CN106981011A - Recommendation method, device and the terminal of article - Google Patents
Recommendation method, device and the terminal of article Download PDFInfo
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- CN106981011A CN106981011A CN201710165879.9A CN201710165879A CN106981011A CN 106981011 A CN106981011 A CN 106981011A CN 201710165879 A CN201710165879 A CN 201710165879A CN 106981011 A CN106981011 A CN 106981011A
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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
Abstract
The disclosure is directed to recommendation method, device and the terminal of a kind of article.The recommendation method of the article includes：The purchase data of every class article of purchase are obtained based on purchaser record；Determine that the next time of correspondence classification article refers to the time buying based on the purchase data；When with reference to the time buying being the first preset time apart from the next time, relative article is recommended for the correspondence classification article.Pass through above-described embodiment, terminal according to purchase data by determining that the next time of all kinds of articles refers to the time buying, so as to only when the distance next reference time buying is the first preset time, the push of relative article is just carried out to user, so as to enhance the specific aim of recommendation, avoid what excessive similar recommendation caused to user from bothering, optimize Consumer's Experience.
Description
Technical field
This disclosure relates to field of terminal technology, more particularly to a kind of article recommendation method, device and terminal.
Background technology
With the development of Internet technology and electric business, electric business platform is directed to the purchaser record of user, can make one to user
It is a little to recommend, ware is for example recommended to user according to the record that browses of user, or according to the purchaser record of user to user
Recommend ware, etc..
But in fact, for many articles, such as white goods, it is short-term after user's purchase within can't purchase again
Buy, thus frequently recommend ware to cause to bother to user, it is impossible to meet the demand of user, Consumer's Experience is not good enough.
The content of the invention
To overcome problem present in correlation technique, the embodiment of the present disclosure provide a kind of recommendation method of article, device and
Terminal, the Item Information to solve to push in correlation technique can not meet the real demand of user, or even user is caused to beat
The problems such as disturbing.
According to the first aspect of the embodiment of the present disclosure there is provided a kind of recommendation method of article, including：
The purchase data of every class article of purchase are obtained based on purchaser record；
Determine that the next time of correspondence classification article refers to the time buying based on the purchase data；
When with reference to the time buying being the first preset time apart from the next time, phase is recommended for the correspondence classification article
Close article in one embodiment,
In one embodiment, the purchase data of every class article that purchase is obtained based on purchaser record, including：
Obtain purchaser record；
The Item Title and time buying information of every class article of purchase are obtained according to purchaser record；
The goods categories affiliated respectively per class article are determined according to the Item Title；
The time buying interval of every class article of purchase is determined according to the time buying information.
It is in one embodiment, described that the goods categories affiliated respectively per class article are determined according to the Item Title,
Including：
Pre-stored taxonomy of goods table is searched based on the Item Title, article class corresponding with the Item Title is obtained
Not.
It is in one embodiment, described to determine that the next time of correspondence classification article refers to the time buying based on the purchase data,
Including：
According to multiple time buying interval calculation average time intervals of every class article for purchase；
The next time of correspondence classification article is calculated with reference to purchase based on the average time interval and last time time buying
Time.
It is in one embodiment, described to determine that the next time of correspondence classification article refers to the time buying based on the purchase data,
Including：
It is determined that for the shortest time interval in multiple time buying intervals of every class article of purchase；
The next time of correspondence classification article is calculated with reference to purchase based on the shortest time interval and last time time buying
Time.
It is in one embodiment, described to determine that the next time of correspondence classification article refers to the time buying based on the purchase data,
Including：
When the purchase number of times of every class article of purchase is one time, the average reference purchase of the goods categories of correspondence article is obtained
Buy the time；
Purchase next time based on average reference time buying classification article corresponding with the calculating of last time time buying
Time.
It is in one embodiment, described to determine that the next time of correspondence classification article refers to the time buying based on the purchase data,
Including：
When the article of purchase is food, the Item Title based on the food determines the shelf-life；
Determine the quantity purchase of the food；
Reference purchase next time based on the shelf-life, last time time buying classification article corresponding with quantity purchase determination
Buy the time.
In one embodiment, it is described apart from it is described next time with reference to the time buying be the first preset time when, for described
Correspondence classification article recommends relative article, including：
Goods attribute based on the correspondence classification article determines relative article to be recommended；
When with reference to the time buying being the first preset time apart from the next time, the thing of the relative article to be recommended is pushed
Product information.
In one embodiment, the goods attribute includes one or more in brand, the place of production, price, color, size.
In one embodiment, it is described based on the purchase data determine next time of correspondence classification article with reference to the time buying it
Afterwards, methods described also includes：
Determine that the association of the correspondence classification article uses article；
When before first preset time being the second preset time, article of the association using article is pushed
Information.
According to the second aspect of the embodiment of the present disclosure there is provided a kind of recommendation apparatus of article, including：
Acquisition module, is configured as obtaining the purchase data of every class article of purchase based on purchaser record；
First determining module, when being configured as determining that the next time of correspondence classification article is with reference to purchase based on the purchase data
Between；
Recommending module, be configured as apart from it is described next time with reference to the time buying be the first preset time when, for described
Correspondence classification article recommends relative article.
In one embodiment, the acquisition module includes：
First acquisition submodule, is configured as obtaining purchaser record；
Second acquisition submodule, is configured as obtaining Item Title and the purchase for the every class article bought according to purchaser record
Temporal information；
First determination sub-module, is configured as determining the article affiliated respectively per class article according to the Item Title
Classification；
Second determination sub-module, when being configured as determining the purchase of every class article of purchase according to the time buying information
Between be spaced.
