CN110163712A - A kind of merchandise news method for pushing and system - Google Patents
A kind of merchandise news method for pushing and system Download PDFInfo
- Publication number
- CN110163712A CN110163712A CN201810192946.0A CN201810192946A CN110163712A CN 110163712 A CN110163712 A CN 110163712A CN 201810192946 A CN201810192946 A CN 201810192946A CN 110163712 A CN110163712 A CN 110163712A
- Authority
- CN
- China
- Prior art keywords
- user
- server
- recommendations
- merchandise news
- request
- 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.)
- Withdrawn
Links
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
- G06Q30/02—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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of merchandise news method for pushing, comprising: first server obtains the participation grouping request of each first user；It is grouped and is requested according to the participation of the first user of first server, the behavioral data of each first user saved is obtained from second server；According to the behavioral data of each first user, each first user is grouped, the feature of each first user in grouping further include: the corresponding second user object crowd characteristic value matching of each first user, the corresponding second user level of each first user are suitable.Second server arranges the first Recommendations involved in the historical transactional information of each first user in same group.It completes user to participate in after being grouped request, the first user chooses whether to enter packet group, and selection enters the classification of group；Packet State is visible to user after into packet group.By such method, when Recommendations may be implemented, it can be exchanged mutually between user, become apparent from commodity, save the time for buyer.
Description
Technical field
This application involves network technique field more particularly to a kind of merchandise news method for pushing and systems.
Background technique
With the development of network technology, network has more and more been intervened in people's lives, selects the people of shopping at network
More and more, the way of recommendation of extensive stock information emerges one after another, and people, which often see in shopping center, is not belonging to oneself purchase
The commodity or oneself uninterested commodity for buying ability occur, and the information of the received commodity push arrived is often inaccurate, than
When being such as related to region, merchandise news dispensing subjectivity selection, the content of push is not simultaneously inaccurate objective.Secondly, push
When merchandise news meets user itself, user may not know commercial quality quality, be also to have one for light sees comment
Fixed deviation, deviation from: if a user commonly using the commodity of unusual good quality, then he it is slightly lower to a price but
The evaluation for being the commodity of medium quality may be very poor, then this evaluation will affect the group for being suitble to buy this commodity.Thus
As it can be seen that existing commodity push has certain wasting of resources.
Summary of the invention
Goal of the invention: merchandise news method for pushing disclosed in the present application and system provide one kind and can solve the above problem
A kind of scheme, the personal information for user includes the identity characteristic and hobby of user, and the level in conjunction with user is special
Value indicative provides a more accurately merchandise news push for user.
Technical solution: the technical solution that the application uses is as follows:
A kind of merchandise news method for pushing, comprising:
First server obtains the participation grouping request of each first user；
It is grouped and is requested according to the participation of the first user of first server, obtain each first user's saved from second server
Behavioral data；
According to the behavioral data of each first user, each first user is grouped, the feature of each first user in grouping
Further include: the corresponding second user object crowd characteristic value matching of each first user, the corresponding second user layer of each first user
Grade is quite；
Second server arranges the first Recommendations involved in the historical transactional information of each first user in same group；
It completes user to participate in after being grouped request, the first user chooses whether to enter packet group, and selection enters the classification of group；Into
Packet State is visible to user after entering packet group；
When information pushes, comprising:
First server receives the merchandise request of the first user；
Second server selectes interested second Recommendations of user according to the behavioral data of the user of acquisition；
According to the merchandise request of the first user, second server retrieves the first Recommendations and recommends the first user；
According to the merchandise request of the first user, second server retrieves the second Recommendations and recommends the first user.
As a preferred solution of the present invention, the behavioral data that the second server obtains the first user includes: to obtain
Take the hobby of the first hierarchy of users and/or the first user and/or being averaged for the first user identity feature and/or the first user
The average moon gross turnover Ai of monovalent Ci and/or monthly average conclusion of the business stroke count Bi and/or the first user.
