CN107247762B - International logistics line recommendation method - Google Patents
International logistics line recommendation method Download PDFInfo
- Publication number
- CN107247762B CN107247762B CN201710404861.XA CN201710404861A CN107247762B CN 107247762 B CN107247762 B CN 107247762B CN 201710404861 A CN201710404861 A CN 201710404861A CN 107247762 B CN107247762 B CN 107247762B
- Authority
- CN
- China
- Prior art keywords
- line
- data
- user
- logistics
- route
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012216 screening Methods 0.000 claims abstract description 9
- 230000006399 behavior Effects 0.000 claims description 49
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 9
- 238000007621 cluster analysis Methods 0.000 claims description 7
- 238000005065 mining Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 5
- 238000003064 k means clustering Methods 0.000 claims description 4
- 238000007635 classification algorithm Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 235000013372 meat Nutrition 0.000 description 2
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- 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
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Databases & Information Systems (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Accounting & Taxation (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses an international logistics line recommending method, which comprises the following steps: s1: collecting package order information submitted by a user; s2: screening the package order information in the step S1 to form user behavior data and line characteristic data; s3: analyzing the user behavior data in the step S2 to form a line selection data portrait; analyzing and excavating the line characteristic data in the step S2 to form historical line big data; s4: recommending a logistics line for the user according to the line selection data image and the historical line big data in the step S3, and recommending a proper international logistics line for the user according to the line selection data image and the historical line big data.
Description
Technical Field
The invention relates to the field of logistics, in particular to an international logistics line recommendation method.
Background
With the development of the internet and electronic commerce, the cross-border shopping has a larger and larger proportion in electronic commerce, and the biggest pain of the cross-border shopping is that the international logistics cost is higher, the transportation period is longer, meanwhile, the business is influenced by customs policies of various countries and regions, and how a user selects a more suitable logistics line becomes a big pain in the experience in the cross-border shopping.
Compared with the relatively transparent price and delivery period of domestic logistics, the international logistics company and the third party agency company are numerous, the same logistics lines of different companies have obvious differences in the delivery price and delivery period, and even the same logistics lines provided by the same logistics company because of different agencies have great differences. In addition, international logistics can be influenced by factors such as mailing limitation, clearance time, distribution range and the like, and it is very difficult for an ordinary user to select the most suitable mailing line from a large number of logistics lines.
Conventional logistics providers simply list available logistics lines to users or simply list each line to users, and users need to select a proper line from a lot of line information, so that users can spend much time selecting the line, but the user cannot select the most proper line finally.
The personalized recommendation system is a potential service technology for solving information overload, and utilizes preference information of users to automatically recommend objects meeting the interest characteristics of the users to the users. This is in contrast to the "one-to-many" type of information services provided by search engines, where the personalized recommendation system outputs results that better meet the expected needs of the user. And in general, no additional information is provided for the system by the user, and all the information is automatically finished by the system, so that the cost of searching the information by the user is greatly reduced. The intelligent recommending system of the international logistics line is a specific application of personalized recommending service in international logistics selection. The recommendation method is not simple and can be carried out according to a certain related condition, but can be evaluated by combining a plurality of dimensions of a region where a user is located, use habits, consumption preferences and the like, and some most suitable results are selected and directly presented to the user. And meanwhile, a management background is provided, so that the calculated line result can be interfered, and the same conditions are recommended.
Disclosure of Invention
The invention provides an international logistics route recommending method, which aims to solve the technical problem of how to recommend a proper logistics route to a user in cross-border e-commerce shopping.
The technical problems of the invention are solved by the following technical scheme:
An international logistics line recommendation method comprises the following steps:
S1: collecting package order information submitted by a user;
S2: screening the package order information in the step S1 to form user behavior data and line characteristic data;
S3: analyzing the user behavior data in the step S2 to form a line selection data portrait; analyzing and excavating the line characteristic data in the step S2 to form historical line big data;
s4: and recommending a logistics line for the user according to the line selection data portrait and the historical line big data in the step S3.
Preferably, in step S3, the route selection data representation is formed by analyzing the user location, user consumption habit, user preference selection of inexpensive or expensive route, and user preference selection of route with long or short delivery time in the user behavior data.