In one embodiment, first determination sub-module includes：
Search submodule, be configured as searching pre-stored taxonomy of goods table based on the Item Title, obtain with it is described
The corresponding goods categories of Item Title.
In one embodiment, first determining module includes：
First calculating sub module, is configured as being put down according to multiple time buying interval calculations of every class article for purchase
Equal time interval；
Second calculating sub module, is configured as calculating correspondence based on the average time interval and last time time buying
The next time of classification article refers to the time buying.
In one embodiment, first determining module includes：
3rd determination sub-module, is configured to determine that in multiple time buying intervals for every class article of purchase most
Short time interval；
3rd calculating sub module, is configured as calculating correspondence based on the shortest time interval and last time time buying
The next time of classification article refers to the time buying.
In one embodiment, first determining module includes：
3rd acquisition submodule, is configured as, when the purchase number of times of every class article of purchase is one time, obtaining homologue
The average reference time buying of the goods categories of product；
4th calculating sub module, is configured as calculating based on the average reference time buying and last time time buying
The next time buying of correspondence classification article.
In one embodiment, first determining module includes：
4th determination sub-module, is configured as when the article of purchase is food, the Item Title based on the food is true
Determine the shelf-life；
5th determination sub-module, is configured to determine that the quantity purchase of the food；
6th determination sub-module, is configured as determining based on the shelf-life, last time time buying and quantity purchase
The next time of correspondence classification article refers to the time buying.
In one embodiment, the recommending module includes：
7th determination sub-module, is configured as the goods attribute based on the correspondence classification article and determines correlative to be recommended
Product；
First push submodule, be configured as apart from it is described next time with reference to the time buying be the first preset time when, push away
Send the Item Information of the relative article to be recommended.
In one embodiment, the goods attribute includes one or more in brand, the place of production, price, color, size.
In one embodiment, described device also includes：
Second determining module, is configured to determine that the association of the correspondence classification article uses article；
Pushing module, is configured as, when before first preset time being the second preset time, pushing described
Association uses the Item Information of article.
According to the third aspect of the embodiment of the present disclosure there is provided a kind of terminal, including：
Processor；
Memory for storing processor-executable instruction；
Wherein, the processor is configured as：
The purchase data of every class article of purchase are obtained based on purchaser record；
Determine that the next time of correspondence classification article refers to the time buying based on the purchase data；
When with reference to the time buying being the first preset time apart from the next time, phase is recommended for the correspondence classification article
Close article.
The technical scheme provided by this disclosed embodiment can include the following benefits：
Terminal in the disclosure according to purchase data by determining that the next time of all kinds of articles refers to the time buying, so as to only exist
When the distance next reference time buying is the first preset time, the push of relative article is just carried out to user, is pushed away so as to enhance
The specific aim recommended, it is to avoid what excessive similar recommendation was caused to user bothers, optimizes Consumer's Experience.
It should be appreciated that the general description of the above and detailed description hereinafter are only exemplary and explanatory, not
The disclosure can be limited.
Brief description of the drawings
Accompanying drawing herein is merged in specification and constitutes the part of this specification, shows the implementation for meeting the present invention
Example, and for explaining principle of the invention together with specification.
Figure 1A is a kind of flow chart of the recommendation method of article according to an exemplary embodiment.
Figure 1B is a kind of scene graph of the recommendation method of article according to an exemplary embodiment.
Fig. 2 is the flow chart of the recommendation method of another article according to an exemplary embodiment.
Fig. 3 is the flow chart of the recommendation method of another article according to an exemplary embodiment.
Fig. 4 is the flow chart of the recommendation method of another article according to an exemplary embodiment.
Fig. 5 is the flow chart of the recommendation method of another article according to an exemplary embodiment.
Fig. 6 is the flow chart of the recommendation method of another article according to an exemplary embodiment.
Fig. 7 is a kind of block diagram of the recommendation apparatus of article according to an exemplary embodiment.
Fig. 8 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Fig. 9 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 10 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 11 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 12 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 13 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 14 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 15 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment.
Figure 16 is a kind of block diagram of recommendation apparatus suitable for article according to an exemplary embodiment.
Embodiment
Here exemplary embodiment will be illustrated in detail, its example is illustrated in the accompanying drawings.Following description is related to
During accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represent same or analogous key element.Following exemplary embodiment
Described in embodiment do not represent and the consistent all embodiments of the present invention.On the contrary, they be only with it is such as appended
The example of the consistent apparatus and method of some aspects be described in detail in claims, the present invention.
Figure 1A is a kind of flow chart of the recommendation method of article according to an exemplary embodiment, and Figure 1B is according to one
A kind of scene graph of the recommendation method of article shown in exemplary embodiment, the recommendation method of the article can be applied in terminal
On, the terminal in the disclosure can be any intelligent terminal with function of surfing the Net, for example, can be specially mobile phone, flat board electricity
Brain, PDA (Personal Digital Assistant, personal digital assistant) etc..Wherein, terminal can pass through WLAN
Couple in router, and pass through the server on router access public network.As shown in Figure 1A, the recommendation method of the article includes following
Step 101-103：
In a step 101, the purchase data of every class article of purchase are obtained based on purchaser record.
