CN111626831A - Neural network-based ticketing method and ticketing system - Google Patents
Neural network-based ticketing method and ticketing system Download PDFInfo
- Publication number
- CN111626831A CN111626831A CN202010488295.7A CN202010488295A CN111626831A CN 111626831 A CN111626831 A CN 111626831A CN 202010488295 A CN202010488295 A CN 202010488295A CN 111626831 A CN111626831 A CN 111626831A
- Authority
- CN
- China
- Prior art keywords
- user
- seat
- neural network
- recommendation model
- identity
- 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.)
- Pending
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims abstract description 10
- 230000000306 recurrent effect Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 230000006872 improvement Effects 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000015654 memory Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006403 short-term memory Effects 0.000 description 2
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
The invention relates to the field of online services, in particular to a neural network-based ticketing method and a ticketing system. It includes: acquiring an identity of a user and user attention field data; extracting a preference sequence of the user based on the identity of the user, and extracting a seat factor based on the concerned session data; processing the preference sequence and the seat factors based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session, and selecting a plurality of seats with the highest selling probability to recommend to the user; wherein, the recommendation model is obtained by training from multi-user historical trading data.
Description
Technical Field
The invention relates to the field of online services, in particular to a neural network-based ticketing method and a ticketing system.
Background
The ticket business system utilizes high-tech product bar codes as passing electronic entrance tickets, and combines a plurality of high-tech technologies such as an electronic technology, a bar code recording technology, a computer network technology, an encryption technology and the like, thereby realizing the entrance guard control and management functions of various entrance ticket channels such as computer ticket selling, statistics, reports, anti-counterfeiting and the like, and having the omnibearing real-time monitoring and management functions.
However, the ticket buying operation is performed before the performance, and has uncertainty, such as that the buying of an improper position causes the experience to be degraded.
Disclosure of Invention
The invention provides a neural network-based ticketing method and a ticketing system.
Some embodiments of the invention are implemented as follows:
a neural network-based ticketing method, comprising:
acquiring an identity of a user and the user attention field data;
extracting a preference sequence of the user based on the identity of the user, and extracting a seat factor based on the field of interest data;
processing the preference sequence and the seat factors based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session, and selecting a plurality of seats with the highest selling probability to recommend to the user; wherein, the recommendation model is obtained by training from multi-user historical trading data.
In one embodiment of the invention:
extracting historical trading factors of the user based on the identity of the user;
and processing the preference sequence, the historical deal factor and the seat factor based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session.
In one embodiment of the invention:
the recommendation model is a recurrent neural network.
A neural network-based ticketing system, comprising:
the acquisition module is used for acquiring the identity of a user and the user attention field data;
the extraction module is used for extracting a preference sequence of the user based on the identity of the user and extracting a seat factor based on the concerned session data;
a recommendation module: processing the preference sequence and the seat factors based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session, and selecting a plurality of seats with the highest selling probability to recommend to the user; wherein, the recommendation model is obtained by training from multi-user historical trading data.
A neural network based ticketing apparatus, comprising a processor and a storage medium, wherein the storage medium is used for storing computer instructions, and the processor is used for executing at least a part of the computer instructions to realize the method.
The technical scheme of the invention at least has the following beneficial effects:
the method and the device can provide the seat information which is most suitable for the user accurately and quickly according to the characteristics of the user, the user can be conveniently handed in, the seat can be met and is most suitable for the user, the user experience is improved while the hand-in rate is improved, and the possibility of ordering the next platform is greatly increased.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic diagram of a neural network-based ticketing system in accordance with some embodiments of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Fig. 1 is a schematic diagram of a neural network-based ticketing system 100 that includes a user terminal 110, a network 120, and a server 130, according to some embodiments of the present application.
The user purchases a ticket or browses through the user terminal 110, which may include, but is not limited to, a mobile device, a tablet computer, a laptop computer, a desktop computer, etc., or any combination thereof. Further, in some embodiments, the user terminal may also be a device running APP, an applet, or the like.
The network 120 may be a wired network, a wireless network, a mobile network, or the like.
The server 130 may include one or more sub-processing devices (e.g., CPUs).