Preferably, in step S3, the historical route big data is formed by analyzing and mining mailing time length, price, clearance time length, mailing limit, mailing country, region feature in the route characteristic data.
Preferably, in step S3, the line characteristic data and the user behavior data are stored in a line characteristic database and a user behavior database, respectively.
Preferably, in step S4, the method further includes configuring a priority of the logistics line, and adjusting a line recommendation sequence by adjusting the priority.
Preferably, in step S4, a collaborative filtering recommendation algorithm is used to recommend a logistic line to the user in combination with the information of the items to be sent.
Preferably, the user behavior data in step S2 is obtained by performing a cluster analysis on the user by using a bayesian classification algorithm.
Further, after cluster analysis is performed on the users, a user behavior model is built for various users.
Preferably, in step S2, the line characteristic data adopts a K-means clustering algorithm to perform cluster analysis on the logistics lines, and a line behavior model is built for each line.
Further, after the line behavior model is established, a machine learning method is adopted to correct and perfect the data of the logistics line.
The invention also provides a device adopting the international logistics line recommendation method, which comprises a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program in the memory to control the device adopting the international logistics line recommendation method to execute any one of the above methods.
Furthermore, the present invention also proposes a computer readable storage medium having stored thereon a computer program, said medium being used in combination with a computer, which program when executed by a processor realizes the steps of any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that: and screening the acquired package order information to obtain line characteristic data of a user behavior data set, analyzing the user behavior data to form a line selection data portrait, analyzing and mining the line characteristic data to form historical line big data, and recommending a proper international logistics line for a user according to the line selection data image and the historical line big data.
Drawings
Fig. 1 is a block diagram of an international logistics route recommendation system in accordance with an embodiment of the present invention.
FIG. 2 is a flow chart of data analysis in an embodiment of the invention.
Fig. 3 is a flow chart of line recommendation in an embodiment of the invention.
FIG. 4 is a flow chart of the international logistics route recommendation method of the present invention.
Detailed Description
The invention will be further described with reference to the following drawings in conjunction with the preferred embodiments.
An international logistics recommendation system, as shown in fig. 1, comprising:
A package order module: and processing the package order data submitted by the user, and synchronizing the package order data and the user information to the data acquisition module.
And a data preprocessing module: and the historical order data synchronized by the package order processing module is screened to form historical line big data and user behavior big data, and the historical line big data and the user behavior big data are respectively stored in a line characteristic database and a user behavior database.
User data analysis module: analyzing and mining the user history selection line data, analyzing the user history selection line data, and forming a user line selection data image. The user line selection data image is classified into a time-lapse type, an economical type, a remote type, and the like according to the user location, the user consumption habit, the user preference for selecting a line which is inexpensive or expensive, the user preference for selecting a line which is long in the conveyance time or short in the conveyance time, and the like.
Line data analysis module: analyzing and mining the data generated by the package preprocessing module, and establishing historical line big data, including line transportation time length, line closing capability, shipping article limit, line coverage, package shelf life and the like.
Line intelligent recommendation module: according to the line classification and characteristics analyzed by the line data analysis module, selecting a most suitable candidate line candidate list for a user by combining the user data image, and recommending the logistics line for the user by combining the priority of the added line for logistics line management.
The logistics line management module: and (3) managing the international logistics line, configuring basic data such as the cost, the transportation path, the sending timeliness and the like of the international logistics line, and configuring the priority of the logistics line, wherein the line recommendation result is influenced by adjusting the priority of the line.
The data analysis flow, as shown in FIG. 2, is as follows:
The data acquisition module collects historical package data from the package order module, performs primary data processing, eliminates useless fields and forms input data taking packages as dimensions.
The data preprocessing module preprocesses the received data taking the package as the dimension, decomposes the data taking the user as the dimension and the data taking the line as the dimension, and stores the data and the data into a corresponding distributed file system (such as HDFS) respectively.
The user data analysis module performs data analysis processing on the user data, calculates the behavior characteristics of the historical shipping package selection lines of each user, and forms a user behavior model, namely a line selection data portrait.
The line data analysis module performs data analysis processing on the line data, calculates the characteristics of each logistics line according to the historical data, performs cluster analysis on each logistics line, and establishes line model data.