In one embodiment, shopping class App (Application, application program) can be installed in terminal, for example, washed in a pan
Treasured, Jingdone district etc., user can be bought in commodity, App by the class App that does shopping and the Shopping Behaviors of user can be recorded, for example
The information such as browsed commodity, the commodity bought, time, title, the amount of money of commodity of purchase, terminal is by reading in App
Purchaser record, the purchase data for every class article that user is bought can be obtained.
In one embodiment, purchase data can include time buying information, the Item Title for buying article etc..
In a step 102, the next time for determining correspondence classification article based on purchase data refers to the time buying.
In one embodiment, terminal estimates next ginseng based on data such as goods categories, the time buyings belonging to purchase article
Examine the time buying.
For example, for article " mobile phone ", " camera ", " iPad ", terminal can determine that the goods categories belonging to it are electronics
Digital article, can determine that next time refers to the time buying based on user to the time buying of above-mentioned electronic digital article.
In step 103, when the distance next reference time buying is the first preset time, pushed away for correspondence classification article
Recommend relative article.
In one embodiment, terminal determines next time with reference to after the time buying, with reference to the time buying is the in distance next time
During one preset time associated recommendation is carried out for correspondence classification article.
For example：Next time is December 20 with reference to the time buying, it is assumed that the first preset time is 2 days, then terminal can be
The relative article for being directed to correspondence classification article on December 18 is recommended.
In an exemplary scenario, as shown in Figure 1B, including as the smart mobile phone of terminal, purchase is installed on smart mobile phone
Thing App.Terminal, which can read the sequence information of user and obtain recording in the purchaser record of user, purchaser record, has bought thing
The information such as Item Title, time buying, the businessman of product.The article class belonging to the article is can determine by Item Title terminal
Not, so that the article bought be classified according to affiliated goods categories, and when determining the purchase of every class article of purchase
Between.Terminal determines to refer to the time buying next time based on the time buying, and when it is first default that distance next time is with reference to the time buying
Between when, such as next time, with reference to the previous day of time buying, recommends relative article, the relative article can be with for correspondence classification article
For ware, or association uses article, etc..
Specifically how article recommendation is carried out, refer to subsequent embodiment.
So far, because after user's purchase article, terminal can carry out similar for the article bought in correlation technique
The recommendation of article, and actually many articles have use and cycles consumed, user buy certain part article after in a short time or
Can't buy ware in certain period of time again, thus frequently the promotion expo of various wares caused to user need not
That wants bothers, and Consumer's Experience is not good enough.And the above method that the embodiment of the present disclosure is provided, by determining all kinds of things according to purchase data
The next time of product refers to the time buying, so as to only just enter when the distance next reference time buying is the first preset time to user
The push of row relative article, so as to enhance the specific aim of recommendation, it is to avoid what excessive similar recommendation was caused to user bothers, excellent
Change Consumer's Experience.
Illustrate the technical scheme that the embodiment of the present disclosure is provided with specific embodiment below.
Fig. 2 is the flow chart of the recommendation method of another article according to an exemplary embodiment, the present embodiment profit
The above method provided with the embodiment of the present disclosure, using how based on purchaser record determine purchase every class article purchase data as
Example is illustrative, as shown in Fig. 2 comprising the following steps 201-203：
In step 201, purchaser record is obtained.
In one embodiment, terminal can get purchaser record by reading the sequence information of user in shopping App.
For example, in Taobao App " my order ", there are Item Title, quantity, color, size, price, purchase that user bought
The purchaser record such as time and shop title.
In step 202., the Item Title and time buying information for the every class article bought according to purchaser record.
In one embodiment, terminal can carry out keyword extraction to purchaser record, so that when obtaining Item Title, purchase
Between information etc..
In step 203, the goods categories according to belonging to Item Title determines every class article difference of purchase.
In one embodiment, be stored with Item Title and the taxonomy of goods table of goods categories in terminal, and terminal is based on article
Title searches the taxonomy of goods table, can obtain goods categories corresponding with the Item Title.
For example, Item Title is " one-piece dress ", " T-shirt ", " overskirt ", its classification in taxonomy of goods table is " clothes ",
Classification more specifically is " summer clothing ".For another example the classification of " liquid detergent ", " liquid detergent ", " perfumed soap " in taxonomy of goods table
Belong to " daily chemical product ".
In step 204, the time buying interval of every class article of purchase is determined according to time buying information.
In one embodiment, terminal can carry out a classification based on the goods categories found to bought article, and
It is determined that per the time buying information of class article, so that time buying interval can be obtained according to time buying information.
For example, in the purchaser record of user, the article that user bought under daily chemical product classification includes：" wash clean
Essence ", " liquid detergent ", " perfumed soap ", " fabric softener ", " collar cleaner " this several articles, the time buying is respectively July 2, August
18 days, September 10 days, October 29 and December 1, then can calculate the time buying at intervals of 47 days, 23 days, 49 days with
And 33 days.
In the present embodiment, by above-mentioned steps 201-204, it can determine to buy article by searching taxonomy of goods table
Goods categories, and time buying interval is calculated based on the time buying of every class article, so as to determine next time in subsequent process
More accurate comprehensively foundation is provided with reference to the time buying, the accuracy rate for calculating next time with reference to the time buying is improved, so that
The article of recommendation more meets user's request.
Fig. 3 is the flow chart of the recommendation method of another article according to an exemplary embodiment, the present embodiment profit
The above method provided with the embodiment of the present disclosure, the time buying is referred to the next time that correspondence classification article is determined based on purchase data
Exemplified by it is illustrative, as shown in figure 3, comprising the following steps 301-303：
In step 301, according to multiple time buying interval calculation average time intervals of every class article for purchase.
