difference value;
the second adjusting method is that the central control module adjusts the recommended quantity duty ratio of the product types to a first duty ratio by using a preset first duty ratio adjusting coefficient under the condition of presetting a second refresh frequency difference value;
the third adjusting method is that the central control module adjusts the recommended quantity duty ratio of the product types to a second duty ratio by using a preset second duty ratio adjusting coefficient under the condition of presetting a third refresh frequency difference value;
the preset first frequency difference condition is that the difference between the refresh frequency in the unit browsing time length and the preset second refresh frequency is smaller than or equal to the preset first frequency difference; the preset second frequency difference condition is that the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency is larger than the preset first frequency difference value and smaller than or equal to the preset second frequency difference value; the preset third frequency difference condition is that the difference between the refresh frequency in the unit browsing time length and the preset second refresh frequency is larger than the preset second frequency difference; the preset first frequency difference value is smaller than the preset second frequency difference value, and the preset first duty ratio adjustment coefficient is smaller than the preset second duty ratio adjustment coefficient.
Specifically, the preset first frequency difference is denoted as Δq1, the preset second frequency difference is denoted as Δq2, the preset first duty cycle adjustment coefficient is denoted as α1, the preset second duty cycle adjustment coefficient is denoted as α2, the preset product category recommended number duty cycle is denoted as P0, wherein Δq1 < Δq2,0 < α1 < α2 < 1, the adjusted product category recommended number duty cycle is denoted as P ', P' =p0× (1- αi), wherein αi is the i-th duty cycle adjustment coefficient, and i=1, 2 are set.
According to the commodity selecting method, the proportion of products is adjusted through the difference of the frequencies, the proportion of products which are not needed is reduced, and the proportion of products which are needed by customers is increased by setting the preset first frequency difference value, the preset second frequency difference value, the preset first duty ratio adjusting coefficient and the preset second duty ratio adjusting coefficient. The accuracy of product recommendation is further improved.
Further, the central control module determines whether the rationality of product recommendation is in two secondary judging methods of the allowable range according to the interval duration of the product order under the condition of presetting a second refreshing frequency, wherein,
the first secondary judging method is that the central control module judges that the rationality of product recommendation is lower than the allowable range for the second time under the condition of a preset first time, and the recommended frequency of the product is adjusted to the corresponding frequency by calculating the difference value between the preset interval duration and the interval duration of the product order;
The second secondary judgment method is that the central control module secondarily judges that the rationality of product recommendation is in an allowable range under the condition of presetting a second duration;
the preset first time length condition is that the interval time length of the product order is smaller than or equal to the preset interval time length; the preset second time length condition is that the interval time length of the product order is longer than the preset interval time length.
Specifically, the time interval of the product order is denoted as T, the preset interval duration is denoted as T0, the difference between the preset interval duration and the time interval of the product order is denoted as Δt, and Δt=t0-T is set.
According to the commodity selecting method, the preset interval time is set, the central control module judges the rationality of product recommendation through the preset interval time, and the problem of poor rationality of product recommendation caused by too short interval time is solved. The accuracy of product recommendation is further improved.
Further, the central control module determines three adjustment methods for the recommended frequency of the product according to the difference value between the preset interval duration and the interval duration of the product order under the preset first time strip condition, wherein,
the first frequency adjusting method is that the central control module adjusts the recommended frequency of the product to a preset frequency under the condition of a preset first time length difference value;
The second frequency adjusting method is that the central control module adjusts the recommended frequency of the product to the first recommended frequency by using a preset second frequency adjusting coefficient under the condition of presetting a second duration difference value;
the third frequency adjusting method is that the central control module adjusts the recommended frequency of the product to a second recommended frequency by using a preset first frequency adjusting coefficient under the condition of presetting a third duration difference value;
the preset first time length difference condition is that the difference value between the preset interval time length and the interval time length of the product order is smaller than or equal to the preset first time length difference value; the preset second time length difference condition is that the difference between the preset interval time length and the interval time length of the product order is larger than the preset first time length difference and smaller than the preset second time length difference; the preset third time length difference condition is that the difference between the preset interval time length and the interval time length of the product order is larger than the preset second time length difference; the preset first time length difference value is smaller than the preset second time length difference value, and the preset first frequency adjustment coefficient is smaller than the preset second frequency adjustment coefficient.
