CN107169834A - A kind of method and apparatus that shopping recommendation is carried out based on big data - Google Patents
A kind of method and apparatus that shopping recommendation is carried out based on big data Download PDFInfo
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- CN107169834A CN107169834A CN201710349385.6A CN201710349385A CN107169834A CN 107169834 A CN107169834 A CN 107169834A CN 201710349385 A CN201710349385 A CN 201710349385A CN 107169834 A CN107169834 A CN 107169834A
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- 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
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- 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]
- G06Q30/0609—Buyer or seller confidence or verification
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Abstract
The present invention proposes a kind of method and apparatus that shopping recommendation is carried out based on big data, and wherein this method includes：Obtain the search keyword that user is inputted；Determine the corresponding merchandise news of the search keyword；The other users that there is interpersonal relationships with the user are determined from the big data of the user；Wherein, the other users nearer with the interpersonal relationships of the user, corresponding weighing factor is bigger；Purchaser record and weighing factor based on each other users are screened to the merchandise news, to determine merchandise news to be recommended；The merchandise news to be recommended is recommended into the user.Shopping recommendation is carried out to user by considering the influence of interpersonal relationships with this, the accurate shopping to user is realized and recommends, reduce the workload of user's screening, improve the purchase experiences of user.
Description
Technical field
It is more particularly to a kind of that the method for shopping recommendation is carried out based on big data and set the present invention relates to data processing field
It is standby.
Background technology
Now with the development of shopping at network, people increasingly tend to be done shopping on network, still, seller and
The abundant degree of commodity is too high, causes in current net purchase mode after user's input keyword, obtained result is too many, and uses
Want to find oneself desired commodity in family, it is desired nonetheless to which carrying out substantial amounts of screening by oneself artificial mode can just obtain, with
This causes the waste on the time and on energy, and then causes Consumer's Experience bad.
Therefore, needing a kind of mode more precisely done shopping and recommended to user at present.
The content of the invention
For defect of the prior art, the present invention proposes a kind of method based on big data progress shopping recommendation and set
It is standby, realize the accurate shopping to user and recommend.
Specifically, the present invention proposes embodiment in detail below：
The embodiment of the present invention proposes a kind of method that shopping recommendation is carried out based on big data, including：
Obtain the search keyword that user is inputted；
Determine the corresponding merchandise news of the search keyword；
The other users that there is interpersonal relationships with the user are determined from the big data of the user；Wherein, with it is described
The nearer other users of the interpersonal relationships of user, corresponding weighing factor is bigger；
Purchaser record and weighing factor based on each other users are screened to the merchandise news, to determine to treat
Recommendations information；
The merchandise news to be recommended is recommended into the user.
In a specific embodiment, if the quantity of the Recommendations information exceedes preset value；The merchandise news
Include the information of businessman；This method also includes：
Obtain delivery position and the reputation information of the businessman of each Recommendations information；
Far and near being weighed to businessman's set location of delivery position address corresponding with the user based on the businessman
Weight；
The businessman is ranked up from height to ground based on the reputation information, to generate prestige sequence；
The businessman of predetermined number is screened from the prestige sequence；
Recommendation sequence is generated from high to low according to position weight to the businessman filtered out；
It is described that the merchandise news to be recommended is recommended into the user, including：
Merchandise news to be recommended included by the recommendation sequence is recommended according to the sequence of the position weight
The user.
In a specific embodiment, after the merchandise news to be recommended recommended into the user, this method is also
Including：
Obtain the history purchaser record of the user；
The purchaser record of commodity in the merchandise news to be recommended is determined based on the commodity in the history purchaser record；
The weighing factor of the corresponding other users of commodity based on the purchaser record to being bought is adjusted；Wherein,
The quantity purchase of commodity is more in purchaser record, and the weighing factor of the corresponding other users of the commodity bought after adjustment is bigger.
In a specific embodiment, this method also includes：
Obtain the purchaser record of the user；
Judging to whether there is in the merchandise news has commodity consistent with the commodity in the purchaser record；
If the determination result is YES, then the commodity in the purchaser record are set to merchandise news to be recommended.
In a specific embodiment, the merchandise news to be recommended includes link；
Described " merchandise news to be recommended is recommended into the user ", including：
Extract the link in the merchandise news to be recommended；
The user is given by the link push.
