CN104951570A - Intelligent part-time job recommendation system based on data mining and LBS - Google Patents
Intelligent part-time job recommendation system based on data mining and LBS Download PDFInfo
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- CN104951570A CN104951570A CN201510443342.5A CN201510443342A CN104951570A CN 104951570 A CN104951570 A CN 104951570A CN 201510443342 A CN201510443342 A CN 201510443342A CN 104951570 A CN104951570 A CN 104951570A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Abstract
The invention discloses an intelligent part-time job recommendation system based on data mining and LBS. The intelligent part-time job recommendation system comprises an application client, a big data log memory pool, a big data mining system, a recommendation system buffer pool, a recommendation system database and an application pushing server, wherein the application client sets buried points on application pages and buttons for behavior counting, and writes related data into the big data log memory pool; the big data mining system regularly extract data from the big data log memory pool, performs data analysis according to a data mining algorithm, writes the analysis result into the recommendation system buffer pool, and synchronously persists the analysis result into the recommendation system database; the application pushing server searches matched information from the recommendation system buffer pool according to the LBS to obtain screening conditions suitable for a user, and pushes a part-time job information list which is best matched with the user to the application client. The intelligent part-time job recommendation system has the advantages that the matching degree between the recommended information and the user is improved, and the application experience of the user is greatly optimized.
Description
Technical field
The present invention relates to internet arena, specifically based on the part-time intelligent recommendation system of data mining and LBS.
Background technology
General part-time supplying system is all carry out simple information pushing for fixed-line subscriber colony, or pushes on a large scale for certain part time job, and user is difficult to the information getting precisely coupling.It is all the information that some matching degrees are not high that user receives major part every day, and the information such as pushed is all generally the temperature information that certain regional clicking rate is high, but is difficult to get up with pushed user's matched.
Summary of the invention
The object of the present invention is to provide the part-time intelligent recommendation system based on data mining and LBS of the matching degree improving recommendation information and user, to solve the problem proposed in above-mentioned background technology.
For achieving the above object, the invention provides following technical scheme:
Based on the part-time intelligent recommendation system of data mining and LBS, comprise APP client, large data log storage pond, large data digging system, commending system cache pool, commending system database and APP push server; APP client is carried out behavioral statistics to the APP page and button and is buried a little, first writes in local daily record temporarily; APP client also can remove log content originally by the local daily record copy of periodic replication simultaneously, then by http post request, local daily record copy push is uploaded to log processing service centre, log processing service centre reads User action log and writes data in large data log storage pond; Large data digging system regularly extracts the data in large data log storage pond by MapReduce, and carries out data analysis in conjunction with data mining algorithm; After data analysis, analysis result will be obtained; Analysis result writes in commending system cache pool by large data digging system, and is synchronously persisted in commending system database; APP push server is applicable to the screening conditions of this user in conjunction with LBS match query acquisition of information from commending system cache pool, from the part-time table commending system database, inquire the part time job list of mating most with user by screening conditions, be pushed to APP client.
As the further scheme of the present invention: large data log storage pond comprises hadoop, hbase cluster.
As the further scheme of the present invention: commending system cache pool adopts Redis cache cluster or kafka distributed information system.
As the further scheme of the present invention: analysis result comprises the label information of the label information of the part time job that user ID is mated with many, user ID and user tag information.
As the further scheme of the present invention: the part time job list of mating most with user is pushed to user with the form of JSON.
Compared with prior art, the invention has the beneficial effects as follows:
The present invention is according to doing data analysis and realizing the intelligent recommendation of part time job in conjunction with LBS technology user behavior data.Substantially increase the matching degree of recommendation information and user, significantly optimize the experience that user uses APP simultaneously.
Accompanying drawing explanation
Fig. 1 is structured flowchart of the present invention.
Embodiment
Below in conjunction with the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment 1
Refer to Fig. 1, in the embodiment of the present invention, based on the part-time intelligent recommendation system of data mining and LBS, comprise APP client, large data log storage pond, large data digging system, commending system cache pool, commending system database and APP push server.
Specific works process based on the part-time intelligent recommendation system of data mining and LBS is as described below.