Specifically, the preset first time difference value is denoted as Δt1, the preset second time difference value is denoted as Δt2, the preset first frequency adjustment coefficient is denoted as β1, the preset second frequency adjustment coefficient is denoted as β2, the recommended frequency of the product is denoted as H0, wherein Δt1 < [ Δt2 ], 0 < β1 < β2 < 1, the recommended frequency of the adjusted product is denoted as H ', H' =h0×βj is set, wherein βj is the preset j-th frequency adjustment coefficient, j=1, 2.
According to the commodity selecting method, the preset first time length difference value, the preset second time length difference value, the preset first frequency adjusting coefficient and the preset second frequency adjusting coefficient are set, the central control module adjusts the recommended frequency of the product according to the purchase time length difference value, the dissatisfaction degree of consumers on recommended content due to the fact that the purchase time is too short is reduced, and the accuracy of product recommendation is further improved.
Further, the central control module determines two types of judging modes of whether the recommended quality of the product is in an allowable range according to the rate of the returned quantity in a unit period when the adjustment of the recommended frequency of the product is completed, wherein,
the first quality judgment mode is that the central control module judges that the recommended quality is in an allowable range under the condition of presetting a first return quantity ratio;
the second type quality judging mode is that the central control module judges that the recommended quality is lower than the allowable range under the condition of the preset second returned quantity proportion, and secondarily adjusts the recommended quantity proportion of the product types according to the difference value of the returned quantity proportion in the unit period and the preset returned quantity proportion;
the first return quantity ratio is preset, wherein the return quantity ratio in the unit period is smaller than or equal to the preset return quantity ratio; the preset second return quantity ratio condition is that the return quantity ratio in the unit period is larger than the preset return quantity ratio.
Specifically, the number of returns in the unit cycle is denoted as a, the preset number of returns is denoted as A0, the difference between the number of returns in the unit cycle and the preset number of returns is denoted as Δa, and Δa=a-A0 is set.
According to the commodity selecting method, the central control module judges the recommended quality of the product by setting the preset quantity of returned goods to be occupied, so that the problem of poor user experience caused by the fact that the quality of the product does not reach the standard is solved, and the accuracy of product recommendation is further improved.
Further, the central control module recommends three types of secondary adjustment modes of the quantity ratio according to the difference value of the quantity ratio of returned goods in the unit period and the preset quantity ratio of returned goods for the product types, wherein,
the first secondary adjustment mode is that the central control module adjusts the recommended quantity duty ratio of the product types to a preset duty ratio under the condition of presetting a first return duty ratio difference value;
the second type of secondary adjustment mode is that the central control module uses a preset third duty ratio adjustment coefficient to secondarily adjust the recommended quantity duty ratio of the product types to a third duty ratio under the condition of presetting a second return duty ratio difference value;
the third type of secondary adjustment mode is that the central control module uses a preset fourth duty ratio adjustment coefficient to secondarily adjust the recommended quantity duty ratio of the product types to the fourth duty ratio under the condition of presetting a third return duty ratio difference value;
The difference value between the number of returned goods in the unit period and the preset number of returned goods is smaller than or equal to the difference value of the preset first returned goods ratio; the preset second return duty ratio difference condition is that the difference between the return number duty ratio and the preset return number duty ratio in the unit period is larger than the preset first return duty ratio difference and smaller than or equal to the preset second return duty ratio difference; the preset third return duty ratio difference condition is that the difference between the return quantity duty ratio in the unit period and the preset return quantity duty ratio is larger than the preset second return duty ratio difference; the preset first return duty ratio difference is smaller than the preset second return duty ratio difference, and the preset third duty ratio adjustment coefficient is smaller than the preset fourth duty ratio adjustment coefficient.
Specifically, the preset first return duty ratio difference is denoted as Δa1, the preset second return duty ratio difference is denoted as Δa2, the preset third duty ratio adjustment coefficient is denoted as α3, the preset fourth duty ratio adjustment coefficient is denoted as α4, the recommended number of product types is denoted as P ', Δa1 < [ Δa2 ], 1 < Δa3 < α4, the recommended number of adjusted product types is denoted as P ', P ' =p0× (1+αδ), wherein αδ is the delta-th duty ratio adjustment coefficient, and δ=3, 4 are set.