In the embodiment depicted in figure 2, terminal has calculated the time buying interval of every class article for being bought, one
In embodiment, terminal can continue to calculate the average time interval at multiple time buying intervals.
Still illustrated with Fig. 2 institutes illustrated example, terminal asks for multiple time buying intervals：47 days, 23 days, 49 days and
The average value of 33 days, obtained average time interval for 33 days.
In step 302, the next time of correspondence classification article is calculated based on average time interval and last time time buying
With reference to the time buying.
In one embodiment, terminal can add the last time time buying average time interval to obtain next time with reference to purchase
Time.
For example, the last time time buying is December 1, average time interval is 33 days, then can calculate next ginseng
Examine the January 3 that the time buying is next year.
, being capable of the average time interval based on the ware bought by above-mentioned steps 301-302 in the present embodiment
And the last time time buying determines that next time refers to the time buying, so as to ensure the estimation of reference time buying next time more
Accurately.
Fig. 4 is the flow chart of the recommendation method of another article according to an exemplary embodiment, the present embodiment profit
The above method provided with the embodiment of the present disclosure, the time buying is referred to the next time that correspondence classification article is determined based on purchase data
Exemplified by it is illustrative, as shown in figure 4, comprising the following steps 401-402：
In step 401, it is determined that for the shortest time interval in multiple time buying intervals of every class article of purchase.
For example in the examples described above, determine the shortest time at intervals of 23 days.
In step 402, the next time of correspondence classification article is calculated based on shortest time interval and last time time buying
Time buying.
In the present embodiment, by above-mentioned steps 401-402, calculated based on shortest time interval and last time time buying
The next time of correspondence classification article refers to the time buying, so as to ensure that calculated next time can be early with reference to the time buying as far as possible
In user's actual next time buying, it is ensured that recommend relative article in time for user before the next time buying.
In one embodiment, when the purchase number of times that certain class buys article is one time, it is impossible to when obtaining the purchase of user
Between be spaced, then terminal can be obtained from server correspondingly buy article goods categories the average reference time buying.
The average reference time buying is drawn by server based on multiple users for the purchase data of category article.
Then, terminal is based on classification thing corresponding with the calculating of last time time buying of acquired average reference time buying
The next time buying of product.
Fig. 5 is the flow chart of the recommendation method of another article according to an exemplary embodiment, the present embodiment profit
The above method provided with the embodiment of the present disclosure, the time buying is referred to the next time that correspondence classification article is determined based on purchase data
Exemplified by it is illustrative, as shown in figure 5, comprising the following steps 501-503：
In step 501, when the article of purchase is food, the Item Title based on the food determines the shelf-life.
In one embodiment, the shelf-life table for the varieties of food items that can be stored with terminal, can also store daily chemical product
Shelf-life, but be due to daily chemical product depletion rate it is generally very fast, user can consume article within the shelf-life substantially,
Thus illustrated in the embodiment of the present disclosure mainly for food.
In one embodiment, terminal searches corresponding shelf-life table by the title of food, can obtain the guarantor of the food
The matter phase.
In step 502, the quantity purchase of the food is determined.
In step 503, based under shelf-life, last time time buying classification article corresponding with quantity purchase determination
The secondary reference time buying.
For example, for Mongolia Ox's flavor yoghourt, the shelf-life that terminal is found is 14 days, due to user's purchase date simultaneously
The food production date is not necessarily, but can be nearer apart from the date of manufacture, thus terminal can preset one and refer to number of days,
Such as 2 days, if so October 21 last time time buying, terminal was believed that the date of manufacture of Yoghourt is October 19,
It it is October 31 so as to estimate the cut-off shelf-life.
Another condition is quantity purchase, if user have purchased eight box Yoghourts, then it is considered that being accomplished by after 7 days
Yoghourt is bought again, if user have purchased 16 box Yoghourts, then exceeded 14 days shelf-lifves due to 16, it is believed that 13
It is accomplished by buying Yoghourt again after it.
Summary quantity purchase and shelf-life, terminal can accurately estimate user under the based food
The secondary reference time buying, so as to recommend relative article to user in time.
In the present embodiment, by above-mentioned steps 501-502, when terminal can be bought based on effective period of food quality, last time
Between and quantity purchase determine that next time refers to the time buying, to recommend ware in time for user so that user is in optimization
Consumer's Experience.
Fig. 6 is the flow chart of the recommendation method of another article according to an exemplary embodiment, the present embodiment profit
The above method provided with the embodiment of the present disclosure, using when the distance next reference time buying is the first preset time, for right
Answer classification article recommend article exemplified by it is illustrative, as shown in fig. 6, comprising the following steps 601-602：
In step 601, the goods attribute based on correspondence classification article determines relative article to be recommended.
In one embodiment, goods attribute can include：One or more in brand, the place of production, price, color, size.
Brand is, for example, domestic brand, Japan and Korea S's brand, German brand or American-European brand, generally for electronic digital product, household electrical appliances etc.
Article user has certain requirement to brand；The place of production can be such as Jiangxi production, Inner Mongol production, for some agricultural product
User may have certain requirement to the place of production；Price can be range format, such as mobile phone, can be divided into 1000 yuan with
Under, 1000-2000 members etc., to meet the user of different demands；Color is the personal like of user, is generally used for clothes, furniture
In ornaments；Size is also commonly used for clothing.