As a preferred solution of the present invention, the evaluation of the hierarchy of users further include:
Second server obtains the first user residence and price-level, the average salary on the ground；
Second server obtains the average monthly income of the first user；
Calculate the hierarchy of users characteristic value of the first user.
As a preferred solution of the present invention, the first user is grouped according to the behavioral data of first user
When, comprising:
Utilize the average moon gross turnover Ai of the first user and/or average unit price Ci of the first user and/or monthly average conclusion of the business pen
Number Bi, and according to the corresponding averagely weight of moon gross turnover and/or the average unit price weight of the first user and/or monthly average at
The weight for handing over stroke count, in conjunction with the behavioral data of the first user, hierarchy of users characteristic value, the characteristic value for calculating the first user.
As a preferred solution of the present invention, further include after the characteristic value for calculating the first user, according to described the
Each first user is ranked up by the characteristic value of one user；
For the first user belonged in same characteristic value interval range, second in the scoped features value is belonged in matching crowd
First user and second user are divided and enter same grouping by user.
As a preferred solution of the present invention, when giving commercial product recommending to the first user, comprising:
When instruction is parked in packet interface, the first Recommendations are sent to the displaying interface of packet interface；
When instruction is parked in the merchandise news page, the first Recommendations are sent to the merchandise news page, send the second Recommendations extremely
It is shown after first Recommendations.
As a preferred solution of the present invention, the behavioral data for obtaining each first user has a time threshold,
One time threshold of every mistake needs to reacquire the behavioral data of each first user, and user is grouped again.
It as a preferred solution of the present invention, include following functions: dialogue function, function of search, exhibition in the grouping
Show function；Each function has corresponding display interface, including dialog interface, search interface, displaying interface；Specifically, described
Dialogue function is used to realize exchanging for the first user and second user, including sends voice, text, picture, video, link；Institute
The purchaser record that function of search searches for second user for the first user is stated, the displaying function is used to show the quotient searched out
Product.
As a preferred solution of the present invention, a kind of merchandise news supplying system, comprising:
Request receiving module receives participation request and the merchandise request of each first user for first server；
Module is obtained, for obtaining and saving user behavior data；
Grouping module is based on user characteristics value, is grouped to user；
Commodity integrate module, for arranging the first Recommendations and the second Recommendations.
The present invention realize it is following the utility model has the advantages that
1, compared with prior art, increase hierarchy of users characteristic value, so that recommending more accurate；
2, compared with prior art, Packet State is to user as it can be seen that the actual effect of commodity can be more directly acquainted with；
3, the first Recommendations are distinguished and the second Recommendations, the merchandise news of recommendation is more in line with；
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure
Example, and consistent with the instructions for explaining the principles of this disclosure.
Fig. 1 be the present embodiments relate to grouping flow diagram；
Fig. 2 be the present embodiments relate to Recommendations flow diagram.
Fig. 3 be the present embodiments relate to system structure diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.
Embodiment one:
According to 1-3: Fig. 1 for the present embodiments relate to grouping flow diagram；Fig. 2 be the present embodiments relate to recommendation
The flow diagram of commodity, Fig. 3 be the present embodiments relate to system structural schematic diagram.
Referring to Fig.1, it illustrates the grouping flow diagram of the application, can specifically include:
Step 110: first server 310 obtains the participation grouping request of each first user.
Step 120: being grouped and requested according to the participation of the first user of first server 310, obtained from second server 320
The behavioral data of each first user saved.
In practice, the first user is buyer.
The participation request of received each first user of first server 310 can be one for an e-commerce platform
A activity, in this application, buyer also need the personal information of oneself uploading to first server 310, so as to subsequent progress
Grouping.
The application can receive the participation grouping request of buyer, and parsing participates in the id of the user in grouping request,
Then trading information data of the user on website is obtained from second server 320 according to the id, these transaction data are to each
The grouping of seller provides objective data supporting.
Step 130, according to the behavioral data of each first user, each first user is grouped, each first uses in grouping
The feature at family further include: the corresponding second user object crowd characteristic value matching of each first user, each first user corresponding the
Two hierarchy of users are suitable.