The embodiment of the present invention also proposed a kind of equipment that shopping recommendation is carried out based on big data, including：
Acquisition module, for obtaining the search keyword that user is inputted；
First determining module, for determining the corresponding merchandise news of the search keyword；
Second determining module, for from the big data of the user determine with the user exist interpersonal relationships other
User；Wherein, the other users nearer with the interpersonal relationships of the user, corresponding weighing factor is bigger；
3rd determining module, for purchaser record and weighing factor based on each other users to the merchandise news
Screened, to determine merchandise news to be recommended；
Recommending module, for the merchandise news to be recommended to be recommended into the user.
In a specific embodiment, the equipment also includes：
Order module, for when the quantity of the Recommendations information exceedes preset value, obtaining each Recommendations
The delivery position of the businessman of information and reputation information；The merchandise news includes the information of businessman；
Far and near being weighed to businessman's set location of delivery position address corresponding with the user based on the businessman
Weight；
The businessman is ranked up from height to ground based on the reputation information, to generate prestige sequence；
The businessman of predetermined number is screened from the prestige sequence；
Recommendation sequence is generated from high to low according to position weight to the businessman filtered out；
The recommending module, is used for：
Merchandise news to be recommended included by the recommendation sequence is recommended according to the sequence of the position weight
The user.
In a specific embodiment, the equipment also includes：Adjusting module, is used for：By the merchandise news to be recommended
Recommend after the user, obtain the history purchaser record of the user；
The purchaser record of commodity in the merchandise news to be recommended is determined based on the commodity in the history purchaser record；
The weighing factor of the corresponding other users of commodity based on the purchaser record to being bought is adjusted；Wherein,
The quantity purchase of commodity is more in purchaser record, and the weighing factor of the corresponding other users of the commodity bought after adjustment is bigger.
In a specific embodiment, the recommending module is additionally operable to：
Obtain the purchaser record of the user；
Judging to whether there is in the merchandise news has commodity consistent with the commodity in the purchaser record；
If the determination result is YES, then the commodity in the purchaser record are set to merchandise news to be recommended.
In a specific embodiment, the merchandise news to be recommended includes link；
The recommending module, is used for：
Extract the link in the merchandise news to be recommended；
The user is given by the link push.
With this, the embodiment of the present invention proposes a kind of method and apparatus that shopping recommendation is carried out based on big data, wherein should
Method includes：Obtain the search keyword that user is inputted；Determine the corresponding merchandise news of the search keyword；Used from described
The other users that there is interpersonal relationships with the user are determined in the big data at family；Wherein, the interpersonal relationships with the user is got over
Near other users, corresponding weighing factor is bigger；Purchaser record and weighing factor based on each other users are to institute
State merchandise news to be screened, to determine merchandise news to be recommended；The merchandise news to be recommended is recommended into the user.With
This carries out shopping recommendation by considering the influence of interpersonal relationships to user, realizes the accurate shopping to user and recommends, reduces
The workload of user's screening, improves the purchase experiences of user.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be attached to what is used required in embodiment
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore is not construed as pair
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
Fig. 1 is a kind of schematic flow sheet for method that shopping recommendation is carried out based on big data that the embodiment of the present invention is proposed；
Fig. 2 is a kind of structural representation for equipment that shopping recommendation is carried out based on big data that the embodiment of the present invention is proposed；
Fig. 3 is a kind of structural representation for equipment that shopping recommendation is carried out based on big data that the embodiment of the present invention is proposed；
Fig. 4 is a kind of structural representation for equipment that shopping recommendation is carried out based on big data that the embodiment of the present invention is proposed.
Embodiment
Hereinafter, the various embodiments of the disclosure will be described more fully.The disclosure can have various embodiments, and
It can adjust and change wherein.It should be understood, however, that：It is limited to spy disclosed herein in the absence of by the various embodiments of the disclosure
Determine the intention of embodiment, but the disclosure should be interpreted as covering in the spirit and scope for the various embodiments for falling into the disclosure
All adjustment, equivalent and/or alternative.
Hereinafter, the term " comprising " that can be used in the various embodiments of the disclosure or " may include " indicate disclosed
Function, operation or the presence of element, and do not limit the increase of one or more functions, operation or element.In addition, such as existing
Used in the various embodiments of the disclosure, term " comprising ", " having " and its cognate are meant only to represent special characteristic, number
Word, step, operation, element, the combination of component or foregoing item, and be understood not to exclude first one or more other
Feature, numeral, step, operation, element, the presence of the combination of component or foregoing item or increase one or more features, numeral,
Step, operation, element, the possibility of the combination of component or foregoing item.