According to the commodity selecting method, the preset first goods returning duty ratio difference value, the preset second goods returning duty ratio difference value, the preset third duty ratio adjusting coefficient and the preset fourth duty ratio adjusting coefficient are set, the central control module carries out secondary adjustment on the recommended quantity duty ratio of the product types through the goods returning duty ratio difference value, the condition that the recommended quality of the product is lower than the allowable range is reduced, and the accuracy of product recommendation is further improved.
Further, the calculation formula of the return quantity in the unit period is as follows:
wherein A is the rate of the returned goods quantity in the unit period, M is the total returned goods quantity in the unit period, W Total (S) The total number of recommended products for the initial recommended page.
Further, if the non-registered user enters the browsing page in a link clicking mode, the central control module judges that the product type recommendation proportion of the linked product is increased.
According to the commodity selecting method, the product type recommending proportion of the linked product is increased through the central control module, so that the browsing time of a non-registered user is increased, and the accuracy of product recommending is further improved.
Example 1
In the commodity selecting method based on the multi-source detection data, a central control module judges and classifies users entering a browsing module, the users are classified into purchased users, registered non-purchased users and unregistered users according to registration time, and the central control module screens out products close to user information to push according to registration information through a big data algorithm, so that the weight of target products is increased; if the non-registered user enters the browsing page in a link clicking mode, the central control module judges that the product type recommendation proportion of the linked product is increased.
According to the embodiment, the target users are classified by the central control module, so that personalized services of different recommended products for different types of users are realized, and the accuracy of product recommendation is further improved.
Example 2
In this embodiment 2, the central control module has three adjustment methods for the recommended quantity of product types of the initial recommended page according to the difference between the refresh frequency and the preset second refresh frequency, the preset first frequency difference is denoted as Δq1, the preset second frequency difference is denoted as Δq2, the preset first duty ratio adjustment coefficient is denoted as α1, the preset second duty ratio adjustment coefficient is denoted as α2, the recommended quantity of product types is denoted as P0, wherein Δq1=10 times/min, Δq2=20 times/min, α1=0.2, α2=0.4, and p0=0.7,
in this embodiment, Δq=15 times/min is obtained, the central control module determines that Δq1 < Δq2is not more than Δq2, and uses α1 to adjust the ratio of the recommended number of preset product types, where the ratio of the recommended number of product types after adjustment is P' =0.7× (1-0.2) =0.56.
According to the method, three adjustment modes of determining the recommended quantity ratio of the preset product types are determined by setting the preset first frequency difference value and the preset second frequency difference value, so that passenger flow loss caused by inaccurate product recommendation is reduced, and the accuracy of product recommendation is further improved.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A commodity selecting method based on multi-source detection data, comprising:
step S1, a product initial recommendation page carries out initial recommendation on products according to different user types;
step S2, the central control module judges whether the suitability of product recommendation is in an allowable range according to the refresh frequency in the unit browsing time length, and adjusts the product category recommendation quantity ratio of the initial recommendation page to a first corresponding ratio;
Step S3, the central control module adjusts the product recommendation frequency to the corresponding recommendation frequency according to the interval duration of the product order;
step S4, when the central control module finishes the adjustment of the product recommended frequency, judging whether the product recommended quality is in an allowable range according to the rate of the quantity of returned goods in a unit period, and secondarily adjusting the rate of the quantity of recommended product types to a second corresponding rate according to the difference between the rate of the quantity of returned goods in the unit period and the rate of the preset quantity of returned goods;
the central control module determines whether the suitability of product recommendation is within the allowable range according to the refresh frequency in the unit browsing time length, wherein,
the first type of judging method is that the central control module judges that the suitability of product recommendation is in an allowable range under the condition of presetting a first refreshing frequency;
the second type of judging method is that the central control module judges that the suitability of the product recommendation is lower than the allowable range under the condition of presetting a second refreshing frequency, preliminarily judges that the rationality of the product recommendation is lower than the allowable range, and judges whether the rationality of the product recommendation is lower than the allowable range for the second time according to the interval duration of the product order;
the third type of judging method is that the central control module judges that the suitability of product recommendation is lower than an allowable range under the condition of presetting a third refreshing frequency, and adjusts the product category recommendation quantity ratio of an initial recommended page to a first corresponding ratio according to the difference value of the refreshing frequency in unit browsing time and the preset second refreshing frequency;
