CN112102043A - Item recommendation page generation method and device, electronic equipment and readable medium - Google Patents

Item recommendation page generation method and device, electronic equipment and readable medium Download PDF

Info

Publication number
CN112102043A
CN112102043A CN202011219704.XA CN202011219704A CN112102043A CN 112102043 A CN112102043 A CN 112102043A CN 202011219704 A CN202011219704 A CN 202011219704A CN 112102043 A CN112102043 A CN 112102043A
Authority
CN
China
Prior art keywords
value
order information
item
article
recommended
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.)
Granted
Application number
CN202011219704.XA
Other languages
Chinese (zh)
Other versions
CN112102043B (en
Inventor
卢伟涛
唐金川
黄冬冬
郭月乔
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hunan Jinzhong Customized Technology Co.,Ltd.
Original Assignee
Beijing Missfresh Ecommerce Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Missfresh Ecommerce Co Ltd filed Critical Beijing Missfresh Ecommerce Co Ltd
Priority to CN202011219704.XA priority Critical patent/CN112102043B/en
Publication of CN112102043A publication Critical patent/CN112102043A/en
Application granted granted Critical
Publication of CN112102043B publication Critical patent/CN112102043B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • 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
    • G06Q30/0643—Graphical representation of items or shoppers

Landscapes

  • Business, Economics & Management (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The embodiment of the disclosure discloses an item recommendation page generation method and device, electronic equipment and a readable medium. One embodiment of the method comprises: recommending and evaluating each piece of historical order information in a historical order information set of a target user in a preset time period to generate a recommendation degree value to obtain a recommendation degree value set; selecting historical order information with the article shelf life remaining time within a first preset range from the historical order information set as candidate order information; selecting candidate order information with the corresponding recommendation degree value in a second preset range from the candidate order information set as candidate recommended article information to obtain a candidate recommended article information set; generating a recommended item information list based on the candidate recommended item information set; and generating an item recommendation page based on the recommended item information list and a preset basic page. This embodiment facilitates the frequency with which value-related operations are performed by a target user and reduces the waste of computing resources.