The line recommendation flow is shown in fig. 3, and the detailed flow is as follows:
when the user submits the package sending request, the user information, the basic information (category, weight, volume) of the articles contained in the package, the sending destination and other basic information are submitted to the intelligent line recommending module.
And the line intelligent recommendation module screens out a line list which is most suitable for the user according to the line model data and the user historical behavior model data and the information of the sent article, and returns the line list to the user side for the user to select.
The line intelligent recommending module can be added with priority of the merchant in line configuration as a reference when selecting the line, and the line configured by the merchant is preferentially recommended when a plurality of lines are calculated, and the merchant effectively improves the profit of the merchant by preferentially recommending the logistics line with higher profit rate.
The information of the object to be sent includes: weight, volume, country of delivery, etc. The algorithm also considers the factors added through background configuration in the execution process, such as recommending lines with higher profit margin preferentially for the same type of logistics lines, and improves the profit of the company while improving the convenience of users.
Based on the above-mentioned international logistics line recommendation system, an international logistics line recommendation method is provided, as shown in fig. 4, comprising the following steps:
s1: collecting package order information submitted by a user; the package order information comprises package basic data such as package weight, package volume, mail item class, mail expense, mail country, region, mail line, mail time length, clearance time length and the like;
S2: screening the package order information in the step S1, screening out required fields, and removing fields irrelevant to line analysis to form user behavior data and line characteristic data; the user behavior data comprise basic data such as age, gender, nationality, residence, payment mode and the like; the line characteristic data comprise line basic information such as mailing duration, price, clearance duration, mailing limit, mailing country, region and the like;
S3: analyzing the user behavior data in the step S2 to form a line selection data image, wherein the user behavior data comprises a user place, a user consumption habit, a user preference selection of a cheap or expensive line and a user preference selection of a line with long or short transportation time; through data analysis, the users are found to like to select the lines with long sending period and low price, and the users can be classified as economic users; analyzing and excavating the line characteristic data in the step S2 to form historical line big data; the historical line big data comprise line transportation time, line closing capability, shipping article limit, line coverage range and the like; and in step S3, the line characteristic data and the user behavior data are respectively stored in a line characteristic database and a user behavior database. The line characteristics database and the user behavior database are provided with corresponding distributed file systems (HDFS); the user behavior data adopts a Bayesian classification algorithm to carry out cluster analysis on users, the users are divided into different types and marked with tag features, a line behavior model is established for various lines, namely, a line selection data image is formed, and the method comprises the following steps: time-efficient (time-critical), economical (preference for selecting inexpensive lines), remote (location remote users), etc., each user corresponding to at least one type according to its behavioral characteristics. The line characteristic data adopts a K-means clustering algorithm to perform clustering analysis on the logistics line, the logistics line is divided into different line types, and a line behavior model is built, and the method comprises the following steps: time-efficient (short shipping period), economical (price benefit), remote (coverage area shopping), packet (e.g. shipping only < = 2KG line), large (e.g. shipping only > = 5KG line), etc., each logistics line corresponds to at least one line type. The line selection data image is formed by analyzing the user location, the user consumption habit, the user bias selection of cheap or expensive lines and the user bias selection of long or short transportation time in the user behavior data; the historical line big data is formed by analyzing and mining mailing time length, price, clearance time length, mailing limit, mailing country and region characteristics in the line characteristic data.
It should be noted that the historical route big data includes route characteristic data formed by all packages, and one route selection data image corresponds to one user.
S4: and recommending a logistics line for the user according to the line selection data image and the historical line big data in the step S3 and the article information to be sent. In step S4, the method further includes configuring the priority of the logistics lines, where the configuration of the priority is that the priority is configured according to the weight of each logistics line, the greater the weight is, the higher the priority is, and by adjusting the priority to adjust the recommendation sequence of the lines, the priority control is an aid, and when a plurality of lines are all suitable for a certain user, the lines with high priority are recommended preferentially. In addition, the method also comprises basic data such as the cost, the transportation path, the sending time and the like for configuring the logistics line; in step S4, a collaborative filtering recommendation algorithm is adopted to recommend a logistics route for the user in combination with the item information to be sent. The logistic recommendation line can further comprise a dispatch point for recommending the package to the user, and when the dispatcher does not directly send the package to the addressee, the addressee can conveniently go to the dispatch point to get.