The preset first refresh frequency condition is that the refresh frequency in unit browsing time length is smaller than or equal to the preset first refresh frequency; the preset second refresh frequency condition is that the refresh frequency in the unit browsing time length is larger than the preset first refresh frequency and smaller than or equal to the preset second refresh frequency; the preset third refresh frequency condition is that the refresh frequency in the unit browsing time length is larger than the preset second refresh frequency; the preset first refresh frequency is smaller than the preset second refresh frequency;
the central control module determines three adjustment methods of the recommended quantity ratio of the product types aiming at the initial recommended page according to the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency under the condition of the preset third refresh frequency,
the first adjusting method is that the central control module adjusts the product category recommended quantity ratio of the initial recommended page to a preset ratio under the condition of presetting a first refresh frequency difference value;
the second adjusting method is that the central control module adjusts the recommended quantity duty ratio of the product types to a first duty ratio by using a preset first duty ratio adjusting coefficient under the condition of presetting a second refresh frequency difference value;
The third adjusting method is that the central control module adjusts the recommended quantity duty ratio of the product types to a second duty ratio by using a preset second duty ratio adjusting coefficient under the condition of presetting a third refresh frequency difference value;
the preset first refresh frequency difference condition is that the difference between the refresh frequency in unit browsing time length and the preset second refresh frequency is smaller than or equal to the preset first refresh frequency difference; the preset second refresh frequency difference condition is that the difference value between the refresh frequency in the unit browsing time length and the preset second refresh frequency is larger than the preset first refresh frequency difference value and smaller than or equal to the preset second refresh frequency difference value; the preset third refresh frequency difference condition is that the difference between the refresh frequency in the unit browsing time length and the preset second refresh frequency is larger than the preset second refresh frequency difference; the preset first refresh frequency difference value is smaller than the preset second refresh frequency difference value, and the preset first duty ratio adjustment coefficient is smaller than the preset second duty ratio adjustment coefficient.
2. The commodity selecting method based on multi-source detection data according to claim 1, wherein the calculation formula of the recommended quantity ratio of the product types is:
Wherein P is the recommended quantity ratio of the product types, W 1 The maximum recommended product quantity, W, for the initial recommended page Total (S) The total number of recommended products for the initial recommended page.
3. The method of claim 2, wherein the central control module determines whether the rationality of product recommendation is within the allowable range according to the interval duration of the product order under the preset second refresh frequency condition, wherein,
the first secondary judging method is that the central control module judges that the rationality of product recommendation is lower than the allowable range for the second time under the condition of a preset first time, and the recommended frequency of the product is adjusted to the corresponding frequency by calculating the difference value between the preset interval duration and the interval duration of the product order;
the second secondary judgment method is that the central control module secondarily judges that the rationality of product recommendation is in an allowable range under the condition of presetting a second duration;
the preset first time length condition is that the interval time length of the product order is smaller than or equal to the preset interval time length; the preset second time length condition is that the interval time length of the product order is longer than the preset interval time length.
4. The method for selecting a commodity based on multi-source detection data according to claim 3, wherein said central control module determines three adjustment methods for the recommended frequency of the product based on the difference between the preset interval duration and the interval duration of the product order under the preset first time condition, wherein,
the first frequency adjusting method is that the central control module adjusts the recommended frequency of the product to a preset frequency under the condition of a preset first time length difference value;
the second frequency adjusting method is that the central control module adjusts the recommended frequency of the product to the first recommended frequency by using a preset second frequency adjusting coefficient under the condition of presetting a second duration difference value;
the third frequency adjusting method is that the central control module adjusts the recommended frequency of the product to a second recommended frequency by using a preset first frequency adjusting coefficient under the condition of presetting a third duration difference value;
the preset first time length difference condition is that the difference value between the preset interval time length and the interval time length of the product order is smaller than or equal to the preset first time length difference value; the preset second time length difference condition is that the difference between the preset interval time length and the interval time length of the product order is larger than the preset first time length difference and smaller than the preset second time length difference; the preset third time length difference condition is that the difference between the preset interval time length and the interval time length of the product order is larger than the preset second time length difference; the preset first time length difference value is smaller than the preset second time length difference value, and the preset first frequency adjustment coefficient is smaller than the preset second frequency adjustment coefficient.