Description

Item recommendation page generation method and device, electronic equipment and readable medium
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for generating an item recommendation page, electronic equipment and a readable medium.
Background
The item recommendation page generation is a technology for determining item information associated with the historical order information and generating a corresponding item recommendation page according to the historical order information of the target user. The currently common method for generating an item recommendation page is to use item information similar to historical order information browsed by a target user as recommended item information to generate an item recommendation page.
However, when the item recommendation page is generated in the above manner, the following technical problems often exist:
firstly, recommendation degree evaluation is not performed on historical order information, so that the situation that the recommendation value is not high possibly exists in the article information obtained according to the historical order information, and further, more article information which is difficult to promote the target user to execute value-related operations is contained in a recommendation page, so that the waste of computing resources is caused;
secondly, when the recommendation degree of the historical order information is evaluated, the categories of the historical orders are not subdivided, and meanwhile, factors influencing the recommendation degree evaluation of the historical order information are not comprehensively considered, so that the generated recommendation degree value is not accurate enough, and further, the recommended content is difficult to meet the appeal of a service providing platform and improve the frequency of value-related operations executed by a user, and the overstocked goods, the waste of shelf resources and the excessive consumption of goods preservation resources can be caused.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Some embodiments of the present disclosure propose an item recommendation page generation method, apparatus, electronic device, and computer readable medium to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an item recommendation page generation method, including: recommending and evaluating each piece of historical order information in a historical order information set of a target user in a preset time period to generate a recommendation degree value, and obtaining the recommendation degree value set, wherein the historical order information comprises: the method comprises the following steps of article name, article label value, average article browsing times, article value transfer numerical values, article value generation numerical values and shelf life remaining time. And selecting historical order information with the article shelf life remaining time within a first preset range from the historical order information set as candidate order information to obtain a candidate order information set. And selecting candidate order information with the corresponding recommendation degree value in a second preset range from the candidate order information set as candidate recommended article information to obtain a candidate recommended article information set. And generating a recommended article information list based on the candidate recommended article information set. And generating an item recommendation page based on the recommended item information list and a preset basic page.
In some embodiments, the performing recommendation evaluation on each historical order information in the historical order information set of the target user within a predetermined time period to generate a recommendation degree value includes:
performing recommendation evaluation on the historical order information to generate a recommendation value through the following formula:
Figure 782177DEST_PATH_IMAGE001
，
wherein,
Figure 491462DEST_PATH_IMAGE002
the value of the degree of recommendation is represented,
Figure 67937DEST_PATH_IMAGE003
indicating a value transfer value for an item included in the historical order information,
Figure 197567DEST_PATH_IMAGE004
indicating that the historical order information includes an item value transfer value fraction,
Figure 808677DEST_PATH_IMAGE005
generating a value indicative of a value of an item included in the historical order information,
Figure 365560DEST_PATH_IMAGE006
indicating the number of value transfers of the item included in the historical order information,
Figure 429331DEST_PATH_IMAGE007
indicating the average browsing times of the articles included in the historical order information,
Figure 362652DEST_PATH_IMAGE008
indicating an item tag value included in the historical order information,
Figure 579001DEST_PATH_IMAGE009
representing the amount of historical order information in the set of historical order information,
Figure 306786DEST_PATH_IMAGE010
the sequence number is shown to indicate that,
Figure 61115DEST_PATH_IMAGE011
representing a value transfer value for an item included in historical order information in the set of historical order information,
Figure 594865DEST_PATH_IMAGE012
indicating the value transfer value ratio of the goods included in the historical order information set,
Figure 649408DEST_PATH_IMAGE013
a value generating value representing a value of an item included in the historical order information in the set of historical order information,
Figure 79253DEST_PATH_IMAGE014
representing the number of value transfers of the item included in the historical order information set,
Figure 55299DEST_PATH_IMAGE015
indicating the average browsing times of the articles included in the historical order information set,
Figure 127160DEST_PATH_IMAGE016
indicating an item tag value included in the historical order information in the set of historical order information,
Figure 770631DEST_PATH_IMAGE017
indicating the first in the historical order information set
Figure 840218DEST_PATH_IMAGE010
The individual historical order information includes an item value transfer value,
Figure 615145DEST_PATH_IMAGE018
indicating the first in the historical order information set
Figure 693959DEST_PATH_IMAGE010
The historical order information includes a value transfer value for the item in proportion,
Figure 926358DEST_PATH_IMAGE019
indicating the first in the historical order information set
Figure 494742DEST_PATH_IMAGE010
The value of the item included in the individual historical order information generates a value,
Figure 445381DEST_PATH_IMAGE020
indicating the first in the historical order information set
Figure 62307DEST_PATH_IMAGE010
The historical order information includes a number of value transfers for the item,
Figure 211528DEST_PATH_IMAGE021
indicating the first in the historical order information set
Figure 154077DEST_PATH_IMAGE010
The historical order information includes an average number of views of the item,
Figure 326432DEST_PATH_IMAGE022
indicating the first in the historical order information set
Figure 543787DEST_PATH_IMAGE010
The individual historical order information includes an item tag value.
In a second aspect, some embodiments of the present disclosure provide an item recommendation page generation apparatus, including: the recommendation evaluation unit is configured to perform recommendation evaluation on each piece of historical order information in a set of historical order information of a target user within a predetermined time period to generate a recommendation degree value, so as to obtain a recommendation degree value set, wherein the historical order information includes: the method comprises the following steps of article name, article label value, average article browsing times, article value transfer numerical values, article value generation numerical values and shelf life remaining time. And the first selection unit is configured to select historical order information with the shelf life remaining time of the article being in a first preset range from the historical order information set as candidate order information to obtain a candidate order information set. And the second selection unit is configured to select candidate order information with a corresponding recommendation degree value within a second predetermined range from the candidate order information set as candidate recommended item information to obtain a candidate recommended item information set. And the first generating unit is configured to generate a recommended item information list based on the candidate recommended item information set. And the second generation unit is configured to generate an item recommendation page based on the recommended item information list and a preset basic page.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in the first aspect.