Terminal can draw the buying habit data of user, such as use for electronic products based on the goods attribute of bought article
American-European brand is always bought at family, and it is Europe that the corresponding brand of electronic product classification can be marked in the buying habit data of the user
It is beautiful；The size of such as clothes of user's purchase is S again, can also be S by the corresponding size-mark of clothing classification.
In step 602, when the distance next reference time buying is the first preset time, relative article to be recommended is pushed
Item Information.
In one embodiment, because logistics also needs to certain time, thus the first preset time can be set as 2-5 days,
That is 2-5 days before next time is with reference to the time buying, push the Item Information of relative article to be recommended.
In step 603, it is determined that the association of correspondence classification article uses article.
In one embodiment, the article being typically used together using article with bought article is associated, for example, right
In mobile phone, it is charger, earphone and other items that it, which is associated using article,；For pan, it is associated can be with using article
For round-bottomed frying pan, cutter, chopping board and other items.
In one embodiment, the association of all kinds of articles can be stored in associated article using article and used in table by terminal,
And determine that the association of bought article uses article using table by searching the associated article.
In step 604, in the second preset time before first preset time, push association and use article
Item Information.
In one embodiment, the second preset time can be the time between the time buying of article and the first preset time
Section.In this period, user just have purchased article, when using the article, it may be desirable to be associated some used
Article, thus recommend association to use article to user in this period, rather than recommend as in correlation technique similar commodity,
While user being avoided bothering, the individual demand of user is met, optimizes Consumer's Experience.
In the present embodiment, by above-mentioned steps 601-604, terminal close to next time with reference to the second of the time buying when presetting
Between, push similar article to be recommended to user；So as to avoid in correlation technique user purchase article after always to user
Recommend ware to bothering caused by user so that recommend more targeted and more meet user's request.
Fig. 7 is a kind of block diagram of the recommendation apparatus of article according to an exemplary embodiment, as shown in fig. 7, the dress
Putting to apply in the terminal, and the method for performing embodiment illustrated in fig. 1, and the recommendation apparatus of the article can include：Obtain
Modulus block 710, the first determining module 720 and recommending module 730.
Acquisition module 710, is configured as obtaining the purchase data of every class article of purchase based on purchaser record；
First determining module 720, the purchase data for being configured as obtaining based on acquisition module 710 determine correspondence classification article
Next time refer to the time buying；
Recommending module 730, it is first default with reference to the time buying to be configured as in the next time determined apart from determining module 720
During the time, relative article is recommended for the correspondence classification article.
In above-described embodiment, by determining that the next time of all kinds of articles refers to the time buying according to purchase data, so as to only exist
When the distance next reference time buying is the first preset time, the push of relative article is just carried out to user, is pushed away so as to enhance
The specific aim recommended, it is to avoid what excessive similar recommendation was caused to user bothers, optimizes Consumer's Experience.
Fig. 8 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 8,
On the basis of above-mentioned embodiment illustrated in fig. 7, in one embodiment, acquisition module 710 can include：First acquisition submodule 711,
Second acquisition submodule 712, the first determination sub-module 713 and the second determination sub-module 714.
First acquisition submodule 711, is configured as obtaining purchaser record；
Second acquisition submodule 712, the purchaser record for being configured as being obtained according to the first acquisition submodule 711 obtains purchase
Every class article Item Title and time buying information；
First determination sub-module 713, is configured as according to the Item Title determination that the second acquisition submodule 712 is obtained
The goods categories affiliated respectively per class article；
Second determination sub-module 714, the time buying information for being configured as being obtained according to the second acquisition submodule 712 is determined
The time buying interval of every class article of purchase.
In the embodiment of the present disclosure, terminal can determine to buy the goods categories of article by searching taxonomy of goods table, and
Time buying interval is calculated based on the time buying of every class article, so as to determine to refer to the time buying in subsequent process next time
More accurate comprehensively foundation is provided, the accuracy rate for calculating next time with reference to the time buying is improved, so that the article recommended is more
Meet user's request.
Fig. 9 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 9,
On the basis of above-mentioned embodiment illustrated in fig. 8, in one embodiment, the first determination sub-module 713 can include：Search submodule
715。
Submodule 715 is searched, is configured as searching pre-stored taxonomy of goods table based on Item Title, obtains and the thing
The name of an article claims corresponding goods categories.
Figure 10 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in Figure 10,
On the basis of above-mentioned embodiment illustrated in fig. 8, in one embodiment, the first determining module 720 can include：First calculates submodule
The calculating sub module 722 of block 721 and second.
First calculating sub module 721, is configured as multiple time buying intervals meter according to every class article for purchase
Calculate average time interval；
Second calculating sub module 722, be configured as the average time interval that is calculated based on the first calculating sub module 721 and
The next time that the last time time buying calculates correspondence classification article refers to the time buying.
In above-described embodiment, the average time interval and last time that terminal can be based on the ware bought are bought
Time determines that next time refers to the time buying, so as to ensure that next time is more accurate with reference to the estimation of time buying.
Figure 11 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 11,
On the basis of above-mentioned embodiment illustrated in fig. 8, in one embodiment, the first determining module 720 can include：3rd determines submodule
The calculating sub module 724 of block 723 and the 3rd.
3rd determination sub-module 723, is configured to determine that in multiple time buying intervals for every class article of purchase
Shortest time interval；
3rd calculating sub module 724, is configured as calculating based on the shortest time interval and last time time buying
The next time of correspondence classification article refers to the time buying.