5. The method of claim 4, wherein the central control module determines whether the recommended quality of the product is within an allowable range according to the ratio of the returned quantity in a unit period when the adjustment of the recommended frequency of the product is completed,
the first quality judgment mode is that the central control module judges that the recommended quality is in an allowable range under the condition of presetting a first return quantity ratio;
the second type quality judging mode is that the central control module judges that the recommended quality is lower than the allowable range under the condition of the preset second returned quantity proportion, and secondarily adjusts the recommended quantity proportion of the product types according to the difference value of the returned quantity proportion in the unit period and the preset returned quantity proportion;
the first return quantity ratio is preset, wherein the return quantity ratio in the unit period is smaller than or equal to the preset return quantity ratio; the preset second return quantity ratio condition is that the return quantity ratio in the unit period is larger than the preset return quantity ratio.
6. The method of claim 5, wherein the central control module recommends three types of secondary adjustment modes of the quantity ratio for the product types according to the difference between the quantity ratio of returned goods in a unit period and the preset quantity ratio of returned goods,
The first secondary adjustment mode is that the central control module adjusts the recommended quantity duty ratio of the product types to a preset duty ratio under the condition of presetting a first return duty ratio difference value;
the second type of secondary adjustment mode is that the central control module uses a preset third duty ratio adjustment coefficient to secondarily adjust the recommended quantity duty ratio of the product types to a third duty ratio under the condition of presetting a second return duty ratio difference value;
the third type of secondary adjustment mode is that the central control module uses a preset fourth duty ratio adjustment coefficient to secondarily adjust the recommended quantity duty ratio of the product types to the fourth duty ratio under the condition of presetting a third return duty ratio difference value;
the difference value between the number of returned goods in the unit period and the preset number of returned goods is smaller than or equal to the difference value of the preset first returned goods ratio; the preset second return duty ratio difference condition is that the difference between the return number duty ratio and the preset return number duty ratio in the unit period is larger than the preset first return duty ratio difference and smaller than or equal to the preset second return duty ratio difference; the preset third return duty ratio difference condition is that the difference between the return quantity duty ratio in the unit period and the preset return quantity duty ratio is larger than the preset second return duty ratio difference; the preset first return duty ratio difference is smaller than the preset second return duty ratio difference, and the preset third duty ratio adjustment coefficient is smaller than the preset fourth duty ratio adjustment coefficient.
7. The method for selecting commodity based on multi-source detection data according to claim 6, wherein the calculation formula of the return quantity in the unit period is:
wherein A is the rate of the returned goods quantity in the unit period, M is the total returned goods quantity in the unit period, W Total (S) The total number of recommended products for the initial recommended page.
8. The method for selecting commodity based on multi-source detection data according to claim 7, wherein if the non-registered user enters the browsing page by clicking the link, the central control module determines to increase the product category recommendation ratio of the linked product.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310451082.0A CN116523594B (en) | 2023-04-24 | 2023-04-24 | Commodity selecting method based on multi-source detection data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310451082.0A CN116523594B (en) | 2023-04-24 | 2023-04-24 | Commodity selecting method based on multi-source detection data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116523594A CN116523594A (en) | 2023-08-01 |
| CN116523594B true CN116523594B (en) | 2024-02-06 |
Family
ID=87391487
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310451082.0A Active CN116523594B (en) | 2023-04-24 | 2023-04-24 | Commodity selecting method based on multi-source detection data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116523594B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117829965A (en) * | 2024-03-01 | 2024-04-05 | 深圳欧税通技术有限公司 | Product recommendation system and method for cross-border e-commerce platform |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017016119A1 (en) * | 2015-07-29 | 2017-02-02 | 百度在线网络技术（北京）有限公司 | Recommendation method, apparatus and device based on e-commerce platform, and storage medium |