The above embodiments of the present disclosure have the following advantages: by the item recommendation page generation method of some embodiments of the disclosure, the content of the recommendation page is simplified, and meanwhile, the association degree between the recommended item information in the item recommendation page and the target user is maintained or improved. Thus, the frequency of performing value-related operations by the target user is increased and the waste of computer resources is reduced. Specifically, the inventors found that the reasons for wasting computer resources are: the recommendation degree of the historical order information is not quantitatively analyzed through recommendation degree evaluation, so that the relevance degree of the content in the recommendation page and the target user is low, and more item information which is difficult to promote the target user to perform value-related operations is contained in the recommendation page. Based on the above, the item recommendation page generation method introduces a historical order information set of the target user in a predetermined time period. In addition, the recommendation degree value of each piece of historical order information in the historical order information set is determined, so that the recommendation degree value is quantized. And then, based on the recommendation degree value set, screening out the historical order information meeting the conditions from the historical order information set to serve as recommended article information so as to generate a recommendation information list. Therefore, the relevance between the recommended article information and the target user is improved, the stability of the frequency of the value-related operation of the target user is ensured, and the waste of computing resources is reduced.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of one application scenario of an item recommendation page generation method according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an item recommendation page generation method according to the present disclosure;
FIG. 3 is a flow diagram of further embodiments of an item recommendation page generation method according to the present disclosure;
FIG. 4 is a block diagram of some embodiments of an item recommendation page generation apparatus according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of an item recommendation page generation method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may perform recommendation evaluation on each piece of historical order information in the set of historical order information 102 of the target user within a predetermined time period to generate a recommendation degree value, resulting in a recommendation degree value set 103, where the piece of historical order information includes: the method comprises the following steps of article name, article label value, average article browsing times, article value transfer numerical values, article value generation numerical values and shelf life remaining time. Secondly, the computing device 101 may select, from the historical order information set 102, historical order information that includes the remaining duration of the shelf life of the item within a first predetermined range as candidate order information, resulting in a candidate order information set 104. Then, the computing device 101 may select candidate order information with a corresponding recommendation degree value within a second predetermined range from the candidate order information set 104 as candidate recommended item information, so as to obtain a candidate recommended item information set 105. Further, the computing device 101 may generate a recommended item information list 106 based on the set 105 of candidate recommended item information. Finally, the computing device 101 may generate an item recommendation page 108 based on the recommended item information list 106 and a preset base page 107.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to FIG. 2, a flow 200 of some embodiments of an item recommendation page generation method according to the present disclosure is shown. The item recommendation page generation method comprises the following steps:
step 201, performing recommendation evaluation on each historical order information in the historical order information set of the target user in a predetermined time period to generate a recommendation degree value, so as to obtain a recommendation degree value set.
In some embodiments, an executing subject (such as the computing device 101 shown in fig. 1) of the item recommendation page generation method may perform recommendation evaluation on each historical order information in a set of historical order information of a target user within a predetermined time period by using the following formula to generate a recommendation degree value, so as to obtain a recommendation degree value set, where the historical order information may include: item name, item label value, item average number of views, item value transfer number (e.g., item purchase amount), item value transfer value (e.g., item unit price), item value transfer value fraction (e.g., item discount), item value generation value (e.g., item cost price), shelf life remaining length. The item tag value may be used to indicate how the target user obtains the item information corresponding to the item name. And the target user can be represented by '1' to obtain the item information corresponding to the item name in a self-searching mode. And 2, the target user can be represented to obtain the item information corresponding to the item name in a system recommendation mode:
Figure 16356DEST_PATH_IMAGE023
。
wherein,
Figure 864227DEST_PATH_IMAGE002
the recommendation degree value is shown.
Figure 336927DEST_PATH_IMAGE003
And the value of the value transfer of the goods included in the historical order information is represented.
Figure 561235DEST_PATH_IMAGE004
And indicating the value transfer value ratio of the goods included in the historical order information.
Figure 357153DEST_PATH_IMAGE005
And generating a value representing the value of the item included in the historical order information.
Figure 438242DEST_PATH_IMAGE006
Indicating the number of value transfers of the item included in the historical order information.
Figure 850768DEST_PATH_IMAGE007
And the average browsing times of the articles included in the historical order information are shown.
Figure 613188DEST_PATH_IMAGE008
Indicating the value of the item label included in the historical order information.
Figure 591508DEST_PATH_IMAGE024
Indicating a rounding down.
As an example, the above-mentioned historical order information set may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ scon, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ], [ pineapple shortbread, 1, 32 times, 2 boxes, 40 yuan, 9.8 folds, 20 yuan, 4 days ] ]. The recommendation degree value corresponding to the above historical order information [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ] may be 17 (the calculation process is as follows).
Figure 781181DEST_PATH_IMAGE025
。
In some optional implementation manners of some embodiments, the performing main body performs recommendation evaluation on each historical order information in the historical order information set of the target user within a predetermined time period to generate a recommendation degree value, and obtains the recommendation degree value set, and may include the following steps:
in the first step, the product value of the ratio of the item value transfer value to the item value transfer value included in the historical order information is determined as a first product value.
And secondly, determining the difference value between the item value generation value and the first product value included in the historical order information as a first difference value.
And thirdly, determining the difference value between the item value transfer value and the item value generation value included in the historical order information as a second difference value.
And fourthly, determining the product value of the number of times of transferring the goods value and the goods value generation value included in the historical order information and the first difference value as a second product value.