Figure 12 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 12,
On the basis of above-mentioned embodiment illustrated in fig. 8, in one embodiment, the first determining module 720 can include：3rd obtains submodule
The calculating sub module 726 of block 725 and the 4th.
3rd acquisition submodule 725, is configured as, when the purchase number of times of every class article of purchase is one time, obtaining correspondence
The average reference time buying of the goods categories of article；
4th calculating sub module 726, is configured as the average reference time buying obtained based on the 3rd acquisition submodule 725
The next time buying of classification article corresponding with the calculating of last time time buying.
Figure 13 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 13,
On the basis of above-mentioned embodiment illustrated in fig. 7, in one embodiment, the first determining module 720 can include：4th determines submodule
Block 727, the 5th determination sub-module 728 and the 6th determination sub-module 729.
4th determination sub-module 727, is configured as when the article of purchase is food, the Item Title based on the food
Determine the shelf-life；
5th determination sub-module 728, is configured to determine that the quantity purchase of the food；
6th determination sub-module 729, is configured as shelf-life, the last time determined based on the 4th determination sub-module 727
The next time of classification article corresponding with the quantity purchase determination that the 5th determination sub-module 728 is determined time buying refers to the time buying.
In above-described embodiment, terminal can be determined based on effective period of food quality, last time time buying and quantity purchase
Next time refers to the time buying, to recommend ware in time for user so that user is in optimization Consumer's Experience.
Figure 14 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 14,
On the basis of above-mentioned embodiment illustrated in fig. 7, in one embodiment, recommending module 730 can include：7th determination sub-module
731 and first push submodule 732.
7th determination sub-module 731, is configured as the goods attribute based on the correspondence classification article and determines phase to be recommended
Close article；
First push submodule 732, be configured as apart from it is described next time with reference to the time buying be the first preset time when,
Push the Item Information of the relative article to be recommended.
Wherein, goods attribute includes one or more in brand, the place of production, price, color, size.
Figure 15 is the block diagram of the recommendation apparatus of another article according to an exemplary embodiment, as shown in figure 15,
On the basis of above-mentioned embodiment illustrated in fig. 7, in one embodiment, the device can also include：Second determining module 740 and push away
Send module 750.
Second determining module 740, is configured to determine that the association of the correspondence classification article uses article；
Pushing module 750, is configured as, when before first preset time being the second preset time, pushing the
Association determined by two determining modules 740 uses the Item Information of article.
In above-described embodiment, terminal can be pushed close to the second preset time of the next time with reference to the time buying to user
Similar article to be recommended；Recommend ware pair to user always after user's purchase article so as to avoid in correlation technique
Bothering caused by user so that recommend more targeted and more meet user's request.
Figure 16 is a kind of block diagram of recommendation apparatus suitable for article according to an exemplary embodiment.For example, dress
It can be mobile phone, computer, digital broadcast terminal, messaging devices, game console, tablet device, doctor to put 1600
Treat equipment, body-building equipment, the user equipment such as personal digital assistant.
Reference picture 16, device 1600 can include following one or more assemblies：Processing assembly 1602, memory 1604,
Power supply module 1606, multimedia groupware 1608, audio-frequency assembly 1610, the interface 1612 of input/output (I/O), sensor cluster
1614, and communication component 1616.
The integrated operation of the usual control device 1600 of processing assembly 1602, such as with display, call, data communication,
The camera operation operation associated with record operation.Treatment element 1602 can include one or more processors 1620 to perform
Instruction, to complete all or part of step of above-mentioned method.In addition, processing assembly 1602 can include one or more moulds
Block, is easy to the interaction between processing assembly 1602 and other assemblies.For example, processing component 1602 can include multi-media module,
To facilitate the interaction between multimedia groupware 1608 and processing assembly 1602.
Memory 1604 is configured as the various types of other data of storage to support the operation in equipment 1600.These data
Example includes the instruction of any application program or method for being used to operate on device 1600, contact data, telephone book data,
Message, image, video etc..Memory 1604 can by any classification volatibility or non-volatile memory device or they
Combination realize, such as static RAM (SRAM), Electrically Erasable Read Only Memory (EEPROM), it is erasable can
Program read-only memory (EPROM), programmable read only memory (PROM), read-only storage (ROM), magnetic memory, flash memory
Reservoir, disk or CD.
Electric power assembly 1606 provides electric power for the various assemblies of device 1600.Electric power assembly 1606 can include power management
System, one or more power supplys, and other components associated with generating, managing and distributing electric power for device 1600.
Multimedia groupware 1608 is included in the screen of one output interface of offer between described device 1600 and user.
In some embodiments, screen can include liquid crystal display (LCD) and touch panel (TP).If screen includes touch panel,
Screen may be implemented as touch-screen, to receive the input signal from user.Touch panel includes one or more touch and passed
Sensor is with the gesture on sensing touch, slip and touch panel.The touch sensor can not only sensing touch or slip be dynamic
The border of work, but also the detection duration related to the touch or slide and pressure.In certain embodiments, it is many
Media component 1608 includes a front camera and/or rear camera.When equipment 1600 is in operator scheme, mould is such as shot
When formula or video mode, front camera and/or rear camera can receive the multi-medium data of outside.Each preposition shooting
Head and rear camera can be a fixed optical lens systems or with focusing and optical zoom capabilities.
Audio-frequency assembly 1610 is configured as output and/or input audio signal.For example, audio-frequency assembly 1610 includes a wheat
Gram wind (MIC), when device 1600 is in operator scheme, when such as call model, logging mode and speech recognition mode, microphone quilt
It is configured to receive external audio signal.The audio signal received can be further stored in memory 1604 or via communication
Component 1616 is sent.In certain embodiments, audio-frequency assembly 1610 also includes a loudspeaker, for exports audio signal.