In this embodiment, after the line behavior model is built, a machine learning method is used to correct and perfect the data of the logistics line. Because the clustering algorithm has certain errors, the accuracy is more and more accurate along with the increase of the data volume of the historical line big data, the newly submitted package data of the user is used as input to update the user behavior data and the line characteristic data, and the user classification is re-divided, so that a positive feedback is achieved, and more accurate recommendation is realized.
The embodiment comprises a device adopting the international logistics line recommendation method, which comprises a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program in the memory to control the device adopting the international logistics line recommendation method to execute the method.
The present embodiment also includes a computer-readable storage medium having stored thereon a computer program for use in conjunction with a computer, which when executed by a processor, performs the steps of the method described above.
The international logistics route recommendation method of the present invention is exemplified below, but is not limited to the following specific examples. For the user who submits the package for the first time, a route selection data portrait is not established yet, the international logistics route recommendation is based on historical route big data, and the following is exemplified by a new user A:
201. the user A submits a meat package to the United kingdom, because the user A is a new user and has no line selection data portrait, the user can only recommend logistics lines for the user according to a line behavior model and a collaborative filtering recommendation algorithm, EMS and EUB (easy mail treasure) are recommended for the user, finally the user selects the EUB, and package basic data such as package weight, package volume, mail item category, mail expense, mail country, region, mail line, mail duration, clearance duration and the like are obtained by collecting package order information submitted by the user A.
202. Screening the package order information, screening out required fields, and removing fields irrelevant to line analysis to form user behavior data and line characteristic data; the user A behavior data comprise 48 years old, female sex, china international, land occupied, shanghai, paying mode, net bank, meat as mail items, 2KG of parcel weight and volume of parcel of 100cm 3; the line characteristic data includes mailing time of 20 days, price of 50 yuan, clear time of 1 day, mailing country of uk, region of scotland, and logistics of EUB.
203. Analyzing the behavior data of the user A to form a line selection data image, and obtaining a route which is long in sending period and low in price and liked by the user A through data analysis, so that the user A is classified as an economic user; analyzing and mining the line characteristic data to form historical line big data, and carrying out clustering analysis on the logistics line by adopting a K-means clustering algorithm to divide the logistics line into economic and small package types. It should be noted that the historical route big data includes route characteristic data which is formed by all packages before.
Steps 201-203 are to reestablish the route selection data portraits and route behavior models of user a based on the parcel characteristics of user a. Next, step 204, a new clothing package recommended international logistics line is submitted for user a:
204. According to the line selection data image and the line behavior model in the step 203, the collaborative filtering recommendation algorithm is adopted to recommend the logistics line for the user in combination with the article information of the clothes to be sent, and according to the data calculation analysis user characteristics and the type of the sent article, the logistics line such as AIR special line with lower price than Yi Bao is pushed for the user, and the logistics line recommendation is completed. The accuracy of recommending the international logistics routes according to the route selection data portraits and the route behavior models can be improved along with the continuous increase and updating of the data, when a user A submits a package, big data can be updated, and the corresponding route behavior models and route selection data portraits can be updated, so that the next more accurate recommendation is realized.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several equivalent substitutions and obvious modifications can be made without departing from the spirit of the invention, and the same should be considered to be within the scope of the invention.
Claims (8)
1. An international logistics line recommendation method is characterized by comprising the following steps:
S1: collecting package order information submitted by a user;
S2: screening the package order information in the step S1, screening out required fields, removing fields irrelevant to line analysis, and forming user behavior data and line characteristic data, namely decomposing data taking the package order information as dimensions into data taking a user as dimensions and data taking a line as dimensions;
S3: analyzing the user behavior data in the step S2 to form a line selection data portrait, wherein the user behavior data adopts a Bayesian classification algorithm to perform cluster analysis on users, the users are divided into different types, the users are marked with tag features, and each user corresponds to at least one type according to the behavior features; analyzing and excavating the line characteristic data in the step S2 to form historical line big data;
S4: recommending a logistics line for a user according to the line selection data portrait and the historical line big data in the step S3 and the object information to be sent;
In step S3, the route selection data representation is formed by analyzing the user location, user consumption habit, user preference selection of inexpensive or expensive route, and user preference selection of long or short transportation time in the user behavior data;
In step S3, the historical route big data is formed by analyzing and mining mailing duration, price, clearance duration, mailing limit, mailing country and region characteristics in the route characteristic data;
the package order information comprises package weight, package volume, items to be mailed, mailing cost, mailing country, region, mailing line, mailing duration and clearance duration;
the user behavior data comprise age, gender, nationality, residence and payment mode;
the historical line big data comprises line transportation time length, line closing capability, shipping article limit and line coverage.