And fifthly, determining a recommendation degree value based on the historical order information, the first difference value, the second difference value and the second product value.
Step 202, selecting historical order information with the article shelf life remaining time within a first preset range from the historical order information set as candidate order information to obtain a candidate order information set.
In some embodiments, the executive body may select, from the historical order information set, historical order information including a shelf life remaining time of the item within a first predetermined range as candidate order information, resulting in a candidate order information set. Wherein the first predetermined range may be [4 days, 7 days ].
As an example, the above-mentioned historical order information set may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ scon, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ], [ pineapple shortbread, 1, 32 times, 2 boxes, 40 yuan, 9.8 folds, 20 yuan, 4 days ] ]. Selecting historical order information with the remaining shelf life of the article within 4 days to 7 days from the historical order information set as candidate order information, and obtaining the candidate order information set, wherein the candidate order information set can be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ sconsin, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ], [ pineapple shortbread, 1, 32 times, 2 boxes, 40 yuan, 9.8 folds, 20 yuan, 4 days ] ].
Step 203, selecting candidate order information with the corresponding recommendation degree value in a second preset range from the candidate order information set as candidate recommended item information to obtain a candidate recommended item information set.
In some embodiments, the executing entity may select candidate order information having a corresponding recommendation degree value within a second predetermined range from the candidate order information set as candidate recommended item information, so as to obtain a candidate recommended item information set. Wherein the second predetermined range may be
Figure 415425DEST_PATH_IMAGE026
。
As an example, the above candidate order information set may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ sconcake, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ], [ pineapple shortbread, 1, 32 times, 2 boxes, 40 yuan, 9.8 folds, 20 yuan, 4 days ] ]. The recommendation degree value corresponding to each candidate order information in the candidate order information set may be 17, 46, 22, and 6, respectively. Thus, the resulting set of candidate recommended item information may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ scon, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ] ].
And step 204, generating the recommended item information list based on the candidate recommended item information set.
In some embodiments, the execution subject may generate the recommended item information list in various ways based on the set of candidate recommended item information.
In some optional implementation manners of some embodiments, the executing entity generates the recommended item information list in various manners based on the candidate recommended item information set, and may include the following steps:
firstly, an initial recommended article information list is obtained.
And secondly, adding each candidate recommended item information in the candidate recommended item information set to the initial recommended item information list to generate a recommended item information list.
Step 205, generating an item recommendation page based on the recommended item information list and a preset basic page.
In some embodiments, the execution subject may generate an item recommendation page based on the recommended item information list and a preset basic page.
As an example, the recommended item information list may be filled in a content display portion in the preset basic page to generate an item recommendation page.
The above embodiments of the present disclosure have the following advantages: by the item recommendation page generation method of some embodiments of the disclosure, the content of the recommendation page is simplified, and meanwhile, the association degree between the recommended item information in the item recommendation page and the target user is maintained or improved. Thus, the frequency of performing value-related operations by the target user is increased and the waste of computer resources is reduced. Specifically, the inventors found that the reasons for wasting computer resources are: the recommendation degree of the historical order information is not quantitatively analyzed through recommendation degree evaluation, so that the relevance degree of the content in the recommendation page and the target user is low, and more item information which is difficult to promote the target user to perform value-related operations is contained in the recommendation page. Based on the above, the item recommendation page generation method introduces a historical order information set of the target user in a predetermined time period. In addition, the recommendation degree value of each piece of historical order information in the historical order information set is determined, so that the recommendation degree value is quantized. And then, based on the recommendation degree value set, screening out the historical order information meeting the conditions from the historical order information set to serve as recommended article information so as to generate a recommendation information list. Therefore, the relevance between the recommended article information and the target user is improved, the stability of the frequency of the value-related operation of the target user is ensured, and the waste of computing resources is reduced.
With further reference to FIG. 3, a flow 300 of further embodiments of an item recommendation page generation method is shown, the flow 300 of the item recommendation page generation method including the steps of:
step 301, in response to receiving an information obtaining request of a target user, obtaining a historical order information set of the target user in a predetermined time period.
In some embodiments, the execution principal may obtain the historical order information set of the target user within a predetermined time period through a wired connection or an unlimited connection in response to receiving the information obtaining request of the target user. Wherein, the historical order information may include: item name, item label value, item average number of views, item value transfer number (e.g., item purchase quantity), item value transfer value (e.g., item unit price), item value transfer value fraction (e.g., item discount), item value generation value (e.g., item cost price), and item shelf life remaining length. The item tag value may be used to indicate how the target user obtains the item information corresponding to the item name. And the target user can be represented by '1' to obtain the item information corresponding to the item name in a self-searching mode. And 2 can be used for representing that the target user obtains the item information corresponding to the item name in a system recommendation mode.
As an example, the above-mentioned historical order information set may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ scon, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ], [ pineapple shortbread, 1, 32 times, 2 boxes, 40 yuan, 9.8 folds, 20 yuan, 4 days ] ].
Step 302, performing recommendation evaluation on each historical order information in the historical order information set of the target user in a predetermined time period to generate a recommendation degree value, so as to obtain a recommendation degree value set.
In some embodiments, the executive body may perform recommendation evaluation on the historical order information by using the following formula to generate a recommendation degree value:
Figure 778273DEST_PATH_IMAGE027
。
wherein,
Figure 814362DEST_PATH_IMAGE002
the recommendation degree value is shown.
Figure 174936DEST_PATH_IMAGE003
And the value of the value transfer of the goods included in the historical order information is represented.
Figure 608061DEST_PATH_IMAGE004
And indicating the value transfer value ratio of the goods included in the historical order information.