In the various embodiments of the disclosure, statement "or" or " in A or/and B at least one " include what is listed file names with
Any combinations of word or all combinations." A or B " or " in A or/and B at least one " may include A, may include for example, statement
B may include A and B both.
The statement (" first ", " second " etc.) used in the various embodiments of the disclosure can be modified to be implemented various
Various element in example, but corresponding element can not be limited.For example, presented above be not intended to limit the suitable of the element
Sequence and/or importance.The purpose presented above for being only used for differentiating an element and other elements.For example, the first user fills
Put and indicate different user device with second user device, although the two is all user's set.For example, not departing from each of the disclosure
In the case of the scope for planting embodiment, the first element is referred to alternatively as the second element, similarly, and the second element is also referred to as first
Element.
It should be noted that：, can be by the first composition member if an element ' attach ' to another element by description
Part is directly connected to the second element, and " connection " the 3rd can be constituted between the first element and the second element
Element.On the contrary, when an element " being directly connected to " is arrived into another element, it will be appreciated that be in the first element
And second the 3rd element is not present between element.
The term " user " used in the various embodiments of the disclosure, which may indicate that, to be used the people of electronic installation or uses electricity
The device (for example, artificial intelligence electronic installation) of sub-device.
The term used in the various embodiments of the disclosure is only used for describing the purpose of specific embodiment and not anticipated
In the various embodiments of the limitation disclosure.As used herein, singulative is intended to also include plural form, unless context is clear
Chu it is indicated otherwise.Unless otherwise defined, all terms (including the technical term and scientific terminology) tool being otherwise used herein
There is the implication identical implication that the various embodiment one skilled in the art with the disclosure are generally understood that.The term
(term such as limited in the dictionary typically used) is to be interpreted as having and the situational meaning in correlative technology field
Identical implication and it will be not construed as with Utopian implication or excessively formal implication, unless in the various of the disclosure
It is clearly defined in embodiment.
Embodiment 1
The embodiment of the present invention 1 discloses a kind of method that shopping recommendation is carried out based on big data, as shown in figure 1, including：
The search keyword that step 101, acquisition user are inputted；
The information that specific search keyword is inputted by user in search column, such as " computer ", " video card " etc., specifically
After the information of user's input is got, the information can also be handled, for example, carry out wrong word, or phonogram enters
Row corrigendum, so as to determine user really want input information, can also subsequently search further for it is identical with the information inputted or
The word of the similar word meaning of person, such as " CPU " and " processor ".
Step 102, determine the corresponding merchandise news of the search keyword；
Specifically, being scanned in a search engine according to search keyword, corresponding merchandise news can be obtained.
Step 103, from the big data of the user determine there are the other users of interpersonal relationships with the user；Its
In, the nearer other users with the interpersonal relationships of the user, corresponding weighing factor is bigger；
Specifically, the step is carried out based on six-point implicit scheme, specific six degree of separations (Six Degrees of
Separation) phenomenon (also known as " Small-World Phenomena " small world phenomenon), can generically be illustrated as：" you
The people being spaced between any one stranger is not over six, that is to say, that at most by six people, you can just recognize
Know any one strange.And interpersonal relationships is nearer, the probability of identical hobby is also bigger.
With this, based on six-point implicit scheme inquiry and user, there are the other users of interpersonal relationships, example in for example, user A
Such as user 1 and user A is good friend, then is directly contacted between user 1 and user A, then it is considered that user 1 and user A's is interpersonal
The distance of relation is 1, and if user 2 and user 1 are good friends, but do not recognize user A, then it is considered that user 2 and user A
The distance of interpersonal relationships be 2, by that analogy, the quantity of acquired other users is The more the better, but in processing the need for, can
Other users using selected distance as 1 and 2, or the other users that selected distance is 1；It can specifically be set as needed
Put.
In addition, setting weighing factor according to the far and near of the distance of interpersonal relationships, wherein distance is nearer, weighing factor is bigger,
Specifically, the weight of such as user 1 is set to 0.8, and the weight of user 2 is set to 0.2.
Step 104, the purchaser record based on each other users and weighing factor are screened to the merchandise news,
To determine merchandise news to be recommended；
Illustrated still exemplified by above-mentioned, get the purchaser record of other users, whether checked wherein to that should search
The corresponding merchandise news of rope keyword, if so, then screening corresponding other users as user is referred to, such as with reference to use
There are a user 1 and user 2 in family, and merchandise news is screened based on the weighing factor of user 1 and user 2, such as in user 1
The commodity 1 in merchandise news are have purchased, and user 2 have purchased the commodity 2 in merchandise news；Then commodity 1 and commodity 2 are defined as
Commodity to be recommended, as other commodity in merchandise news, then do not do and recommend, and recommend to be arranged according to weighing factor
Sequence, or only filter out weighing factor and buy commodity more than the other users of predetermined threshold value and be used as commodity to be recommended.
Still exemplified by above-mentioned, if same commodity, for example all there is the purchaser record in user 1 and user 2 in commodity 1 simultaneously
In, then the weighing factor of commodity 1 be the weighing factor of user 1 and user 2 and.
Step 105, the merchandise news to be recommended recommended into the user.
At one, if the quantity of the Recommendations information exceedes preset value；The merchandise news includes the letter of businessman
Breath；This method also includes：
Obtain delivery position and the reputation information of the businessman of each Recommendations information；
Far and near being weighed to businessman's set location of delivery position address corresponding with the user based on the businessman
Weight；
The businessman is ranked up from height to ground based on the reputation information, to generate prestige sequence；
The businessman of predetermined number is screened from the prestige sequence；
Recommendation sequence is generated from high to low according to position weight to the businessman filtered out；
It is described that the merchandise news to be recommended is recommended into the user, including：
Merchandise news to be recommended included by the recommendation sequence is recommended according to the sequence of the position weight
The user.
Specifically, the screening amount in order to further reduce user, the delivery position of the businessman based on commodity to be recommended and
Information is ranked up, and just recommends after sequence user.
In a specific embodiment, after the merchandise news to be recommended recommended into the user, this method is also
Including：
Obtain the history purchaser record of the user；
The purchaser record of commodity in the merchandise news to be recommended is determined based on the commodity in the history purchaser record；
The weighing factor of the corresponding other users of commodity based on the purchaser record to being bought is adjusted；Wherein,
The quantity purchase of commodity is more in purchaser record, and the weighing factor of the corresponding other users of the commodity bought after adjustment is bigger.
Specifically, the accuracy in order to provide this programme recommendation, also periodically obtains the history purchaser record of user, sentences
Relation between the product and merchandise news to be recommended of disconnected purchase, and weighing factor is adjusted according to the number of times of purchase, purchase
That buys is more, and corresponding weighing factor is bigger, and the number of times for the commodity for example bought based on user 1 at most, then can be by user 1
Weighing factor be adjusted to 0.9 from 0.8, other by that analogy, but adjustment after weighing factor also correspond to and the user
The nearer other users of interpersonal relationships, the bigger principle of corresponding weighing factor.
In a specific embodiment, this method also includes：
Obtain the purchaser record of the user；
Judging to whether there is in the merchandise news has commodity consistent with the commodity in the purchaser record；
If the determination result is YES, then the commodity in the purchaser record are set to merchandise news to be recommended.
Specifically, in view of the custom of user itself, same commodity were bought if having before, the probability bought again
It is very big, therefore it regard the commodity of purchase before itself as merchandise news to be recommended.
In a specific embodiment, the merchandise news to be recommended includes link；
Described " merchandise news to be recommended is recommended into the user ", including：
Extract the link in the merchandise news to be recommended；
The user is given by the link push.
Embodiment 2
The embodiment of the present invention 2 also discloses a kind of equipment that shopping recommendation is carried out based on big data, as shown in Fig. 2 including：
Acquisition module 201, for obtaining the search keyword that user is inputted；
First determining module 202, for determining the corresponding merchandise news of the search keyword；
Second determining module 203, for determining there is interpersonal relationships with the user from the big data of the user
Other users；Wherein, the other users nearer with the interpersonal relationships of the user, corresponding weighing factor is bigger；
3rd determining module 204, for purchaser record and weighing factor based on each other users to the commodity
Information is screened, to determine merchandise news to be recommended；
Recommending module 205, for the merchandise news to be recommended to be recommended into the user.
In a specific embodiment, as shown in figure 3, also including：
Order module 206, for when the quantity of the Recommendations information exceedes preset value, obtaining each recommendation business
The delivery position of the businessman of product information and reputation information；The merchandise news includes the information of businessman；
Far and near being weighed to businessman's set location of delivery position address corresponding with the user based on the businessman
Weight；
The businessman is ranked up from height to ground based on the reputation information, to generate prestige sequence；
The businessman of predetermined number is screened from the prestige sequence；
Recommendation sequence is generated from high to low according to position weight to the businessman filtered out；
The recommending module 205, is used for：
Merchandise news to be recommended included by the recommendation sequence is recommended according to the sequence of the position weight
The user.
In a specific embodiment, as shown in figure 4, also including：Adjusting module 207, is used for：By the business to be recommended
Product information recommendation is given after the user, obtains the history purchaser record of the user；
The purchaser record of commodity in the merchandise news to be recommended is determined based on the commodity in the history purchaser record；
The weighing factor of the corresponding other users of commodity based on the purchaser record to being bought is adjusted；Wherein,
The quantity purchase of commodity is more in purchaser record, and the weighing factor of the corresponding other users of the commodity bought after adjustment is bigger.
In a specific embodiment, the recommending module 205 is additionally operable to：
Obtain the purchaser record of the user；
Judging to whether there is in the merchandise news has commodity consistent with the commodity in the purchaser record；
If the determination result is YES, then the commodity in the purchaser record are set to merchandise news to be recommended.
In a specific embodiment, the merchandise news to be recommended includes link；
The recommending module 205, is used for：
Extract the link in the merchandise news to be recommended；
The user is given by the link push.
With this, the embodiment of the present invention proposes a kind of method and apparatus that shopping recommendation is carried out based on big data, wherein should
Method includes：Obtain the search keyword that user is inputted；Determine the corresponding merchandise news of the search keyword；Used from described
The other users that there is interpersonal relationships with the user are determined in the big data at family；Wherein, the interpersonal relationships with the user is got over
Near other users, corresponding weighing factor is bigger；Purchaser record and weighing factor based on each other users are to institute
State merchandise news to be screened, to determine merchandise news to be recommended；The merchandise news to be recommended is recommended into the user.With
This carries out shopping recommendation by considering the influence of interpersonal relationships to user, realizes the accurate shopping to user and recommends, reduces
The workload of user's screening, improves the purchase experiences of user.
It will be appreciated by those skilled in the art that accompanying drawing is a schematic diagram for being preferable to carry out scene, module in accompanying drawing or
Flow is not necessarily implemented necessary to the present invention.
It will be appreciated by those skilled in the art that the module in device in implement scene can be described according to implement scene into
Row is distributed in the device of implement scene, can also carry out one or more dresses that respective change is disposed other than this implement scene
In putting.The module of above-mentioned implement scene can be merged into a module, can also be further split into multiple submodule.
The invention described above sequence number is for illustration only, and the quality of implement scene is not represented.
Disclosed above is only several specific implementation scenes of the present invention, and still, the present invention is not limited to this, Ren Heben
What the technical staff in field can think change should all fall into protection scope of the present invention.
Claims (10)
1. a kind of method that shopping recommendation is carried out based on big data, it is characterised in that including：
Obtain the search keyword that user is inputted；
Determine the corresponding merchandise news of the search keyword；
The other users that there is interpersonal relationships with the user are determined from the big data of the user；Wherein, with the user
The nearer other users of interpersonal relationships, corresponding weighing factor is bigger；
Purchaser record and weighing factor based on each other users are screened to the merchandise news, to be recommended to determine
Merchandise news；
The merchandise news to be recommended is recommended into the user.
2. the method as described in claim 1, it is characterised in that if the quantity of the Recommendations information exceedes preset value；Institute
Stating merchandise news includes the information of businessman；This method also includes：
Obtain delivery position and the reputation information of the businessman of each Recommendations information；
The distance of delivery position address corresponding with the user based on the businessman is to businessman's set location weight；
The businessman is ranked up from height to ground based on the reputation information, to generate prestige sequence；
The businessman of predetermined number is screened from the prestige sequence；
Recommendation sequence is generated from high to low according to position weight to the businessman filtered out；
It is described that the merchandise news to be recommended is recommended into the user, including：
Merchandise news to be recommended included by the recommendation sequence is recommended according to the sequence of the position weight described
User.
3. the method as described in claim 1, it is characterised in that by the merchandise news to be recommended recommend the user it
Afterwards, this method also includes：
Obtain the history purchaser record of the user；
The purchaser record of commodity in the merchandise news to be recommended is determined based on the commodity in the history purchaser record；
The weighing factor of the corresponding other users of commodity based on the purchaser record to being bought is adjusted；Wherein, buy
The quantity purchase of commodity is more in record, and the weighing factor of the corresponding other users of the commodity bought after adjustment is bigger.
4. the method as described in claim 1, it is characterised in that also include：
Obtain the purchaser record of the user；
Judging to whether there is in the merchandise news has commodity consistent with the commodity in the purchaser record；
If the determination result is YES, then the commodity in the purchaser record are set to merchandise news to be recommended.
5. the method as described in claim 1, it is characterised in that the merchandise news to be recommended includes link；
Described " merchandise news to be recommended is recommended into the user ", including：
Extract the link in the merchandise news to be recommended；
The user is given by the link push.
6. a kind of equipment that shopping recommendation is carried out based on big data, it is characterised in that including：
Acquisition module, for obtaining the search keyword that user is inputted；
First determining module, for determining the corresponding merchandise news of the search keyword；
Second determining module, for other use for determining to there is interpersonal relationships with the user from the big data of the user
Family；Wherein, the other users nearer with the interpersonal relationships of the user, corresponding weighing factor is bigger；
3rd determining module, is carried out for purchaser record and weighing factor based on each other users to the merchandise news
Screening, to determine merchandise news to be recommended；
Recommending module, for the merchandise news to be recommended to be recommended into the user.
7. equipment as claimed in claim 6, it is characterised in that also include：
Order module, for when the quantity of the Recommendations information exceedes preset value, obtaining each Recommendations information
Businessman delivery position and reputation information；The merchandise news includes the information of businessman；
The distance of delivery position address corresponding with the user based on the businessman is to businessman's set location weight；
The businessman is ranked up from height to ground based on the reputation information, to generate prestige sequence；
The businessman of predetermined number is screened from the prestige sequence；
Recommendation sequence is generated from high to low according to position weight to the businessman filtered out；
The recommending module, is used for：
Merchandise news to be recommended included by the recommendation sequence is recommended according to the sequence of the position weight described
User.
8. equipment as claimed in claim 6, it is characterised in that also include：Adjusting module, is used for：By the commodity to be recommended
Information recommendation is given after the user, obtains the history purchaser record of the user；
The purchaser record of commodity in the merchandise news to be recommended is determined based on the commodity in the history purchaser record；
The weighing factor of the corresponding other users of commodity based on the purchaser record to being bought is adjusted；Wherein, buy
The quantity purchase of commodity is more in record, and the weighing factor of the corresponding other users of the commodity bought after adjustment is bigger.
9. equipment as claimed in claim 6, it is characterised in that the recommending module, is additionally operable to：
Obtain the purchaser record of the user；
Judging to whether there is in the merchandise news has commodity consistent with the commodity in the purchaser record；If judged result is
It is that the commodity in the purchaser record are then set to merchandise news to be recommended.