The buyer that request is participated in for sending, after the trading information data for obtaining each buyer, according to transaction data pair
Buyer is grouped, and can be so grouped based on objective data, grouping can guarantee the user with similar characteristic value
In the same grouping, in general, second user is the buyer relative to the first user, same group is assigned to the first user
It is interior.
Step 140, second server 320 arranges involved in the historical transactional information of each first user in same group
One Recommendations.
After being grouped to buyer, the buyer in default packet possesses same purchasing power, at this time by second server 320
The commodity of the purchase of each first user saved arrange, and are further integrated into the first merchandise news to be recommended.Such as
In step 130, buyer A, B, C are in same group, then the merchandise news whole quilt of the above-mentioned buyer in group bought
Then the commodity adaptable with grouping classification, the first Recommendations as recommendation are selected in integration in the commodity of integration.?
It when integrating, is automatically performed by system, so far the participation grouping request of user is completed, and no matter whether buyer selects to enter that he is corresponding
In packet group, buyer can receive the first Recommendations that server is recommended according to grouping.
Complete to be divided into after user participates in grouping request, first user chooses whether to enter packet group, and selection enters group
Classification.Packet State is visible to user after into packet group.
In the step 120, first server 310 receives buyer's personal information, including identity characteristic and hobby,
Grouping classification also includes these two types, and when buyer's selection does not enter in corresponding packet group, buyer can also receive server and come from
Two grouping commercial product recommendings, if selection enter packet group, can choose into hobby packet group and or identity characteristic
Packet group, in actual operation, the name of two groupings are free, but it is to be based on hobby Recommendations that content, which is one,
Commodity have apparent class indication, the other is identity-based feature Recommendations, commodity do not have apparent class indication.Than
Such as, certain buyer takes part in grouping request, and then selection enters hobby packet group, it is assumed that and the hobby of buyer is mountain-climbing, the
One server 310 calculates the user characteristics value of the buyer, user is assigned in corresponding packet group, according to pushing away for step 140
Recommend method, the first step integrates the purchase commodity of buyer all in the group, then integrate according to packet class be adapted commodity to
User recommends.For example, buyer's selection enters identity characteristic packet group, it is assumed that the identity characteristic of buyer is precious mother, then enters opposite
The precious mother's packet group answered.It is such according to identity characteristic packet group also: student, milk father, precious mother, workplace women, workplace male
Etc. divide identity characteristic packet group.Packet State is to user as it can be seen that being mainly manifested in, comprising following functions in grouping: right
It talks about function, function of search, show function.Each function has corresponding display interface, including dialog interface, search interface, exhibition
Show interface.Specifically, dialogue function is used to realize exchanging for the first user and second user, including send voice, text, figure
Piece, video, link.Main function is to aid in buyer and is better understood by commodity, and function of search is used for the first user search second
The purchaser record at family shows that function is used to show the commodity searched out.More, buyer can be set not received message and prevent from doing
It disturbs, or packet group is exited in selection, but still enjoys the preferential recommendation of the first Recommendations of the grouping after exiting.
When information pushes, comprising:
Step 210, first server 310 receives the merchandise request of the first user.
Merchandise request is the commodity that buyer needs to buy, and the page of merchandise request can be to be accessed in packet group, can also
To be the searched page access in e-commerce website.
Step 220, second server 320 is selected user interested second and is pushed away according to the behavioral data of the user of acquisition
Recommend commodity.
Here the second Recommendations are recommended methods in the prior art, i.e., calculate first degree of association for search content
Second degree of association and the third degree of association, finally provide the Recommendations of suitable buyer.
Step 230, according to the merchandise request of the first user, second server 320 retrieves the first Recommendations and recommends the
One user.
Step 240, according to the merchandise request of the first user, second server 320 retrieves the second Recommendations and recommends the
One user.
Embodiment two:
Preferably, it includes: to obtain the first hierarchy of users and/or first that second server 320, which obtains the behavioral data of the first user,
The average unit price Ci and/or monthly average conclusion of the business pen of the hobby of user and/or the first user identity feature and/or the first user
Number Bi and/or the first user month gross turnover Ai.
Preferably, the evaluation of hierarchy of users further include:
Second server 320 obtains the first user residence and the average salary on the ground.
Second server 320 obtains the average monthly income of the first user.
Calculate the hierarchy of users characteristic value of the first user.
As a kind of alternative embodiment of the invention, here for the first user, hierarchy characteristic value j show themselves in that j=
Average monthly income/average salary
Preferably, when being grouped according to the behavioral data of the first user to the first user, comprising:
Utilize the average moon gross turnover Ai of the first user and/or average unit price Ci of the first user and/or monthly average conclusion of the business pen
Number Bi, and according to the corresponding averagely weight of moon gross turnover and/or the average unit price weight of the first user and/or monthly average at
The weight for handing over stroke count, in conjunction with the first hierarchy of users characteristic value, the characteristic value of the first user of calculating.
As a kind of optinal plan of the invention, such as the first user, average moon gross turnover is respectively A1,
A2 ... ... An, monthly average conclusion of the business stroke count are respectively B1, and B2 ... ... Bn, average unit price is respectively C1, C2 ... ... Cn, wherein putting down
The weight of equal moon turnover is n1, and the weight of monthly average conclusion of the business stroke count is n2, the weight n3 of average unit price.So for first
For user, characteristic value w=j* (n1* (A1/ (A1+A2+ ... An))+n2* (B1/ (B1+B2+ ... Bn))+n3* (C1/ (C1+
C2+…Cn)))。
Preferably, further include after calculating the characteristic value of the first user, according to the characteristic value of the first user, by each first user
It is ranked up.
For the first user belonged in same characteristic value interval range, belong in the scoped features value in matching crowd
First user and second user are divided and enter same grouping by second user.
As a kind of optinal plan of the invention, following table indicates a kind of situation of buyer's grouping:
| Buyer | Buyer a | Buyer b | Buyer c | Buyer d |
| Characteristic value | 7 | 7 | 5 | 4 |
At this point it is possible to find, after buyer has selected into certain a kind of grouping, first server 310 calculates buyer's
The user that selection enters packet group is identified with characteristic value, the identical user of characteristic value is divided to same group: being bought by characteristic value
Family a and buyer b is divided to same group, buyer c and independent one group of buyer d, and continues waiting for buyer identical with their characteristic values
It is divided into same group.
Preferably, when giving commercial product recommending to the first user, comprising:
When instruction is parked in packet interface, the first Recommendations are sent to the displaying interface of packet interface.
The embodiment is introduced in example 1, is not repeated herein.
When instruction is parked in the merchandise news page, the first Recommendations are sent to the merchandise news page, second is sent and recommends quotient
It is shown after product to the first Recommendations.
For buyer when using the systematic search commodity, there are two types of selections for meeting, first is that in the homepage search commercial articles of the system,
The first Recommendations of preferential recommendation at this time, while recommending the second Recommendations, quotient is recommended first in the position of the second Recommendations
Behind product, in grouping when search, the displaying interface in grouping only shows the first Recommendations.
Preferably, the behavioral data for obtaining each first user has a time threshold, and one time threshold of every mistake needs more
The behavioral data of new each first user, and user is grouped again.The purpose done so is that timing updates the purchase energy of buyer
Power.
Preferably, a kind of merchandise news supplying system, comprising:
First server 310, second server 320.
First server 310 includes request receiving module 321, and the ginseng of each first user is received for first server 310
With request and merchandise request.
Module 322 is obtained, is grouped and is requested according to the participation of each first user, the use saved is obtained from second server 320
Family behavioral data.
Grouping module 323 is based on user characteristics value, is grouped to user, and grouping module 323 includes dialog unit, searches
Cable elements, display unit.
Commodity integrate module 324, for arranging the first Recommendations and the second Recommendations.
Second server 320 is used to record the behavioral data of user.
The above embodiments merely illustrate the technical concept and features of the present invention, and the purpose is to allow the skill for being familiar with the technical field
Art personnel can understand the content of the present invention and implement it accordingly, and can not be limited the scope of the invention with this.All bases
Equivalent changes or modifications made by spirit of the invention, should be covered by the protection scope of the present invention.
Claims (9)
1. a kind of merchandise news method for pushing characterized by comprising
First server obtains the participation grouping request of each first user；
It is grouped and is requested according to the participation of the first user of first server, obtain each first user's saved from second server
Behavioral data；
According to the behavioral data of each first user, each first user is grouped, the feature of each first user in grouping
Further include: the corresponding second user object crowd characteristic value matching of each first user, the corresponding second user layer of each first user
Grade is quite；
Second server arranges the first Recommendations involved in each first user's history Transaction Information in same group；
It completes user to participate in after being grouped request, the first user chooses whether to enter packet group, and selection enters dividing for packet group
Class；Packet State is visible to user after into packet group；
When information pushes, comprising:
First server receives the merchandise request of the first user；
Second server selectes interested second Recommendations of user according to the behavioral data of the user of acquisition；
According to the merchandise request of the first user, second server retrieves the first Recommendations and recommends the first user；
According to the merchandise request of the first user, second server retrieves the second Recommendations and recommends the first user.
2. a kind of merchandise news method for pushing according to claim 1, which is characterized in that the second server obtains
The behavioral data of first user includes: the hobby and/or first user's body for obtaining the first hierarchy of users and/or the first user
Average unit price Ci and/or monthly average conclusion of the business stroke count Bi and/or the first user months gross turnovers of part feature and/or the first user
Ai。
3. a kind of merchandise news method for pushing according to claim 1, which is characterized in that the evaluation of the hierarchy of users is also
Include:
Second server obtains the first user residence and the average salary on the ground；
Second server obtains the average monthly income of the first user；
Calculate the hierarchy of users characteristic value of the first user.
4. a kind of merchandise news method for pushing according to claim 1, which is characterized in that according to the row of first user
When being grouped for data to the first user, comprising:
Utilize the average moon gross turnover Ai of the first user and/or average unit price Ci of the first user and/or monthly average conclusion of the business pen
Number Bi, and according to the corresponding averagely weight of moon gross turnover and/or the average unit price weight of the first user and/or monthly average at
The weight for handing over stroke count, in conjunction with the first hierarchy of users characteristic value, the characteristic value of the first user of calculating.
5. a kind of merchandise news method for pushing according to claim 4, which is characterized in that the spy for calculating the first user
It further include, according to the characteristic value of first user, each first user being ranked up after value indicative；
For the first user belonged in same characteristic value interval range, second in the scoped features value is belonged in matching crowd
First user and second user are divided and enter same grouping by user.
6. a kind of merchandise news method for pushing according to claim 1, which is characterized in that give commercial product recommending to the first user
When, comprising:
When instruction is parked in packet interface, the first Recommendations are sent to the displaying interface of packet interface；
When instruction is parked in the merchandise news page, the first Recommendations are sent to the merchandise news page, send the second Recommendations extremely
It is shown after first Recommendations.
7. a kind of merchandise news method for pushing according to claim 1, which is characterized in that described to obtain each first user's
Behavioral data has a time threshold, and one time threshold of every mistake needs to reacquire the behavioral data of each first user, and will
User is grouped again.
8. a kind of merchandise news method for pushing according to claim 1, which is characterized in that include following function in the grouping
Can: dialogue function, shows function at function of search；Each function has corresponding display interface, including dialog interface, search circle
Face shows interface；Specifically, the dialogue function is used to realize exchanging for the first user and second user, including send voice,
Text, picture, video, link；Described search function searches for the purchaser record of second user, the displaying function for the first user
It can be used to show the commodity searched out.
9. a kind of merchandise news supplying system according to claim 1-8 characterized by comprising
Request receiving module receives participation request and the merchandise request of each first user for first server；
Module is obtained, for obtaining and saving user behavior data；
Grouping module is based on user characteristics value, is grouped to user；
Commodity integrate module, for arranging the first Recommendations and the second Recommendations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810192946.0A CN110163712A (en) | 2018-03-09 | 2018-03-09 | A kind of merchandise news method for pushing and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810192946.0A CN110163712A (en) | 2018-03-09 | 2018-03-09 | A kind of merchandise news method for pushing and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110163712A true CN110163712A (en) | 2019-08-23 |
Family
ID=67635740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810192946.0A Withdrawn CN110163712A (en) | 2018-03-09 | 2018-03-09 | A kind of merchandise news method for pushing and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110163712A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110428133A (en) * | 2019-06-27 | 2019-11-08 | 平安科技（深圳）有限公司 | Personnel's packet control process, device, computer equipment and storage medium |
| CN111127095A (en) * | 2019-12-20 | 2020-05-08 | 秒针信息技术有限公司 | Target audience interest analysis method, device, equipment and storage medium |
-
2018
- 2018-03-09 CN CN201810192946.0A patent/CN110163712A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110428133A (en) * | 2019-06-27 | 2019-11-08 | 平安科技（深圳）有限公司 | Personnel's packet control process, device, computer equipment and storage medium |
| CN111127095A (en) * | 2019-12-20 | 2020-05-08 | 秒针信息技术有限公司 | Target audience interest analysis method, device, equipment and storage medium |
| CN111127095B (en) * | 2019-12-20 | 2023-05-30 | 秒针信息技术有限公司 | Target audience interest analysis method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10776435B2 (en) | Canonicalized online document sitelink generation | |
| US10133786B2 (en) | Method and system for recommending target object information | |
| US20190244229A1 (en) | Systems and techniques for ranking items | |
| Hooi et al. | Intention to use online food delivery service in Malaysia among university students | |
| WO2018045937A1 (en) | Page information processing system, and method and apparatus for generating pages and providing page information | |
| WO2017012474A1 (en) | Method and apparatus for providing service object information | |
| TW201305944A (en) | Providing social product recommendations | |
| US20150193848A1 (en) | Method and server for information processing | |
| CN107220888A (en) | Social modularization distribution system based on electric business pattern | |
| CN104766213A (en) | Intelligent distribution system of fast moving consumer goods | |
| CN106384266A (en) | Mobile internet-base fashion designer and buyer communion and trading system and method | |
| WO2022121957A1 (en) | Order output method and apparatus, computer device, and computer-readable storage medium | |
| CN103778545B (en) | A kind of merchandise information processing method and system | |
| CN104867037A (en) | Data processing method and device of figure characteristics | |
| EP2954480A2 (en) | Directing communications to semantic bundles of locations | |
| CN110163712A (en) | A kind of merchandise news method for pushing and system | |
| JP7145823B2 (en) | Provision device, provision method and provision program | |
| CN105550304A (en) | Community mode and system based on user interests in screen locking APP (Application) | |
| Pramono et al. | The Influence Of Social Media Marketing Tiktok On Brand Awareness And Purchase Interest (case Study: Sociolla) | |
| Chang et al. | Customers' intention towards O2O food delivery service under the different characteristic of customer group–A case study of Suzhou industrial park | |
| US20160189190A1 (en) | Computer implemented system and method for creation of a digital collaborative communication network for generating inquiries and receiving responses over mobile systems to provide customer response data to vendors and distribute selective data over social and other networks | |
| Qian et al. | Factors influencing purchase intention toward online food delivery service: the case study of Ele. me in China | |
| CN105975629A (en) | Information interaction method and device for electronic trading platform | |
| CN108280727A (en) | A kind of differential pin unified platform system | |
| CN106548377A (en) | A kind of method that sale scheme is automatically generated according to sales lead |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20190823 |
|
| WW01 | Invention patent application withdrawn after publication |