Figure 712283DEST_PATH_IMAGE005
And generating a value representing the value of the item included in the historical order information.
Figure 399616DEST_PATH_IMAGE006
Indicating the number of value transfers of the item included in the historical order information.
Figure 196671DEST_PATH_IMAGE007
And the average browsing times of the articles included in the historical order information are shown.
Figure 539928DEST_PATH_IMAGE008
Indicating the value of the item label included in the historical order information.
Figure 510158DEST_PATH_IMAGE009
Representing the amount of historical order information in the historical order information set.
Figure 520839DEST_PATH_IMAGE010
Indicating a serial number.
Figure 223216DEST_PATH_IMAGE011
And the value of the item value transfer value included in the historical order information set is represented.
Figure 116085DEST_PATH_IMAGE012
And the value ratio of the goods value transfer value included in the historical order information set is shown.
Figure 827689DEST_PATH_IMAGE013
And generating a numerical value representing the value of the item included in the historical order information set.
Figure 427298DEST_PATH_IMAGE014
And indicating the value transfer times of the goods included in the historical order information set.
Figure 566155DEST_PATH_IMAGE015
And the average browsing times of the articles included in the historical order information set are shown.
Figure 697053DEST_PATH_IMAGE016
And indicating the value of the item label included in the historical order information set.
Figure 681190DEST_PATH_IMAGE017
Indicating the first in the historical order information set
Figure 666464DEST_PATH_IMAGE010
The historical order information includes an item value transfer value.
Figure 772960DEST_PATH_IMAGE018
Indicating the first in the historical order information set
Figure 578105DEST_PATH_IMAGE010
The historical order information includes an item value transfer value.
Figure 631511DEST_PATH_IMAGE019
Indicating the first in the historical order information set
Figure 268029DEST_PATH_IMAGE010
The value of the item included in the individual historical order information generates a value.
Figure 483110DEST_PATH_IMAGE020
Indicating the first in the historical order information set
Figure 775551DEST_PATH_IMAGE010
The historical order information includes a number of value transfers for the item.
Figure 429386DEST_PATH_IMAGE021
Indicating the first in the historical order information set
Figure 858093DEST_PATH_IMAGE010
The historical order information includes an average number of views of the item.
Figure 555660DEST_PATH_IMAGE022
Indicating the first in the historical order information set
Figure 600976DEST_PATH_IMAGE010
The individual historical order information includes an item tag value.
As an example, the above-mentioned historical order information set may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ scon, 1, 12 times, 20 blocks, 8 yuan, 9.6 folds, 3 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ], [ pineapple shortbread, 1, 32 times, 2 boxes, 40 yuan, 9.8 folds, 20 yuan, 4 days ] ]. The set of recommended degree values may be [1.46, 0.59, 1.39, 0.56 ]. And the recommendation degree values in the recommendation degree value set are reserved with two decimal numbers.
The formula in step 302 is an invention point of the embodiment of the present disclosure, and solves a technical problem mentioned in the background art that "when recommendation degree evaluation is performed on history order information, categories of history orders are not subdivided, and meanwhile, factors influencing the recommendation degree evaluation of the history order information are not comprehensively considered, so that the generated recommendation degree value is not accurate enough, and further, recommended contents are difficult to meet the appeal of a service providing platform and the frequency of performing value-related operations by a user is difficult to increase, which may cause overstock of goods, waste of shelf resources, and excessive consumption of goods fresh-keeping resources. Factors that lead to overstocking of goods, waste of shelf resources, and excessive consumption of goods fresh-keeping resources are often as follows: factors influencing recommendation degree evaluation of historical order information cannot be comprehensively considered, so that the generated recommendation degree value is not accurate enough. If the above factors are solved, the effects of reducing the overstock of goods, improving the use efficiency of shelf resources and reducing the investment of equipment for keeping the goods fresh can be achieved. To achieve this effect, first, the present disclosure introduces an item value transfer value, an item value transfer value ratio, an item value generation value, and an item value transfer number included in the history order information. Thus, the actual profit value corresponding to each historical order information is obtained. In this way, one appeal of the service providing platform is satisfied. Secondly, the average browsing times of the articles are introduced, so that the requirement degree of the target user for the articles is quantified. In addition, the article tag value is introduced, and whether the article corresponding to the historical order information is actively searched by the user or recommended by the system is represented through the article tag value. In practical situations, if the target user purchases the recommended item of the system, it indicates that the recommended item to the user can often meet the consumption habit of the user. If the target user purchases the article obtained through the active retrieval, the article is indicated to be more consistent with the requirement of the user, and the article similar to the article obtained through the active retrieval by the user can be pushed to the user. The accuracy of the recommendation degree value is ensured to a certain extent by fully considering the factors influencing the recommendation degree estimation. Furthermore, the appeal of the service providing platform is met, and the association degree between the recommended content and the target user is improved, so that the frequency of executing value-related operations by the target user is improved, and the overstocked goods, the waste of shelf resources and the equipment investment for keeping the goods fresh are reduced.
Step 303, selecting historical order information with the remaining time of the product shelf life within a first predetermined range from the historical order information set as candidate order information to obtain a candidate order information set.
And step 304, selecting candidate order information with the corresponding recommendation degree value in a second preset range from the candidate order information set as candidate recommended item information to obtain a candidate recommended item information set.
In some embodiments, the specific implementation manner and technical effects of steps 303 and 304 may refer to steps 202 and 203 in those embodiments corresponding to fig. 2, which are not described herein again.
Step 305, selecting candidate recommended article information, of which the article tag value does not meet a first predetermined condition, from the candidate recommended article information set as first basic recommended article information, so as to obtain a first basic recommended article information set.
In some embodiments, the execution subject may select, from the candidate recommended item information set, candidate recommended item information including an item tag value that does not satisfy a first predetermined condition as first basic recommended item information, resulting in a first basic recommended item information set. Wherein the first predetermined condition may be that the item tag value is 2.
As an example, the above set of candidate recommended item information may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ] ]. Thus, the first basic recommended item information set obtained may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ] ].
Step 306, selecting candidate recommended item information, of which the item tag value meets a first predetermined condition, from the candidate recommended item information set as alternative recommended item information, and obtaining an alternative recommended item information set.