In the prior ticket selling process, an area with a front center is designed as an optimal area, and the optimal area radiates outwards to be arranged from the optimal area to the poor area. However, due to different experiences at different positions of a music scene, a performance scene and a movie playing scene, different people tend to be different, for example, a position where the number of beats in the music scene is forward but close to two sides is generally considered as a poor position, but the position is close to a stage although the music performance is poor, in a specific group (such as a star tracker), there is a chance that a photographer can conveniently take a picture or can be in close contact with the stage before playing, for example, in the performance session, standing waves sometimes appear at partial positions or stage interference occurs during playback, so that the most beautiful position is often in a position behind the center of the field, and a user with higher requirements for music quality should recommend in the region. Therefore, how to accurately utilize the scene information provides a proper position for the user, and the user experience is greatly related.
In order to utilize historical data and recommend a seat more suitable in a receiving price for a user when buying a ticket, the application provides a neural network-based ticketing method, which at least comprises the following steps:
s1: and acquiring the identity of the user and the user attention field data. This step may be performed by the acquisition module.
The id is information that can represent a user representation, such as age, location, gender, occupation, etc., for example, the information captured to the user may be represented as "23 year old female college loved a doll-man".
The user interest session may include session type (e.g., live, drama, meet, etc.), performer information (e.g., nationality, frequency of performance), and other information (e.g., number of tickets sold, cost), etc.
S2: a sequence of preferences for extracting the user based on the user's identity, and a seating factor based on the session of interest data. This step may be performed by the extraction module.
The identity of the user is extracted into a preference sequence, and in some cases, the identity can be spliced into the preference sequence after vectorization based on an encoder according to needs. The preference sequence may also include behavior traces that can be obtained, such as voting for a star on a platform or forwarding a star status on a social platform.
In some embodiments, the historical deal factor of the user is also extracted based on the identity of the user; the historical transaction factor represents the last in-platform ticket purchase amount, area, duration of the ticket purchase process, etc. Being able to obtain the historical trading factor simplifies the recommendation process, and in the following, it is described that the historical trading factor is not obtained, which does not mean that the factor is not obtained.
The process of extracting the seat factors in the concerned session can also be obtained by vectorizing the seats through an encoder, and the fares and experiences of the seats are different according to the difference of the seats.
S3: and processing the preference sequence and the seat factors based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session, and selecting a plurality of seats with the highest selling probability to recommend to the user. This step may be performed by the recommendation module.
Seat factors and preference sequence seats are input into a recommendation model, and a selling probability is obtained for each seat. And recommending a plurality of seats (such as 3 seats) with the highest selling probability to the client, and enabling the client to select. Reducing the time required to make a selection among a large number of seats.
In some embodiments, the recommendation model may be a recurrent neural network. Preferably, the model is a Long Short Term Memory artificial neural network (LSTM, Long Short-Term Memory). The long-short term memory artificial neural network is a time-cycle neural network, and is specially designed for solving the long-term dependence problem of the general RNN (recurrent neural network), and all the RNNs have a chain form of repeated neural network modules.
In some embodiments, the recommendation model may configure the convolutional neural network to assist in processing factor information in addition to the recurrent neural network described above.
In step S3, the recommendation model is obtained by training from multi-user historical deal data, that is, historical data (such as user sex, age, practice, field type, etc.) is used as training data to train the long-short term memory artificial neural network until the difference is small enough. Likewise, the training data may also include a training preference sequence and a training seat factor.
The method and the device can provide the seat information which is most suitable for the user accurately and quickly according to the characteristics of the user, the user can be conveniently handed in, the seat can be met and is most suitable for the user, the user experience is improved while the hand-in rate is improved, and the possibility of ordering the next platform is greatly increased.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
Claims (7)
1. A ticket selling method based on a neural network is characterized by comprising the following steps:
acquiring an identity of a user and the user attention field data;
extracting a preference sequence of the user based on the identity of the user, and extracting a seat factor based on the field of interest data;
processing the preference sequence and the seat factors based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session, and selecting a plurality of seats with the highest selling probability to recommend to the user; wherein, the recommendation model is obtained by training from multi-user historical trading data.