1, APP push server active push information (backstage operation personnel):
The backstage operation personnel of APP push server can regularly prepare some and be applicable to doing the information (these information are mainly derived from urgent recruitment information, enterprise's value added service information, event promotion information etc.) of recommending, then log in supplying system and backstage operation system edit content recommendation to be pushed and set the screening conditions receiving user, click after pushing immediately.APP push server can according to the screening conditions waiting to push content and receive user of backstage operation personnel, in the user message table of commending system database, screen applicable user list, and draw the final user list being applicable to receiving this content recommendation in conjunction with the label information of " user ID <-> user tag information " in recommendation information cache pool.All the elements are combined into JSON bit string and send to user with http post form of asking by APP push server.Effective client (again reaching the standard grade after online client or client off-line) receives the JSON data that APP push server end active push is come, and is resolved JSON data and obtained to push content by set form.If APP client is minimized in background process, then pushed information will be shown to user with the form of informing and by data buffer storage to internal memory, local disk or SQLite database by APP client; When APP client is presented at mobile phone foreground (user just in use), then APP client is by the page of " part-time Fancy Match " module that is presented in APP client.User can check this detailed pushed information content pushed after clicking.
2, APP push server active push information (system centre):
System centre is according to commending system platform user liveness period rule, from commending system cache pool, regularly extract the data of " part time job of user ID <-> many coupling " form, and push JSON information by the form of concurrent poll to fixed-line subscriber, such as: " { " userId ": 10001, " job ": { " id ": " 001 ", " title ": " recommending part-time title ", " content ": " recommending part-time content ", " XXX ": " XXX " } } ", effective client (again reaching the standard grade after online client or client off-line) receives the JSON data that APP push server active push is come, resolve JSON data by set form and obtain and push content.If APP client is minimized in background process, then pushed information will be shown to user with the form of informing and by data buffer storage to internal memory, local disk or SQLite database by APP client; When APP client is presented at mobile phone foreground (user just in use), then pushed information is presented in the page of " part-time Fancy Match " module in APP client by APP client.User can check this detailed content pushed after clicking.
3, APP client active obtaining pushed information:
User logs in or opens APP client when entering homepage each time, first can position by calling mobile phone GPS, and the LBS information such as the longitude and latitude of acquisition and user profile be uploaded to APP push server simultaneously.Then, APP client can start a sub-thread and initiatively sends a http post and ask APP push server and wait for that APP push server information returns.After APP push server receives APP client-requested, can according to the LBS geographic position of user and user profile, and obtain in conjunction with the label information of " user ID <-> user tag information " in commending system cache pool the screening conditions being applicable to this user, then from the part-time table commending system database, the part time job list of mating most with user is inquired with these screening conditions, return to user with the form response of JSON, such as: " { " userId ": 10001, " jobList ": [{ " id ": " 001 ", " title ": " recommending part-time title 001 ", " content ": " recommending part-time content 001 ", " XXX ": " XXX " }, { " id ": " 002 ", " title ": " recommending part-time title 002 ", " XXX ": " XXX " }] } ".After APP client receives the JSON information that APP push server returns, JSON data can be resolved by set form and obtain the concrete propelling movement content recommendation part time job of user's matched (a string with).This heap part time job can be presented in the module of APP " you may be interested " by APP client.When the time comes, user just can browse to the part time job that APP push server is initiatively recommended.
In the present invention, MapReduce is a kind of programming model, for the concurrent operation of large-scale dataset (being greater than 1TB).Concept " Map(mapping) " and " Reduce(reduction) ", and their main thought, all borrow from Functional Programming, the characteristic of borrowing from vector programming language in addition.It is very easy to programming personnel when can not distributed parallel programming, the program of oneself is operated in distributed system.Current software simulating is that appointment Map(maps) function, be used for one group of key-value pair to be mapped to one group of new key-value pair, specify concurrent Reduce(reduction) function, each being used for ensureing in the key-value pair of all mappings shares identical key group.
Data mining algorithm is one group of trial method according to data creation data mining model and calculating.In order to model of creation, first algorithm will analyze the data provided, and searches pattern and the trend of particular type.
LBS, location Based service, it is the positional information (geographic coordinate being obtained mobile phone users by the radio communication network (as GSM net, CDMA net) of telecommunications mobile operator or outside locator meams (as GPS), or terrestrial coordinate), under the support of Geographic Information System (foreign language is abridged: GIS, foreign language full name: Geographic Information System) platform, for user provides a kind of value-added service of respective service.
To those skilled in the art, obviously the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when not deviating from spirit of the present invention or essential characteristic, the present invention can be realized in other specific forms.Therefore, no matter from which point, all should embodiment be regarded as exemplary, and be nonrestrictive, scope of the present invention is limited by claims instead of above-mentioned explanation, and all changes be therefore intended in the implication of the equivalency by dropping on claim and scope are included in the present invention.