In some embodiments, the execution subject may select, from the candidate recommended item information set, candidate recommended item information including an item tag value satisfying a first predetermined condition as the replacement recommended item information, resulting in a replacement recommended item information set. Wherein the first predetermined condition may be that the item tag value is 2.
As an example, the above set of candidate recommended item information may be [ [ pineapple shortbread, 1, 40 times, 5 boxes, 30 yuan, 9.8 folds, 20 yuan, 7 days ], [ cheese tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ] ]. Thus, the resulting set of alternate recommended item information may be [ [ cheese egg tart, 2, 50 times, 3 boxes, 45 dollars, 9.8 folds, 30 dollars, 4 days ] ].
And 307, generating a second basic recommended item information set based on the replaced recommended item information set.
In some embodiments, the execution subject may generate the second basic recommended item information set based on the replacement recommended item information set in various ways.
In some optional implementation manners of some embodiments, the executing body may screen, from the database, item information whose association degree with each piece of the replacement recommended item information in the replacement recommended item information set satisfies a second predetermined condition, as second basic recommended item information, to obtain a second basic recommended item information set. The second predetermined condition may be the item information that is most closely related to the replacement recommended item information. The correlation degree can be calculated by cosine similarity, mahalanobis distance, Pearson correlation coefficient and other methods.
As an example, the above-mentioned substitute article information may be [ cheese egg tart, 2, 50 times, 3 boxes, 45 yuan, 9.8 folds, 30 yuan, 4 days ]. The second basic recommended article information may be [ tart, 2, 45 yuan, 9.8 yuan, 30 yuan ].
And 308, combining the first basic recommended item information set and the second basic recommended item information set to generate the recommended item information list.
In some embodiments, the execution subject may combine the first basic recommended item information set and the second basic recommended item information set to generate the recommended item information list.
As an example, the recommended item information list may be [ [ pineapple shortcake, 1, 30 yuan, 9.8 folds, 20 yuan ], [ tart, 2, 45 yuan, 9.8 folds, 30 yuan ] ].
Step 309, generating an item recommendation page based on the recommended item information list and a preset basic page
In some embodiments, the specific implementation manner and technical effects of step 309 may refer to step 204 in those embodiments corresponding to fig. 2, and are not described herein again.
The above embodiments of the present disclosure have the following advantages: first, the present disclosure introduces an item value transfer value, an item value transfer value fraction, an item value generation value, and an item value transfer number included in the history order information. Thus, the actual profit value corresponding to each historical order information is obtained. In this way, one appeal of the service providing platform is satisfied. Secondly, the average browsing times of the articles are introduced, so that the requirement degree of the target user for the articles is quantified. In addition, the article tag value is introduced, and whether the article corresponding to the historical order information is actively searched by the user or recommended by the system is represented through the article tag value. In practical situations, if the target user purchases the recommended item of the system, it indicates that the recommended item to the user can often meet the consumption habit of the user. If the target user purchases the article obtained through the active retrieval, the article is indicated to be more consistent with the requirement of the user, and the article similar to the article obtained through the active retrieval by the user can be pushed to the user. The accuracy of the recommendation degree value is ensured to a certain extent by fully considering the factors influencing the recommendation degree estimation. Furthermore, the appeal of the service providing platform is met, and the association degree between the recommended content and the target user is improved, so that the frequency of executing value-related operations by the target user is improved, and the overstocked goods, the waste of shelf resources and the equipment investment for keeping the goods fresh are reduced.
With further reference to fig. 4, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides some embodiments of an item recommendation page generation method, which correspond to those shown in fig. 2, and which may be specifically applied in various electronic devices.
As shown in fig. 4, the item recommendation page generation apparatus 400 of some embodiments includes: recommendation evaluation unit 401, first selection unit 402, second selection unit 403, first generation unit 404, and second generation unit 405. The recommendation evaluation unit 401 is configured to perform recommendation evaluation on each piece of historical order information in a set of historical order information of a target user within a predetermined time period to generate a recommendation degree value, so as to obtain a recommendation degree value set, where the historical order information includes: the method comprises the following steps of article name, article label value, average article browsing times, article value transfer numerical values, article value generation numerical values and shelf life remaining time. A first selecting unit 402, configured to select, from the historical order information set, historical order information that includes a remaining time of the product shelf life within a first predetermined range as candidate order information, resulting in a candidate order information set. A second selecting unit 403, configured to select candidate order information with a corresponding recommendation degree value within a second predetermined range from the candidate order information set as candidate recommended item information, so as to obtain a candidate recommended item information set. A first generating unit 404 configured to generate a recommended item information list based on the set of candidate recommended item information. A second generating unit 405 configured to generate an item recommendation page based on the recommended item information list and a preset basic page.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: recommending and evaluating each piece of historical order information in a historical order information set of a target user in a preset time period to generate a recommendation degree value, and obtaining the recommendation degree value set, wherein the historical order information comprises: the method comprises the following steps of article name, article label value, average article browsing times, article value transfer numerical values, article value generation numerical values and shelf life remaining time. And selecting historical order information with the article shelf life remaining time within a first preset range from the historical order information set as candidate order information to obtain a candidate order information set. And selecting candidate order information with the corresponding recommendation degree value in a second preset range from the candidate order information set as candidate recommended article information to obtain a candidate recommended article information set. And generating a recommended article information list based on the candidate recommended article information set. And generating an item recommendation page based on the recommended item information list and a preset basic page.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a recommendation evaluation unit, a first selection unit, a second selection unit, a first generation unit, and a second generation unit. The names of the units do not form a limitation on the units themselves in some cases, for example, the recommendation evaluation unit may also be described as a unit that performs recommendation evaluation on each piece of historical order information in a set of historical order information of a target user within a predetermined time period to generate a recommendation degree value, and obtains a recommendation degree value set.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims (10)