| US10157411B1 (en) * | 2014-03-13 | 2018-12-18 | Amazon Technologies, Inc. | Recommendation system that relies on RFM segmentation |
| KR102213768B1 (en) * | 2020-05-19 | 2021-02-08 | 주식회사 스타일씨코퍼레이션 | Customer-specific product recommendation system that exposes products with high purchase conversion rate based on customer information by artificial intelligence based on big data |
| CN112581238A (en) * | 2020-12-30 | 2021-03-30 | 平潭综合实验区澄心贸易有限公司 | E-commerce commodity display system and working method thereof |
| CN112750011A (en) * | 2021-01-13 | 2021-05-04 | 叮当快药科技集团有限公司 | Commodity recommendation method and device and electronic equipment |
| CN114298787A (en) * | 2021-12-23 | 2022-04-08 | 如数(上海)信息技术有限公司 | Automatic commodity recommendation method and device |
| WO2022116833A1 (en) * | 2020-12-01 | 2022-06-09 | 北京沃东天骏信息技术有限公司 | Product search method, terminal device, and server |
| CN114708070A (en) * | 2022-06-06 | 2022-07-05 | 广东鑫兴科技有限公司 | Intelligent information pushing method and device, electronic equipment and storage medium |
-
2023
- 2023-04-24 CN CN202310451082.0A patent/CN116523594B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10157411B1 (en) * | 2014-03-13 | 2018-12-18 | Amazon Technologies, Inc. | Recommendation system that relies on RFM segmentation |
| WO2017016119A1 (en) * | 2015-07-29 | 2017-02-02 | 百度在线网络技术（北京）有限公司 | Recommendation method, apparatus and device based on e-commerce platform, and storage medium |
| KR102213768B1 (en) * | 2020-05-19 | 2021-02-08 | 주식회사 스타일씨코퍼레이션 | Customer-specific product recommendation system that exposes products with high purchase conversion rate based on customer information by artificial intelligence based on big data |
| WO2022116833A1 (en) * | 2020-12-01 | 2022-06-09 | 北京沃东天骏信息技术有限公司 | Product search method, terminal device, and server |
| CN112581238A (en) * | 2020-12-30 | 2021-03-30 | 平潭综合实验区澄心贸易有限公司 | E-commerce commodity display system and working method thereof |
| CN112750011A (en) * | 2021-01-13 | 2021-05-04 | 叮当快药科技集团有限公司 | Commodity recommendation method and device and electronic equipment |
| CN114298787A (en) * | 2021-12-23 | 2022-04-08 | 如数(上海)信息技术有限公司 | Automatic commodity recommendation method and device |
| CN114708070A (en) * | 2022-06-06 | 2022-07-05 | 广东鑫兴科技有限公司 | Intelligent information pushing method and device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 考虑商品重复购买周期的协同过滤推荐方法改进;张志清;李梦;胡竹青;;武汉科技大学学报(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116523594A (en) | 2023-08-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7272573B2 (en) | Internet strategic brand weighting factor | |
| US20180197209A1 (en) | Advertising and fulfillment system | |
| US7933899B2 (en) | Dynamic bid pricing for sponsored search | |
| US7249058B2 (en) | Method of promoting strategic documents by bias ranking of search results | |
| CN116523594B (en) | Commodity selecting method based on multi-source detection data | |
| CN102902691B (en) | Recommend method and system | |
| US20040199419A1 (en) | Promoting strategic documents by bias ranking of search results on a web browser | |
| US20040078214A1 (en) | Product recommendation in a network-based commerce system | |
| US20090076886A1 (en) | Advertisement plusbox | |
| US8903796B2 (en) | Web advertising management method | |
| US20080183675A1 (en) | System for updating advertisement bids | |
| CN101853463A (en) | Collaborative filtering recommending method and system based on client characteristics | |
| CN108664564B (en) | Improved collaborative filtering recommendation method based on article content characteristics | |
| WO2008127869A1 (en) | A method and system for generating an ordered list | |
| US8769079B1 (en) | Determination and management of click values associated with visitors to web sites | |
| US9830392B1 (en) | Query-dependent and content-class based ranking | |
| Scholz et al. | Using PageRank for non-personalized default rankings in dynamic markets | |
| CN105809475A (en) | Commodity recommendation method compatible with O2O applications in internet plus tourism environment | |
| CN106970972A (en) | A kind of commodity method for pushing and device analyzed based on big data | |
| CN110930214A (en) | Commodity sorting method for full-subtractive activities | |
| CN102073737A (en) | Method and system for automatically providing real-time purchasing suggestion for network retailer | |
| US7143085B2 (en) | Optimization of server selection using euclidean analysis of search terms | |
| CN103810626A (en) | Method for online shopping and online shopping intermediary | |
| CN112150227A (en) | Commodity recommendation method, system, device and medium | |
| CN1996363A (en) | Information displaying method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |