CN107679943A - Intelligent recommendation method and system - Google Patents
Intelligent recommendation method and system Download PDFInfo
- Publication number
- CN107679943A CN107679943A CN201710889600.1A CN201710889600A CN107679943A CN 107679943 A CN107679943 A CN 107679943A CN 201710889600 A CN201710889600 A CN 201710889600A CN 107679943 A CN107679943 A CN 107679943A
- Authority
- CN
- China
- Prior art keywords
- commodity
- value
- similarity
- hot value
- recommendation
- 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
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
- 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
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention relates to a kind of intelligent recommendation method and system, methods described includes：Calculate the hot value of commodity；Obtain the commodity code for the current commodity that user browses；The similarity of other commodity and current commodity is calculated according to the commodity code；The recommendation of the relatively current commodity of other commodity is obtained according to the hot value and similarity；Other commodity are ranked up from high to low according to recommendation, by the predetermined number commercial product recommending for sorting forward to user.The intelligent recommendation method of the present invention is by calculating the hot value of commodity and similarity with current commodity, the recommendation of current commodity is obtained according to hot value and with the similarity of current commodity, according to commercial product recommending value size, the forward commercial product recommending of recommendation is taken to user, it is not only similarity height so to recommend commodity out, and fashion degree is also higher.Allow user to understand more commodity that represent fashion, trend, improve Consumer's Experience, platform and businessman can also be allowed to obtain more preferable achievement.
Description
Technical field
The present invention relates to commercial product recommending technical field, more particularly to a kind of intelligent recommendation method and system.
Background technology
Intelligent recommendation is mainly used in the commercial product recommending module of electric business platform, on the one hand allows user to be easier to find and meet it
The commodity needed, user's potential demand is on the one hand excavated, recommend suitable commodity to user.Therefore if intelligent recommendation system pushes away
The commodity recommended take into account the demand of experience and operation, it is possible to preferably improve Consumer's Experience, platform and businessman can also be allowed to obtain
More preferable achievement.
Common commercial product recommending technology on the market at present, commercial product recommending is mainly carried out according to commodity similarity, such as when
What preceding user browsed is A commodity, then other commodity are contrasted according to parameter and this commodity, ultimately generate the value of similarity,
Then finally it is pushed to user by being just ranked up on earth according to similarity.But the commodity generated according to similarity, it can lead
Family of applying has browsed an A commodity, and the commodity of recommendation are all closely similar, in the scene of the more fashion-orientation demands of current user
Under, these commodity recommended can not meet the needs of user's this respect personalization.
The content of the invention
In order to overcome the shortcomings of the prior art, the invention provides a kind of intelligent recommendation method and system.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of intelligent recommendation method, including：Calculate the hot value of commodity；Obtain the commodity code for the current commodity that user browses；
The similarity of other commodity and current commodity is calculated according to the commodity code；Other are obtained according to the hot value and similarity
The recommendation of the relatively current commodity of commodity；Other commodity are ranked up from high to low according to recommendation, will be sorted forward pre-
If quantity commercial product recommending is to user.
Preferably, the step of hot value for calculating commodity, includes：Every the platform inside trade of the first preset time crawl partner
The hot value of affiliated brand in the index and commodity platform that the product page returns；Every the more new commodity platform inside trade of the second preset time
The flow value of product；The hot value of commodity is worth to according to index, the hot value of affiliated brand and flow.
Preferably, partner's platform includes wechat, Baidu and microblogging；The step of hot value for calculating commodity, also wraps
Include：Belonging to being captured respectively in the index and commodity platform that the commodity page returns in wechat, Baidu, microblogging every the first preset time
The hot value of brand；Every the flow value of the second preset time commodity more in new commodity platform；By wechat index, Baidu's index,
Microblogging index, the hot value of affiliated brand and flow value carry out weight summation, obtain the hot value of commodity.
Preferably, the step of similarity that other commodity and current commodity are calculated according to the commodity code, includes：By its
His commodity, the parameters of current commodity are multiplied with each parameter accounting respectively, obtain the parameters value after weight；By weight
The parameters value of current commodity carries out Similarity Measure after the parameters value and weight of other commodity afterwards, obtains other
Commodity relative to current commodity similarity.
Preferably, the step of obtaining the recommendation of the relatively current commodity of other commodity according to the hot value and similarity is wrapped
Include：The hot value and Similarity value are subjected to weight summation, obtain recommendation.
A kind of intelligent recommendation system, including：Hot value computing module, similarity calculation module, recommendation computing module and
Recommending module；The hot value computing module, for calculating the hot value of commodity；The similarity calculation module, for obtaining
The commodity code for the current commodity that user browses；The similarity of other commodity and current commodity is calculated according to the commodity code；
The recommendation computing module, for obtaining the recommendation of the relatively current commodity of other commodity according to the hot value and similarity
Value；The recommending module, for other commodity to be ranked up from high to low according to recommendation, by the forward predetermined number that sorts
Commercial product recommending is to user.
Preferably, the hot value computing module, it is additionally operable to every commodity in the first preset time crawl partner platform
The hot value of affiliated brand in the index and commodity platform that the page returns；Every the second preset time commodity more in new commodity platform
Flow value；The hot value of commodity is worth to according to index, the hot value of affiliated brand and flow.
Preferably, partner's platform includes wechat, Baidu, microblogging；The hot value computing module, be additionally operable to every
First preset time captures affiliated brand in the index and commodity platform that the commodity page returns in wechat, Baidu, microblogging respectively
Hot value；Every the flow value of the second preset time commodity more in new commodity platform；Wechat index, Baidu's index, microblogging are referred to
The hot value and flow value of several, affiliated brand carry out weight summation, obtain the hot value of commodity.
Preferably, the similarity calculation module, be additionally operable to by other commodity, current commodity parameters respectively and respectively
Parameter accounting is multiplied, and obtains the parameters value after weight；By the parameters value of other commodity after weight and weight it
The parameters value of current commodity carries out Similarity Measure afterwards, obtains similarity of other commodity relative to current commodity.
Preferably, the recommendation computing module, it is additionally operable to the hot value and Similarity value carrying out weight summation, obtains
To recommendation.
The beneficial effects of the invention are as follows：The present invention intelligent recommendation method by calculate commodity hot value and with current business
The similarity of product, the recommendation of current commodity is obtained according to hot value and with the similarity of current commodity, according to commercial product recommending value
Size, taking the forward commercial product recommending of recommendation, it is not only that similarity is high so to recommend commodity out, fashion to user
Degree is also higher.Allow user to understand more commodity that represent fashion, trend, improve Consumer's Experience, can also allow platform and business
Family obtains more preferable achievement.
Brief description of the drawings
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 is the indicative flowchart of the intelligent recommendation method of an embodiment.
Fig. 2 is the indicative flowchart of the hot value of the calculating commodity of an embodiment.
Fig. 3 be an embodiment according to the commodity code calculate other commodity and current commodity similarity it is schematic
Flow chart.
Fig. 4 is the schematic diagram of the intelligent recommendation system of an embodiment.
Embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These accompanying drawings are simplified schematic diagram, only with
Illustration illustrates the basic structure of the present invention, therefore it only shows the composition relevant with the present invention.
Embodiment 1
Referring to Fig. 1, a kind of intelligent recommendation method, including：
S11, calculate the hot value of commodity；
S12, obtain the commodity code for the current commodity that user browses；
S13, the similarity of other commodity and current commodity is calculated according to the commodity code；
S14, the recommendation of the relatively current commodity of other commodity is obtained according to the hot value and similarity；
S15, other commodity are ranked up from high to low according to recommendation, by the predetermined number commercial product recommending for sorting forward to use
Family.
The commodity of this programme are wrist-watch.
Intelligent recommendation method is by calculating the hot value of commodity and similarity with current commodity, according to hot value and with working as
The similarity of preceding commodity obtains the recommendation of current commodity, according to commercial product recommending value size, takes the commercial product recommending that recommendation is forward
To user, it is not only similarity height so to recommend commodity out, and fashion degree is also higher, and client can be allowed fuller
Meaning.
Embodiment 2
Referring to Fig. 1-4, a kind of intelligent recommendation method, including：
S11, calculate the hot value of commodity；
In the present embodiment, step S11 includes：Every the index that the commodity page returns in the first preset time crawl partner platform
With the hot value of affiliated brand in commodity platform；Every the flow value of the second preset time commodity more in new commodity platform；According to
Index, the hot value of affiliated brand and flow are worth to the hot value of commodity.Further, partner's platform includes micro-
Letter, Baidu and microblogging；Step S11 includes：
S111, the index and commodity platform that the commodity page returns in wechat, Baidu, microblogging are captured respectively every the first preset time
The hot value of brand belonging to interior；Preferably, the first preset time is 24 hours.
S112, every the flow value of the second preset time commodity more in new commodity platform；Preferably, the second preset time
30min.The flow of commodity is the number of users for accessing the commodity page.Per the flow value of 30min commodity more in new commodity platform.
S113, wechat index, Baidu's index, microblogging index, the hot value of affiliated brand and flow value progress weight are asked
With obtain the hot value of commodity.As an alternative embodiment, wechat index, Baidu's index, microblogging index, the heat of affiliated brand
Angle value and flow value respectively account for the 20% of weight.Such as when obtaining the wechat index of certain commodity, Baidu's index, microblogging index, affiliated
The hot value and flow value of brand are respectively 82,56,88,85,50, then the hot value of certain commodity is 82*20%+56*20%+88*
20%+85*20%+50*20%=72.2.Preferably, per 30min, by wechat index, Baidu's index, microblogging index, affiliated brand
Hot value and flow value carry out weight summation, obtain the hot value of commodity.
S12, obtain the commodity code for the current commodity that user browses.When user browses commodity, front page layout returns
The commodity code of current page is calculated the similarity of other commodity and current commodity to backstage in real time.
S13, the similarity of other commodity and current commodity is calculated according to the commodity code；If commodity similarity calculates
Cross, can cache general 24 hours.
In the present embodiment, step S13 includes：
S131, the parameters of other commodity, current commodity are multiplied with each parameter accounting respectively, obtain the items after weight
Parameter value；Wherein parameters include：Brand, series, movement, watchcase, dial plate, function, parameter and sales volume, corresponding parameter account for
Than that can be 15%, 15%, 10%, 10%, 10%, 10%, 20%.
S132, the parameters value of current commodity after the parameters value and weight of other commodity after weight is carried out
Similarity Measure, obtain similarity of other commodity relative to current commodity.
For example, it is A currently to browse commodity, commodity to be compared are B, and x represents brand, and y represents series, and z1 represents movement, i.e.,
It is A currently to browse commodity（X1, y1...）, commodity to be compared are B（X2, y2...）, by taking two parameters as an example, each commodity are with working as
Before browse commodity calculating formula of similarity it is as follows：
If three parameters, then similarity is：
S14, the recommendation of the relatively current commodity of other commodity is obtained according to the hot value and similarity, further, will
The hot value and Similarity value carry out weight summation, obtain recommendation.Such as recommendation=hot value * 50%+ Similarity values *
50%。
S15, other commodity are ranked up from high to low according to recommendation, the predetermined number commercial product recommending that will sort forward
To user.Preferably, the commodity of 10 are to user before being recommended according to the recommendation of commodity.
The intelligent recommendation method of this programme is by calculating the hot value of commodity and similarity with current commodity, according to temperature
Value and the recommendation of current commodity is obtained with the similarity of current commodity, according to commercial product recommending value size, take recommendation forward
Commercial product recommending is to user, and it is not only similarity height so to recommend commodity out, and fashion degree is also higher.Allow user
The more commodity that represent fashion, trend of solution, improve Consumer's Experience, and platform and businessman can also be allowed to obtain more preferable achievement.
Embodiment 3
A kind of intelligent recommendation system, including：Hot value computing module 11, similarity calculation module 12, recommendation computing module 13
With recommending module 14；The hot value computing module 11, for calculating the hot value of commodity；The similarity calculation module 12,
The commodity code of the current commodity browsed for obtaining user；Other commodity and current commodity are calculated according to the commodity code
Similarity；The recommendation computing module 13, for obtaining the relatively current business of other commodity according to the hot value and similarity
The recommendation of product；The recommending module 14, for other commodity to be ranked up from high to low according to recommendation, it will sort forward
Predetermined number commercial product recommending to user.
In the present embodiment, the hot value computing module 11, it is additionally operable to every the first preset time crawl partner platform
The hot value of affiliated brand in the index and commodity platform that the interior commodity page returns；Every the second preset time more new commodity platform
The flow value of interior commodity；The hot value of commodity is worth to according to index, the hot value of affiliated brand and flow.Further,
Partner's platform includes wechat, Baidu, microblogging；The hot value computing module 11, is additionally operable to every the first preset time
The hot value of affiliated brand in the index and commodity platform that the commodity page returns in wechat, Baidu, microblogging is captured respectively；Every
The flow value of two preset times commodity more in new commodity platform；By wechat index, Baidu's index, microblogging index, affiliated brand
Hot value and flow value carry out weight summation, obtain the hot value of commodity.
In the present embodiment, the similarity calculation module 12, it is additionally operable to the parameters of other commodity, current commodity point
It is not multiplied with each parameter accounting, obtains the parameters value after weight；By the parameters value of other commodity after weight and
The parameters value of current commodity carries out Similarity Measure after weight, obtains other commodity relative to the similar of current commodity
Degree.
In the present embodiment, the recommendation computing module 13, it is additionally operable to the hot value and Similarity value carrying out weight
Summation, obtains recommendation.
The intelligent recommendation system of the present embodiment is by calculating the hot value of commodity and similarity with current commodity, according to heat
Angle value and the recommendation of current commodity is obtained with the similarity of current commodity, according to commercial product recommending value size, take recommendation forward
Commercial product recommending to user, it is not only similarity height so to recommend commodity out, and fashion degree is also higher.Allow user
Understand more commodity that represent fashion, trend, improve Consumer's Experience, platform and businessman can also be allowed to obtain more preferable achievement.
It is complete by above-mentioned description, relevant staff using the above-mentioned desirable embodiment according to the present invention as enlightenment
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property scope is not limited to the content on specification, it is necessary to determines its technical scope according to right.
Claims (10)
- A kind of 1. intelligent recommendation method, it is characterised in that including：Calculate the hot value of commodity；Obtain the commodity code for the current commodity that user browses；The similarity of other commodity and current commodity is calculated according to the commodity code；The recommendation of the relatively current commodity of other commodity is obtained according to the hot value and similarity；Other commodity are ranked up from high to low according to recommendation, by the predetermined number commercial product recommending for sorting forward to user.
- 2. intelligent recommendation method according to claim 1, it is characterised in that include the step of the hot value for calculating commodity：Every affiliated brand in the index and commodity platform that the commodity page returns in the first preset time crawl partner platform Hot value；Every the flow value of the second preset time commodity more in new commodity platform；The hot value of commodity is worth to according to index, the hot value of affiliated brand and flow.
- 3. intelligent recommendation method according to claim 2, it is characterised in that partner's platform includes wechat, Baidu And microblogging；The step of hot value for calculating commodity, also includes：Institute in the index and commodity platform that the commodity page returns in wechat, Baidu, microblogging is captured respectively every the first preset time Belong to the hot value of brand；Every the flow value of the second preset time commodity more in new commodity platform；Wechat index, Baidu's index, microblogging index, the hot value of affiliated brand and flow value are subjected to weight summation, obtain business The hot value of product.
- 4. intelligent recommendation method according to claim 1, it is characterised in that other commodity are calculated according to the commodity code Include with the step of similarity of current commodity：The parameters of other commodity, current commodity are multiplied with each parameter accounting respectively, obtain the parameters after weight Value；The parameters value of current commodity after the parameters value and weight of other commodity after weight is subjected to similarity meter Calculate, obtain similarity of other commodity relative to current commodity.
- 5. according to the intelligent recommendation method described in claim 1-4 any one, it is characterised in that according to the hot value and phase The step of obtaining the recommendation of the relatively current commodity of other commodity like degree includes：The hot value and Similarity value are subjected to weight summation, obtain recommendation.
- A kind of 6. intelligent recommendation system, it is characterised in that including：Hot value computing module, similarity calculation module, recommendation meter Calculate module and recommending module；The hot value computing module, for calculating the hot value of commodity；The similarity calculation module, the commodity code of the current commodity browsed for obtaining user；According to the commodity code Calculate the similarity of other commodity and current commodity；The recommendation computing module, for obtaining pushing away for the relatively current commodity of other commodity according to the hot value and similarity Recommend value；The recommending module, for other commodity to be ranked up from high to low according to recommendation, by the forward present count that sorts Commercial product recommending is measured to user.
- 7. intelligent recommendation system according to claim 6, it is characterised in that the hot value computing module, be additionally operable to every Every the hot value of affiliated brand in the index and commodity platform that the commodity page returns in the first preset time crawl partner platform；Every the flow value of the second preset time commodity more in new commodity platform；The hot value of commodity is worth to according to index, the hot value of affiliated brand and flow.
- 8. intelligent recommendation system according to claim 7, it is characterised in that partner's platform include wechat, Baidu, Microblogging；The hot value computing module, it is additionally operable to capture commodity page in wechat, Baidu, microblogging respectively every the first preset time The hot value of affiliated brand in the index and commodity platform that face returns；Every the flow value of the second preset time commodity more in new commodity platform；Wechat index, Baidu's index, microblogging index, the hot value of affiliated brand and flow value are subjected to weight summation, obtain business The hot value of product.
- 9. intelligent recommendation method according to claim 6, it is characterised in that the similarity calculation module, be additionally operable to by Other commodity, the parameters of current commodity are multiplied with each parameter accounting respectively, obtain the parameters value after weight；The parameters value of current commodity after the parameters value and weight of other commodity after weight is subjected to similarity meter Calculate, obtain similarity of other commodity relative to current commodity.
- 10. according to the intelligent recommendation system described in claim 6-9 any one, it is characterised in that the recommendation calculates mould Block, it is additionally operable to the hot value and Similarity value carrying out weight summation, obtains recommendation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710889600.1A CN107679943A (en) | 2017-09-27 | 2017-09-27 | Intelligent recommendation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710889600.1A CN107679943A (en) | 2017-09-27 | 2017-09-27 | Intelligent recommendation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107679943A true CN107679943A (en) | 2018-02-09 |
Family
ID=61136917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710889600.1A Pending CN107679943A (en) | 2017-09-27 | 2017-09-27 | Intelligent recommendation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107679943A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109033233A (en) * | 2018-06-29 | 2018-12-18 | 武汉斗鱼网络科技有限公司 | A kind of direct broadcasting room recommended method, storage medium, electronic equipment and system |
| CN110852846A (en) * | 2019-11-11 | 2020-02-28 | 京东数字科技控股有限公司 | Processing method and device for recommended object, electronic equipment and storage medium |
| CN112651805A (en) * | 2020-12-30 | 2021-04-13 | 广东各有所爱信息科技有限公司 | Commodity recommendation method and system for online shopping mall |
| CN113793182A (en) * | 2021-09-15 | 2021-12-14 | 广州华多网络科技有限公司 | Commodity object recommendation method and device, equipment, medium and product thereof |
| CN113793182B (en) * | 2021-09-15 | 2024-05-28 | 广州华多网络科技有限公司 | Commodity object recommendation method and device, equipment, medium and product thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030182295A1 (en) * | 2002-03-20 | 2003-09-25 | Fujitsu Limited | Location information recommending apparatus, method, and storage medium |
| CN105005579A (en) * | 2015-05-28 | 2015-10-28 | 携程计算机技术（上海）有限公司 | Personalized sorting method and system of hotel room types in OTA (Online Travel Agent) website |
| CN106651542A (en) * | 2016-12-31 | 2017-05-10 | 珠海市魅族科技有限公司 | Goods recommendation method and apparatus |
| CN106993030A (en) * | 2017-03-22 | 2017-07-28 | 北京百度网讯科技有限公司 | Information-pushing method and device based on artificial intelligence |
-
2017
- 2017-09-27 CN CN201710889600.1A patent/CN107679943A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030182295A1 (en) * | 2002-03-20 | 2003-09-25 | Fujitsu Limited | Location information recommending apparatus, method, and storage medium |
| CN105005579A (en) * | 2015-05-28 | 2015-10-28 | 携程计算机技术（上海）有限公司 | Personalized sorting method and system of hotel room types in OTA (Online Travel Agent) website |
| CN106651542A (en) * | 2016-12-31 | 2017-05-10 | 珠海市魅族科技有限公司 | Goods recommendation method and apparatus |
| CN106993030A (en) * | 2017-03-22 | 2017-07-28 | 北京百度网讯科技有限公司 | Information-pushing method and device based on artificial intelligence |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109033233A (en) * | 2018-06-29 | 2018-12-18 | 武汉斗鱼网络科技有限公司 | A kind of direct broadcasting room recommended method, storage medium, electronic equipment and system |
| CN110852846A (en) * | 2019-11-11 | 2020-02-28 | 京东数字科技控股有限公司 | Processing method and device for recommended object, electronic equipment and storage medium |
| CN112651805A (en) * | 2020-12-30 | 2021-04-13 | 广东各有所爱信息科技有限公司 | Commodity recommendation method and system for online shopping mall |
| CN112651805B (en) * | 2020-12-30 | 2023-11-03 | 广东各有所爱信息科技有限公司 | Commodity recommendation method and system for online mall |
| CN113793182A (en) * | 2021-09-15 | 2021-12-14 | 广州华多网络科技有限公司 | Commodity object recommendation method and device, equipment, medium and product thereof |
| CN113793182B (en) * | 2021-09-15 | 2024-05-28 | 广州华多网络科技有限公司 | Commodity object recommendation method and device, equipment, medium and product thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104102648B (en) | Interest based on user behavior data recommends method and device | |
| CN102346894B (en) | The output intent of recommendation information, system and server | |
| CN107066476A (en) | A kind of real-time recommendation method based on article similarity | |
| CN103870505B (en) | One kind inquiry words recommending method and query word commending system | |
| CN105426528B (en) | A kind of retrieval ordering method and system of commodity data | |
| CN105976212B (en) | Commodity display method and device and electronic commerce platform | |
| CN103729383A (en) | Push method and device for commodity information | |
| CN106997358A (en) | Information recommendation method and device | |
| CN103309894B (en) | Based on search implementation method and the system of user property | |
| CN105808685A (en) | Promotion information pushing method and device | |
| CN103823908A (en) | Method and server for content recommendation on basis of user preferences | |
| CN103106600A (en) | Commodity information push system and commodity information push method | |
| CN109360057A (en) | Information-pushing method, device, computer equipment and storage medium | |
| CN107679943A (en) | Intelligent recommendation method and system | |
| CN106850750A (en) | A kind of method and apparatus of real time propelling movement information | |
| CN103886001A (en) | Personalized commodity recommendation system | |
| CN103942193B (en) | A kind of method and device of information push | |
| CN104679835A (en) | Book recommending method based on multi-view hash | |
| CN105976229A (en) | Collaborative filtering algorithm based on user and project mixing | |
| CN104598450A (en) | Popularity analysis method and system of network public opinion event | |
| CN107730336A (en) | Commodity method for pushing and device in a kind of online transaction | |
| CN108319646B (en) | Vehicle source searching method and device based on user historical behaviors | |
| CN104615741A (en) | Cloud computing based cold start item recommending method and device | |
| CN107277115A (en) | A kind of content delivery method and device | |
| CN105844357A (en) | Dish combination recommending method and dish combination recommending system based on required heat |
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: 20180209 |
|
| RJ01 | Rejection of invention patent application after publication |