In addition, be to be understood that, although this instructions is described according to embodiment, but not each embodiment only comprises an independently technical scheme, this narrating mode of instructions is only for clarity sake, those skilled in the art should by instructions integrally, and the technical scheme in each embodiment also through appropriately combined, can form other embodiments that it will be appreciated by those skilled in the art that.
Claims (5)
1. based on the part-time intelligent recommendation system of data mining and LBS, it is characterized in that, comprise APP client, large data log storage pond, large data digging system, commending system cache pool, commending system database and APP push server; APP client is carried out behavioral statistics to the APP page and button and is buried a little, first writes in local daily record temporarily; APP client also can remove log content originally by the local daily record copy of periodic replication simultaneously, then by http post request, local daily record copy push is uploaded to log processing service centre, log processing service centre reads User action log and writes data in large data log storage pond; Large data digging system regularly extracts the data in large data log storage pond by MapReduce, and carries out data analysis in conjunction with data mining algorithm; After data analysis, analysis result will be obtained; Analysis result writes in commending system cache pool by large data digging system, and is synchronously persisted in commending system database; APP push server is applicable to the screening conditions of this user in conjunction with LBS match query acquisition of information from commending system cache pool, from the part-time table commending system database, inquire the part time job list of mating most with user by screening conditions, be pushed to APP client.
2. the part-time intelligent recommendation system based on data mining and LBS according to claim 1, it is characterized in that, large data log storage pond comprises hadoop, hbase cluster.
3. the part-time intelligent recommendation system based on data mining and LBS according to claim 1, is characterized in that, commending system cache pool adopts Redis cache cluster or kafka distributed information system.
4. the part-time intelligent recommendation system based on data mining and LBS according to claim 1, is characterized in that, analysis result comprises the label information of the label information of the part time job that user ID is mated with many, user ID and user tag information.
5. the part-time intelligent recommendation system based on data mining and LBS according to claim 1, it is characterized in that, the part time job list of mating most with user is pushed to APP client by with the form of JSON.
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| CN106294650A (en) * | 2016-08-03 | 2017-01-04 | 北京金和网络股份有限公司 | Neologisms method for digging a little is buried based on search |
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| CN106598994A (en) * | 2015-10-20 | 2017-04-26 | 苏州贝弗安信息科技有限公司 | Efficient recruitment system and method based on on-line behavior data of user |
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| CN108763415A (en) * | 2018-05-23 | 2018-11-06 | 杭州优行科技有限公司 | Information processing method, device, electronic equipment and readable storage medium storing program for executing |
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| CN109493152A (en) * | 2017-09-11 | 2019-03-19 | 宋梓莘 | A kind of network data processing and matched combination method |
| CN110809050A (en) * | 2019-11-08 | 2020-02-18 | 智者四海（北京）技术有限公司 | Personalized push system and method based on streaming computing |
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| CN106598994A (en) * | 2015-10-20 | 2017-04-26 | 苏州贝弗安信息科技有限公司 | Efficient recruitment system and method based on on-line behavior data of user |
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| CN106294650B (en) * | 2016-08-03 | 2019-08-20 | 北京金和网络股份有限公司 | Neologisms method for digging a little is buried based on search |
| CN106294650A (en) * | 2016-08-03 | 2017-01-04 | 北京金和网络股份有限公司 | Neologisms method for digging a little is buried based on search |
| CN106528798A (en) * | 2016-11-11 | 2017-03-22 | 苏州天平先进数字科技有限公司 | Data processing system based on user logs |
| CN109313638A (en) * | 2017-03-09 | 2019-02-05 | 微软技术许可有限责任公司 | Using recommendation |
| CN109313638B (en) * | 2017-03-09 | 2023-09-01 | 微软技术许可有限责任公司 | Application recommendation |
| CN109493152A (en) * | 2017-09-11 | 2019-03-19 | 宋梓莘 | A kind of network data processing and matched combination method |
| CN107977828A (en) * | 2017-12-06 | 2018-05-01 | 朱喜 | Information processing method, device and server |
| CN108763415A (en) * | 2018-05-23 | 2018-11-06 | 杭州优行科技有限公司 | Information processing method, device, electronic equipment and readable storage medium storing program for executing |
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| CN113139833A (en) * | 2021-04-29 | 2021-07-20 | 杭州弧途科技有限公司 | Recommendation method based on user active time zone prediction and flow distribution optimization |
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