I/O interfaces 1612 are that interface, above-mentioned peripheral interface module are provided between processing assembly 1602 and peripheral interface module
Can be keyboard, click wheel, button etc..These buttons may include but be not limited to：Home button, volume button, start button and
Locking press button.
Sensor cluster 1614 includes one or more sensors, and the state for providing various aspects for device 1600 is commented
Estimate.For example, sensor cluster 1614 can detect opening/closed mode of equipment 1600, the relative positioning of component, such as institute
Display and keypad that component is device 1600 are stated, sensor cluster 1614 can be with detection means 1600 or device 1,600 1
The position of individual component changes, the existence or non-existence that user contacts with device 1600, the orientation of device 1600 or acceleration/deceleration and dress
Put 1600 temperature change.Sensor cluster 1614 can include proximity transducer, be configured in not any physics
The presence of object nearby is detected during contact.Sensor cluster 1614 can also include optical sensor, such as CMOS or ccd image sensing
Device, for being used in imaging applications.In certain embodiments, the sensor cluster 1614 can also include acceleration sensing
Device, gyro sensor, Magnetic Sensor, pressure sensor or temperature sensor.
Communication component 1616 is configured to facilitate the communication of wired or wireless way between device 1600 and other equipment.Dress
The wireless network based on communication standard, such as WiFi, 2G or 3G, or combinations thereof can be accessed by putting 1600.It is exemplary at one
In embodiment, communication component 1616 receives broadcast singal or broadcast correlation from external broadcasting management system via broadcast channel
Information.In one exemplary embodiment, the communication component 1616 also includes near-field communication (NFC) module, to promote short distance
Communication.For example, radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra wide band can be based in NFC module
(UWB) technology, bluetooth (BT) technology and other technologies are realized.
In the exemplary embodiment, device 1600 can be by one or more application specific integrated circuits (ASIC), numeral
Signal processor (DSP), digital signal processing appts (DSPD), PLD (PLD), field programmable gate array
(FPGA), controller, microcontroller, microprocessor or other electronic components are realized, for performing the above method.
In the exemplary embodiment, a kind of non-transitorycomputer readable storage medium including instructing, example are additionally provided
Such as include the memory 1604 of instruction, above-mentioned instruction can be performed to complete the above method by the processor 1620 of device 1600.Example
Such as, the non-transitorycomputer readable storage medium can be ROM, it is random access memory (RAM), CD-ROM, tape, soft
Disk and optical data storage devices etc..
Wherein, processor 1620 is configured as：
The purchase data of every class article of purchase are obtained based on purchaser record；
Determine that the next time of correspondence classification article refers to the time buying based on the purchase data；
When with reference to the time buying being the first preset time apart from the next time, phase is recommended for the correspondence classification article
Close article.
Those skilled in the art will readily occur to its of the disclosure after considering specification and putting into practice disclosure disclosed herein
Its embodiment.The application is intended to any modification, purposes or the adaptations of the disclosure, these modifications, purposes or
Person's adaptations follow the general principle of the disclosure and including the undocumented common knowledge in the art of the disclosure
Or conventional techniques.Description and embodiments are considered only as exemplary, and the true scope of the disclosure and spirit are by following
Claim is pointed out.
It should be appreciated that the precision architecture that the disclosure is not limited to be described above and is shown in the drawings, and
And various modifications and changes can be being carried out without departing from the scope.The scope of the present disclosure is only limited by appended claim.
Claims (21)
1. a kind of recommendation method of article, it is characterised in that methods described includes：
The purchase data of every class article of purchase are obtained based on purchaser record；
Determine that the next time of correspondence classification article refers to the time buying based on the purchase data；
When with reference to the time buying being the first preset time apart from the next time, correlative is recommended for the correspondence classification article
Product.
2. according to the method described in claim 1, it is characterised in that every class article that purchase is obtained based on purchaser record
Data are bought, including：
Obtain purchaser record；
The Item Title and time buying information of every class article of purchase are obtained according to purchaser record；
The goods categories affiliated respectively per class article are determined according to the Item Title；
The time buying interval of every class article of purchase is determined according to the time buying information.
3. method according to claim 2, it is characterised in that described that every class article is determined according to the Item Title
Goods categories belonging to respectively, including：
Pre-stored taxonomy of goods table is searched based on the Item Title, goods categories corresponding with the Item Title are obtained.
4. method according to claim 2, it is characterised in that described to determine correspondence classification article based on the purchase data
Next time refer to the time buying, including：
According to multiple time buying interval calculation average time intervals of every class article for purchase；
The next time for calculating correspondence classification article based on the average time interval and last time time buying refers to the time buying.
5. method according to claim 2, it is characterised in that described to determine correspondence classification article based on the purchase data
Next time refer to the time buying, including：
It is determined that for the shortest time interval in multiple time buying intervals of every class article of purchase；
The next time for calculating correspondence classification article based on the shortest time interval and last time time buying refers to the time buying.
6. method according to claim 2, it is characterised in that described to determine correspondence classification article based on the purchase data
Next time refer to the time buying, including：
When the purchase number of times of every class article of purchase is one time, during the average reference purchase for the goods categories for obtaining correspondence article
Between；
The next time buying based on average reference time buying classification article corresponding with the calculating of last time time buying.