2. The international logistics route recommendation method of claim 1, wherein the route characteristic data and the user behavior data are stored in a route characteristic database and a user behavior database, respectively, in step S3.
3. The international logistics line recommendation method of claim 1, further comprising configuring a priority of the logistics line, and adjusting a line recommendation order by adjusting the priority in step S4.
4. The international logistics line recommendation method as claimed in claim 1, wherein in step S4, a collaborative filtering recommendation algorithm is used to recommend logistics lines to users in combination with information of items to be sent.
5. The international logistics line recommendation method of claim 1, further comprising establishing a user behavior model for each type of user after performing the cluster analysis on the user.
6. The international logistics line recommendation method according to claim 1, wherein in step S2, the line characteristic data is clustered by a K-means clustering algorithm, a line behavior model is built for each line, and after the line behavior model is built, a machine learning method is used to correct and perfect the data of the logistics line.
7. An apparatus for using an international logistics route recommendation method, comprising a processor and a memory, the memory storing a computer program, the processor running the computer program in the memory to control the apparatus for an international logistics route recommendation method to perform the method of any one of claims 1-6.
8. A computer readable storage medium having stored thereon a computer program, the medium being for use in connection with a computer, the program when executed by a processor performing the steps of any of the methods of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710404861.XA CN107247762B (en) | 2017-06-01 | 2017-06-01 | International logistics line recommendation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710404861.XA CN107247762B (en) | 2017-06-01 | 2017-06-01 | International logistics line recommendation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107247762A CN107247762A (en) | 2017-10-13 |
| CN107247762B true CN107247762B (en) | 2024-05-14 |
Family
ID=60017837
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710404861.XA Active CN107247762B (en) | 2017-06-01 | 2017-06-01 | International logistics line recommendation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107247762B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909326B (en) * | 2017-11-30 | 2022-07-26 | 江门市链程科技有限公司 | Chain journey transportation method and system for international logistics |
| CN108921486B (en) * | 2018-07-25 | 2021-07-23 | 宏桥高科技集团有限公司 | Block chain based trade supply chain recommendation method and device |
| CN109871991A (en) * | 2019-01-30 | 2019-06-11 | 深圳市快金数据技术服务有限公司 | A kind of recommended method, device, equipment and the medium of logistics route |
| CN110991950A (en) * | 2019-11-07 | 2020-04-10 | 拓扑丝路(南京)科技有限公司 | Logistics service data processing method and device and storage medium |
| CN113076469B (en) * | 2020-01-03 | 2024-06-14 | 阿里巴巴集团控股有限公司 | Model construction method, data recommendation method and device |
| CN111242549A (en) * | 2020-01-14 | 2020-06-05 | 马晓翔 | Box separation method for E-commerce logistics |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103278833A (en) * | 2013-05-13 | 2013-09-04 | 深圳先进技术研究院 | Line recommendation system and method based on Beidou satellite/GPS (global positioning system) data |
| CN103473230A (en) * | 2012-06-06 | 2013-12-25 | 阿里巴巴集团控股有限公司 | Service range determining method, logistics service provider recommending method and corresponding device |
| CN104156836A (en) * | 2013-05-13 | 2014-11-19 | 阿里巴巴集团控股有限公司 | Logistic network load query method and system |
| CN105654267A (en) * | 2016-03-01 | 2016-06-08 | 淮阴工学院 | Cold-chain logistic stowage intelligent recommendation method based on spectral cl9ustering |
| CN106157081A (en) * | 2015-04-15 | 2016-11-23 | 金忠国 | A kind of international logistics e-commerce platform application system based on technology of Internet of things |
| WO2017045562A1 (en) * | 2015-09-17 | 2017-03-23 | 阿里巴巴集团控股有限公司 | Trading order payment information processing method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106548309A (en) * | 2015-09-18 | 2017-03-29 | 阿里巴巴集团控股有限公司 | Shipping Information processing method and processing device |