1. A method for generating an item recommendation page comprises the following steps:
performing recommendation evaluation on each piece of historical order information in a set of historical order information of a target user in a preset time period to generate a recommendation degree value, and obtaining a recommendation degree value set, wherein the historical order information comprises: the method comprises the following steps of (1) article name, article label value, article average browsing times, article value transfer numerical values, article value transfer numerical value ratio, article value generation numerical values and shelf life remaining time;
selecting historical order information with the article shelf life remaining time within a first preset range from the historical order information set as candidate order information to obtain a candidate order information set;
selecting candidate order information with the corresponding recommendation degree value in a second preset range from the candidate order information set as candidate recommended article information to obtain a candidate recommended article information set;
generating a recommended item information list based on the candidate recommended item information set;
and generating an item recommendation page based on the recommended item information list and a preset basic page.
2. The method of claim 1, wherein before performing recommendation evaluation on each historical order information in the set of historical order information of the target user over a predetermined time period to generate a recommendation degree value, the method further comprises:
and in response to receiving the information acquisition request of the target user, acquiring a historical order information set of the target user in a preset time period.
3. The method according to one of claims 1-2, wherein the performing recommendation evaluation on each historical order information in the historical order information set of the target user in a predetermined time period to generate a recommendation degree value comprises:
determining a product value of the ratio of the item value transfer value to the item value transfer value included in the historical order information as a first product value;
determining a difference value between an item value generation value included in the historical order information and the first product value as a first difference value;
determining a difference value between the item value transfer value and the item value generation value included in the historical order information as a second difference value;
determining a product value of the number of times of transferring the item value and an item value generation value included in the historical order information and the first difference value as a second product value;
determining the recommendation degree value based on the historical order information, the first difference value, the second difference value and the second product value.
4. The method of claim 1, wherein generating a recommended item information list based on the set of candidate recommended item information comprises:
selecting candidate recommended article information, of which the article tag value does not meet a first preset condition, from the candidate recommended article information set as first basic recommended article information to obtain a first basic recommended article information set;
selecting candidate recommended item information with item tag values meeting first preset conditions from the candidate recommended item information set as alternative recommended item information to obtain an alternative recommended item information set;
and generating a second basic recommended item information set based on the replacement recommended item information set.
5. The method of claim 4, wherein the generating a second set of base recommended item information based on the set of replacement recommended item information comprises:
and screening out article information of which the degree of association with each alternative recommended article information in the alternative recommended article information set meets a second preset condition from a database to serve as second basic recommended article information, so as to obtain a second basic recommended article information set.
6. The method of claim 4, wherein the method further comprises:
combining the first basic recommended item information set and the second basic recommended item information set to generate the recommended item information list.
7. An item recommendation page generation apparatus comprising:
the recommendation evaluation unit is configured to perform recommendation evaluation on each piece of historical order information in a set of historical order information of a target user within a predetermined time period to generate a recommendation degree value, and obtain a recommendation degree value set, wherein the historical order information includes: the method comprises the following steps of (1) article name, article label value, article average browsing times, article value transfer numerical values, article value transfer numerical value ratio, article value generation numerical values and shelf life remaining time;
the first selection unit is configured to select historical order information with the shelf life remaining time of the article being in a first preset range from the historical order information set as candidate order information to obtain a candidate order information set;
the second selection unit is configured to select candidate order information of which the corresponding recommendation degree value is within a second preset range from the candidate order information set as candidate recommended article information to obtain a candidate recommended article information set;
a first generating unit configured to generate a recommended item information list based on the set of candidate recommended item information;
and the second generation unit is configured to generate an item recommendation page based on the recommended item information list and a preset basic page.
8. The item recommendation page generating device according to claim 7, wherein the first generating unit is further configured to:
selecting candidate recommended article information, of which the article tag value does not meet a first preset condition, from the candidate recommended article information set as first basic recommended article information to obtain a first basic recommended article information set;
selecting candidate recommended item information with item tag values meeting first preset conditions from the candidate recommended item information set as alternative recommended item information to obtain an alternative recommended item information set;
and generating a second basic recommended item information set based on the replacement recommended item information set.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
CN202011219704.XA 2020-11-05 2020-11-05 Item recommendation page generation method and device, electronic equipment and readable medium Active CN112102043B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011219704.XA CN112102043B (en) 2020-11-05 2020-11-05 Item recommendation page generation method and device, electronic equipment and readable medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011219704.XA CN112102043B (en) 2020-11-05 2020-11-05 Item recommendation page generation method and device, electronic equipment and readable medium