2. The method of claim 1, wherein:
extracting historical trading factors of the user based on the identity of the user;
and processing the preference sequence, the historical deal factor and the seat factor based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session.
3. The method of claim 1, wherein:
the recommendation model is a recurrent neural network.
4. A neural network-based ticketing system, comprising:
the acquisition module is used for acquiring the identity of a user and the user attention field data;
the extraction module is used for extracting a preference sequence of the user based on the identity of the user and extracting a seat factor based on the concerned session data;
a recommendation module: processing the preference sequence and the seat factors based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session, and selecting a plurality of seats with the highest selling probability to recommend to the user; wherein, the recommendation model is obtained by training from multi-user historical trading data.
5. The system of claim 4, wherein:
extracting historical trading factors of the user based on the identity of the user;
and processing the preference sequence, the historical deal factor and the seat factor based on the recommendation model to obtain the selling probability of each seat which can be sold in the concerned session.
6. The system of claim 4, wherein:
the recommendation model is a recurrent neural network.
7. A neural network based ticketing apparatus comprising a processor and a storage medium for storing computer instructions, the processor being configured to execute at least a portion of the computer instructions to implement a method as claimed in any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010488295.7A CN111626831A (en) | 2020-06-02 | 2020-06-02 | Neural network-based ticketing method and ticketing system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010488295.7A CN111626831A (en) | 2020-06-02 | 2020-06-02 | Neural network-based ticketing method and ticketing system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111626831A true CN111626831A (en) | 2020-09-04 |
Family
ID=72272858
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010488295.7A Pending CN111626831A (en) | 2020-06-02 | 2020-06-02 | Neural network-based ticketing method and ticketing system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111626831A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379514A (en) * | 2021-01-28 | 2021-09-10 | 北京沃东天骏信息技术有限公司 | Information recommendation method and device, electronic equipment and medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120010913A1 (en) * | 2010-07-08 | 2012-01-12 | Lele Avinash S | Systems and methods for managing empty seat inventory on an airplane |
| US20120226575A1 (en) * | 2011-03-03 | 2012-09-06 | Brett Ian Goldberg | Electronic ticket market |
| US20140019172A1 (en) * | 2012-07-12 | 2014-01-16 | Ebay, Inc. | User Preferred Venue Seating |
| US20150052001A1 (en) * | 2013-08-14 | 2015-02-19 | Mark Delun Yuan | User-specific seat recommendations based on common interests |
| WO2016092687A1 (en) * | 2014-12-12 | 2016-06-16 | 株式会社日立製作所 | Seat designation system and seat designation method |
| CN106233740A (en) * | 2013-11-13 | 2016-12-14 | 谷歌公司 | For presenting the recommendation method of items of media content, system and medium |
| CN109241431A (en) * | 2018-09-07 | 2019-01-18 | 腾讯科技（深圳）有限公司 | A kind of resource recommendation method and device |
| CN110097433A (en) * | 2019-05-14 | 2019-08-06 | 苏州大学 | Recommended method, device, equipment and storage medium based on attention mechanism |
| CN110245976A (en) * | 2019-05-20 | 2019-09-17 | 北京交通大学 | A kind of high-speed railway dynamic admission fee custom-built system and method based on big data |
| CN110287412A (en) * | 2019-06-10 | 2019-09-27 | 腾讯科技（深圳）有限公司 | Content recommendation method, recommended models generation method, equipment and storage medium |
| CN110503531A (en) * | 2019-08-30 | 2019-11-26 | 中国科学技术大学 | The dynamic social activity scene recommended method of timing perception |
| CN111144933A (en) * | 2019-12-13 | 2020-05-12 | 北京三快在线科技有限公司 | Commodity recommendation method and device, electronic equipment and storage medium |
-
2020
- 2020-06-02 CN CN202010488295.7A patent/CN111626831A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120010913A1 (en) * | 2010-07-08 | 2012-01-12 | Lele Avinash S | Systems and methods for managing empty seat inventory on an airplane |