-
2017
- 2017-06-01 CN CN201710404861.XA patent/CN107247762B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473230A (en) * | 2012-06-06 | 2013-12-25 | 阿里巴巴集团控股有限公司 | Service range determining method, logistics service provider recommending method and corresponding device |
| CN103278833A (en) * | 2013-05-13 | 2013-09-04 | 深圳先进技术研究院 | Line recommendation system and method based on Beidou satellite/GPS (global positioning system) data |
| CN104156836A (en) * | 2013-05-13 | 2014-11-19 | 阿里巴巴集团控股有限公司 | Logistic network load query method and system |
| CN106157081A (en) * | 2015-04-15 | 2016-11-23 | 金忠国 | A kind of international logistics e-commerce platform application system based on technology of Internet of things |
| WO2017045562A1 (en) * | 2015-09-17 | 2017-03-23 | 阿里巴巴集团控股有限公司 | Trading order payment information processing method and device |
| CN105654267A (en) * | 2016-03-01 | 2016-06-08 | 淮阴工学院 | Cold-chain logistic stowage intelligent recommendation method based on spectral cl9ustering |
Non-Patent Citations (2)
| Title |
|---|
| "物流服务平台中推荐系统的框架设计";李宏基;《科技传播》;20101008;第235页 * |
| "面向物流配送领域的个性化推荐系统设计与实现";穆文忠;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150215;第5章 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107247762A (en) | 2017-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107247762B (en) | International logistics line recommendation method | |
| US20210295361A1 (en) | Method and server for providing a set of price estimates, such as air fare price estimates | |
| Höpken et al. | Business intelligence for cross-process knowledge extraction at tourism destinations | |
| CN104933049B (en) | Generate the method and system of Digital Human | |
| AU2014363194B2 (en) | Method and server for providing fare availabilities, such as air fare availabilities | |
| US20160191450A1 (en) | Recommendations Engine in a Layered Social Media Webpage | |
| US20170046653A1 (en) | Planning of transportation requests | |
| AU2013407812B2 (en) | Method and server for providing a set of price estimates, such as air fare price estimates | |
| CN107274257A (en) | Method and device is recommended in a kind of hotel service | |
| CN104820879A (en) | User behavior information analysis method and device thereof | |
| CN109902213A (en) | Real-time bus service circuit recommendation method, apparatus and electronic equipment | |
| Wu et al. | A personalized recommendation system for multi-modal transportation systems | |
| CN112632405A (en) | Recommendation method, device, equipment and storage medium | |
| CN112825182A (en) | Method and device for determining recommended commodities | |
| CN116029637A (en) | Cross-border electronic commerce logistics channel intelligent recommendation method and device, equipment and storage medium | |
| CN109146606B (en) | Brand recommendation method, electronic equipment, storage medium and system | |
| CN113537878A (en) | Package delivery method, device, equipment and storage medium | |
| CN110321516A (en) | A kind of universe tourism public service platform and its management system | |
| Anjali et al. | User profiling in travel recommender system using hybridization and collaborative method | |
| CN117114766A (en) | Cost control factor determining method, device, equipment and storage medium | |
| CN111460300A (en) | Network content pushing method and device and storage medium | |
| CN112541798A (en) | Shared wardrobe service system | |
| CN112632414A (en) | Method, apparatus, device, medium and program product for determining candidate get-off location | |
| Zelin et al. | Planning Above the API Clouds Before Flying Above the Clouds: A Real-Time Personalized Air Travel Planning Approach | |
| Liu et al. | Planning above the API clouds before flying above the clouds: a real-time personalized air travel planning approach |
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 | ||
| CP03 | Change of name, title or address |
Address after: 518000 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Patentee after: Xingjing Yunchuang Digital Technology (Shenzhen) Co.,Ltd. Country or region after: China Address before: 518000, Building AB, 5B, Tianzhan Building, Tian'an Digital City, Futian District, Shenzhen City, Guangdong Province Patentee before: SHENZHEN QIANHAI KUAHAIXIA KUAJING E-COMMERCE Co.,Ltd. Country or region before: China |