Publications (2)

Publication Number Publication Date
CN112102043A true CN112102043A (en) 2020-12-18
CN112102043B CN112102043B (en) 2021-03-02

Family

ID=73785386

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011219704.XA Active CN112102043B (en) 2020-11-05 2020-11-05 Item recommendation page generation method and device, electronic equipment and readable medium

Country Status (1)

Country Link
CN (1) CN112102043B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112365328A (en) * 2021-01-13 2021-02-12 北京每日优鲜电子商务有限公司 Article sale information display method and device, electronic equipment and readable medium
CN113191840A (en) * 2021-04-25 2021-07-30 北京沃东天骏信息技术有限公司 Article information display method and device, electronic equipment and computer readable medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140100986A1 (en) * 1998-09-18 2014-04-10 Amazon.Com, Inc. Automated detection and exposure of behavior-based relationships between browsable items
CN106228386A (en) * 2016-07-12 2016-12-14 腾讯科技（深圳）有限公司 A kind of information-pushing method and device
CN110097412A (en) * 2018-01-31 2019-08-06 阿里巴巴集团控股有限公司 Item recommendation method, device, equipment and storage medium
CN111401974A (en) * 2020-06-03 2020-07-10 北京每日优鲜电子商务有限公司 Information sending method, information sending device, electronic equipment and computer readable medium
CN111428143A (en) * 2020-06-11 2020-07-17 北京每日优鲜电子商务有限公司 Commodity recommendation method and system, server and storage medium
CN111881361A (en) * 2020-09-28 2020-11-03 北京每日优鲜电子商务有限公司 Article information pushing method and device, electronic equipment and computer readable medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140100986A1 (en) * 1998-09-18 2014-04-10 Amazon.Com, Inc. Automated detection and exposure of behavior-based relationships between browsable items
CN106228386A (en) * 2016-07-12 2016-12-14 腾讯科技（深圳）有限公司 A kind of information-pushing method and device
CN110097412A (en) * 2018-01-31 2019-08-06 阿里巴巴集团控股有限公司 Item recommendation method, device, equipment and storage medium
CN111401974A (en) * 2020-06-03 2020-07-10 北京每日优鲜电子商务有限公司 Information sending method, information sending device, electronic equipment and computer readable medium
CN111428143A (en) * 2020-06-11 2020-07-17 北京每日优鲜电子商务有限公司 Commodity recommendation method and system, server and storage medium
CN111881361A (en) * 2020-09-28 2020-11-03 北京每日优鲜电子商务有限公司 Article information pushing method and device, electronic equipment and computer readable medium

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112365328A (en) * 2021-01-13 2021-02-12 北京每日优鲜电子商务有限公司 Article sale information display method and device, electronic equipment and readable medium
CN112365328B (en) * 2021-01-13 2021-05-25 北京每日优鲜电子商务有限公司 Article sale information display method and device, electronic equipment and readable medium
CN113191840A (en) * 2021-04-25 2021-07-30 北京沃东天骏信息技术有限公司 Article information display method and device, electronic equipment and computer readable medium

Also Published As

Publication number Publication date
CN112102043B (en) 2021-03-02

Similar Documents

Publication Publication Date Title
CN110298716B (en) Information pushing method and device
CN112035753B (en) Recommendation page generation method and device, electronic equipment and computer readable medium
CN110866040B (en) User portrait generation method, device and system
CN112184100A (en) Article inventory monitoring method and device, electronic equipment and computer readable medium
CN111783810A (en) Method and apparatus for determining attribute information of user
CN112102043B (en) Item recommendation page generation method and device, electronic equipment and readable medium
CN109961299A (en) The method and apparatus of data analysis
CN110738508B (en) Data analysis method and device
CN114049072B (en) Index determination method and device, electronic equipment and computer readable medium
WO2022156589A1 (en) Method and device for determining live broadcast click rate
CN114663015A (en) Replenishment method and device
CN109978594B (en) Order processing method, device and medium
CN113822734B (en) Method and device for generating information
CN112036990B (en) Article information pushing method and device, electronic equipment and computer readable medium
CN111932321B (en) Method and device for pushing article information for user, electronic equipment and medium
CN107291923B (en) Information processing method and device
CN112598337A (en) Article-oriented vehicle control method, apparatus, device and computer readable medium
CN112308378A (en) Scheduling device control method, scheduling device control device, electronic device and computer readable medium
CN112449217B (en) Method and device for pushing video, electronic equipment and computer readable medium
CN112364232A (en) Information generation method and device, electronic equipment and computer readable medium
CN113742564A (en) Target resource pushing method and device
CN114926234A (en) Article information pushing method and device, electronic equipment and computer readable medium
CN112200643B (en) Article information pushing method and device, electronic equipment and computer readable medium
CN113159877B (en) Data processing method, device, system and computer readable storage medium
CN110084541B (en) Method and apparatus for predicting supplier delivery duration

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
TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20231121

Address after: 518000, Building 702, Building A, No. 4545 Songbai Road, Heshuikou Community, Matian Street, Guangming District, Shenzhen, Guangdong Province

Patentee after: Shenzhen Yongsheng Intellectual Property Service Co.,Ltd.

Address before: 100102 room 801, 08 / F, building 7, yard 34, Chuangyuan Road, Chaoyang District, Beijing

Patentee before: BEIJING MISSFRESH E-COMMERCE Co.,Ltd.

TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20240424

Address after: No. 106, Jiangshiwu Group, Songjiaqiao Village, Ansha Town, Changsha County, Changsha City, Hunan Province, 410100

Patentee after: Hunan Jinzhong Customized Technology Co.,Ltd.

Country or region after: China

Address before: 518000, Building 702, Building A, No. 4545 Songbai Road, Heshuikou Community, Matian Street, Guangming District, Shenzhen, Guangdong Province

Patentee before: Shenzhen Yongsheng Intellectual Property Service Co.,Ltd.

Country or region before: China