| US20120226575A1 (en) * | 2011-03-03 | 2012-09-06 | Brett Ian Goldberg | Electronic ticket market |
| US20140019172A1 (en) * | 2012-07-12 | 2014-01-16 | Ebay, Inc. | User Preferred Venue Seating |
| US20150052001A1 (en) * | 2013-08-14 | 2015-02-19 | Mark Delun Yuan | User-specific seat recommendations based on common interests |
| CN106233740A (en) * | 2013-11-13 | 2016-12-14 | 谷歌公司 | For presenting the recommendation method of items of media content, system and medium |
| WO2016092687A1 (en) * | 2014-12-12 | 2016-06-16 | 株式会社日立製作所 | Seat designation system and seat designation method |
| CN109241431A (en) * | 2018-09-07 | 2019-01-18 | 腾讯科技（深圳）有限公司 | A kind of resource recommendation method and device |
| CN110097433A (en) * | 2019-05-14 | 2019-08-06 | 苏州大学 | Recommended method, device, equipment and storage medium based on attention mechanism |
| CN110245976A (en) * | 2019-05-20 | 2019-09-17 | 北京交通大学 | A kind of high-speed railway dynamic admission fee custom-built system and method based on big data |
| CN110287412A (en) * | 2019-06-10 | 2019-09-27 | 腾讯科技（深圳）有限公司 | Content recommendation method, recommended models generation method, equipment and storage medium |
| CN110503531A (en) * | 2019-08-30 | 2019-11-26 | 中国科学技术大学 | The dynamic social activity scene recommended method of timing perception |
| CN111144933A (en) * | 2019-12-13 | 2020-05-12 | 北京三快在线科技有限公司 | Commodity recommendation method and device, electronic equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 周万珍等: "推荐系统研究综述", 《河北科技大学学报》 * |
| 焦琛皓: "基于交易序列的推荐算法研究", 《计算机产品与流通》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379514A (en) * | 2021-01-28 | 2021-09-10 | 北京沃东天骏信息技术有限公司 | Information recommendation method and device, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108874823A (en) | The implementation method and device of intelligent customer service | |
| JP2022527640A (en) | Systems and methods for improving meeting engagement | |
| US20120179557A1 (en) | Performance Based Internet Reward System | |
| US20200273070A1 (en) | Systems and methods for leveraging social queuing to identify and prevent ticket purchaser simulation | |
| Redi et al. | Crowdsourcing for rating image aesthetic appeal: Better a paid or a volunteer crowd? | |
| US11216518B2 (en) | Systems and methods of providing recommendations of content items | |
| CN108650531A (en) | The method and system of video content Rapid matching product | |
| US20150006241A1 (en) | Analyzing participants of a social network | |
| CN111340565B (en) | Information recommendation method, device, equipment and storage medium | |
| CN110046806B (en) | Method and device for customer service order and computing equipment | |
| CN111754267A (en) | Data processing method and system based on block chain | |
| US20120166235A1 (en) | System and method for programmatically benchmarking performance of contact centers on social networks | |
| CN108647349A (en) | A kind of content recommendation method, device and terminal device | |
| US9665695B2 (en) | Systems and methods for ranking rules that identify potentially illegitimate activities | |
| CN109947814A (en) | Method and apparatus for the abnormal data group in detection data set | |
| CN111626831A (en) | Neural network-based ticketing method and ticketing system | |
| WO2016125166A1 (en) | Systems and methods for analyzing video and making recommendations | |
| CN109829593B (en) | Credit determining method and device for target object, storage medium and electronic device | |
| CN111737576B (en) | Application function personalized recommendation method and device | |
| US20230088484A1 (en) | Artificial Intelligence Assisted Live Sports Data Quality Assurance | |
| CN110198460A (en) | Choosing method and device, storage medium, the electronic device of media information | |
| US11188917B2 (en) | Systems and methods for compressing behavior data using semi-parametric or non-parametric models | |
| CN111865753A (en) | Method and device for determining parameters of media information, storage medium and electronic device | |
| CN110852338A (en) | User portrait construction method and device | |
| US11868942B2 (en) | Methods and systems for facilitating ranking agents making sales of real estate |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200904 |
|
| RJ01 | Rejection of invention patent application after publication |