10. equipment as claimed in claim 6, it is characterised in that the merchandise news to be recommended includes link；
The recommending module, is used for：
Extract the link in the merchandise news to be recommended；
The user is given by the link push.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710349385.6A CN107169834A (en) | 2017-05-17 | 2017-05-17 | A kind of method and apparatus that shopping recommendation is carried out based on big data |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710349385.6A CN107169834A (en) | 2017-05-17 | 2017-05-17 | A kind of method and apparatus that shopping recommendation is carried out based on big data |
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| CN201710349385.6A Pending CN107169834A (en) | 2017-05-17 | 2017-05-17 | A kind of method and apparatus that shopping recommendation is carried out based on big data |
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108615182A (en) * | 2018-05-09 | 2018-10-02 | 广州链通互联网科技有限公司 | A kind of method and system that product intelligent is recommended |
| CN108647327A (en) * | 2018-05-11 | 2018-10-12 | 广东工业大学 | A kind of commodity purchasing decision-making technique, system and equipment and storage medium |
| CN108921649A (en) * | 2018-06-13 | 2018-11-30 | 苏州若依玫信息技术有限公司 | A kind of screening type e-commerce system based on Internet of Things |
| CN109558539A (en) * | 2018-11-23 | 2019-04-02 | 咪咕数字传媒有限公司 | Information recommendation method and device and computer readable storage medium |
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| CN109858985A (en) * | 2017-11-30 | 2019-06-07 | 阿里巴巴集团控股有限公司 | Merchandise news processing, the method shown and device |
| CN110163692A (en) * | 2018-01-30 | 2019-08-23 | 哈尔滨学院 | A kind of Method of Commodity Recommendation and its system based on big data |
| CN110689410A (en) * | 2019-09-29 | 2020-01-14 | 京东数字科技控股有限公司 | Data processing method, device, equipment and storage medium |
| CN110766478A (en) * | 2019-10-31 | 2020-02-07 | 深圳市云积分科技有限公司 | Method and device for improving user connectivity |
| CN111651682A (en) * | 2020-05-28 | 2020-09-11 | 广西东信互联科技有限公司 | System for mining circle-level social business value |
| CN111753158A (en) * | 2020-01-20 | 2020-10-09 | 全息空间(深圳)智能科技有限公司 | Live broadcast platform commodity searching method and device, computer equipment and storage medium |
| CN112288570A (en) * | 2020-11-23 | 2021-01-29 | 中国农业银行股份有限公司 | Data processing method and device |
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| CN109583985A (en) * | 2017-09-18 | 2019-04-05 | 林劲璋 | Online shopping system and method |
| CN109858985A (en) * | 2017-11-30 | 2019-06-07 | 阿里巴巴集团控股有限公司 | Merchandise news processing, the method shown and device |
| CN110163692A (en) * | 2018-01-30 | 2019-08-23 | 哈尔滨学院 | A kind of Method of Commodity Recommendation and its system based on big data |
| CN108615182A (en) * | 2018-05-09 | 2018-10-02 | 广州链通互联网科技有限公司 | A kind of method and system that product intelligent is recommended |
| CN108647327A (en) * | 2018-05-11 | 2018-10-12 | 广东工业大学 | A kind of commodity purchasing decision-making technique, system and equipment and storage medium |
| CN108921649A (en) * | 2018-06-13 | 2018-11-30 | 苏州若依玫信息技术有限公司 | A kind of screening type e-commerce system based on Internet of Things |
| CN109558539A (en) * | 2018-11-23 | 2019-04-02 | 咪咕数字传媒有限公司 | Information recommendation method and device and computer readable storage medium |
| CN110689410B (en) * | 2019-09-29 | 2021-09-03 | 京东数字科技控股有限公司 | Data processing method, device, equipment and storage medium |
| CN110689410A (en) * | 2019-09-29 | 2020-01-14 | 京东数字科技控股有限公司 | Data processing method, device, equipment and storage medium |
| CN110766478A (en) * | 2019-10-31 | 2020-02-07 | 深圳市云积分科技有限公司 | Method and device for improving user connectivity |
| CN111753158A (en) * | 2020-01-20 | 2020-10-09 | 全息空间(深圳)智能科技有限公司 | Live broadcast platform commodity searching method and device, computer equipment and storage medium |
| CN111651682A (en) * | 2020-05-28 | 2020-09-11 | 广西东信互联科技有限公司 | System for mining circle-level social business value |
| CN112288570A (en) * | 2020-11-23 | 2021-01-29 | 中国农业银行股份有限公司 | Data processing method and device |
| CN112288570B (en) * | 2020-11-23 | 2024-07-23 | 中国农业银行股份有限公司 | Data processing method and device |
| CN115935068A (en) * | 2022-12-12 | 2023-04-07 | 杭州洋驼网络科技有限公司 | Commodity recommendation method and device for Internet platform |
| CN115935068B (en) * | 2022-12-12 | 2023-09-05 | 杭州洋驼网络科技有限公司 | Commodity recommendation method and device for Internet platform |
| CN116611897A (en) * | 2023-07-19 | 2023-08-18 | 宜宾叙控科技有限公司 | Message reminding method and system based on artificial intelligence |
| CN116611897B (en) * | 2023-07-19 | 2023-10-13 | 北京快益通科技有限公司 | Message reminding method and system based on artificial intelligence |
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