7. according to the method described in claim 1, it is characterised in that described to determine correspondence classification article based on the purchase data
Next time refer to the time buying, including：
When the article of purchase is food, the Item Title based on the food determines the shelf-life；
Determine the quantity purchase of the food；
During based on the shelf-life, reference purchase next time of last time time buying classification article corresponding with quantity purchase determination
Between.
8. according to the method described in claim 1, it is characterised in that described is being first apart from reference time buying next time
During preset time, relative article is recommended for the correspondence classification article, including：
Goods attribute based on the correspondence classification article determines relative article to be recommended；
When with reference to the time buying being the first preset time apart from the next time, the article letter of the relative article to be recommended is pushed
Breath.
9. method according to claim 8, it is characterised in that the goods attribute include brand, the place of production, price, color,
One or more in size.
10. according to the method described in claim 1, it is characterised in that described to determine correspondence classification thing based on the purchase data
The next time of product, methods described also included with reference to after the time buying：
Determine that the association of the correspondence classification article uses article；
In the second preset time before first preset time, Item Information of the association using article is pushed.
11. a kind of recommendation apparatus of article, it is characterised in that described device includes：
Acquisition module, is configured as obtaining the purchase data of every class article of purchase based on purchaser record；
First determining module, is configured as determining that the next time of correspondence classification article refers to the time buying based on the purchase data；
Recommending module, be configured as apart from it is described next time with reference to the time buying be the first preset time when, for the correspondence
Classification article recommends relative article.
12. device according to claim 11, it is characterised in that the acquisition module includes：
First acquisition submodule, is configured as obtaining purchaser record；
Second acquisition submodule, is configured as obtaining Item Title and the time buying for the every class article bought according to purchaser record
Information；
First determination sub-module, is configured as determining the article class affiliated respectively per class article according to the Item Title
Not；
Second determination sub-module, was configured as between the time buying according to every class article of time buying information determination purchase
Every.
13. device according to claim 12, it is characterised in that first determination sub-module includes：
Submodule is searched, is configured as searching pre-stored taxonomy of goods table based on the Item Title, obtains and the article
The corresponding goods categories of title.
14. device according to claim 12, it is characterised in that first determining module includes：
First calculating sub module, is configured as multiple time buying interval calculation mean times according to every class article for purchase
Between be spaced；
Second calculating sub module, is configured as calculating correspondence classification based on the average time interval and last time time buying
The next time of article refers to the time buying.
15. device according to claim 12, it is characterised in that first determining module includes：
3rd determination sub-module, be configured to determine that for purchase every class article multiple time buying intervals in most in short-term
Between be spaced；
3rd calculating sub module, is configured as calculating correspondence classification based on the shortest time interval and last time time buying
The next time of article refers to the time buying.
16. device according to claim 12, it is characterised in that first determining module includes：
3rd acquisition submodule, is configured as, when the purchase number of times of every class article of purchase is one time, obtaining correspondence article
The average reference time buying of goods categories；
4th calculating sub module, is configured as corresponding with the calculating of last time time buying based on the average reference time buying
The next time buying of classification article.
17. device according to claim 11, it is characterised in that first determining module includes：
4th determination sub-module, is configured as when the article of purchase is food, the Item Title based on the food determines to protect
The matter phase；
5th determination sub-module, is configured to determine that the quantity purchase of the food；
6th determination sub-module, is configured as corresponding with quantity purchase determination based on the shelf-life, last time time buying
The next time of classification article refers to the time buying.
18. device according to claim 11, it is characterised in that the recommending module includes：
7th determination sub-module, is configured as the goods attribute based on the correspondence classification article and determines relative article to be recommended；
First push submodule, be configured as apart from it is described next time with reference to the time buying be the first preset time when, push institute
State the Item Information of relative article to be recommended.
19. device according to claim 18, it is characterised in that the goods attribute includes brand, the place of production, price, face
One or more in color, size.
20. device according to claim 11, it is characterised in that described device also includes：
Second determining module, is configured to determine that the association of the correspondence classification article uses article；
Pushing module, is configured as, when before first preset time being the second preset time, pushing the association
Use the Item Information of article.
21. a kind of terminal, it is characterised in that including：
Processor；
Memory for storing processor-executable instruction；
Wherein, the processor is configured as：
The purchase data of every class article of purchase are obtained based on purchaser record；
Determine that the next time of correspondence classification article refers to the time buying based on the purchase data；
When with reference to the time buying being the first preset time apart from the next time, correlative is recommended for the correspondence classification article
Product.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201710165879.9A CN106981011A (en) | 2017-03-20 | 2017-03-20 | Recommendation method, device and the terminal of article |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710165879.9A CN106981011A (en) | 2017-03-20 | 2017-03-20 | Recommendation method, device and the terminal of article |
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| CN201710165879.9A Pending CN106981011A (en) | 2017-03-20 | 2017-03-20 | Recommendation method, device and the terminal of article |
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| CN107508898A (en) * | 2017-08-31 | 2017-12-22 | 努比亚技术有限公司 | A kind of information-pushing method and device, computer-readable recording medium |
| CN107734003A (en) * | 2017-09-26 | 2018-02-23 | 努比亚技术有限公司 | Shopping reminding method, terminal, server and computer-readable recording medium |
| CN107767217A (en) * | 2017-10-19 | 2018-03-06 | 康佳集团股份有限公司 | Shopping recommendation method, mobile terminal and storage medium |
| CN108122156A (en) * | 2017-10-31 | 2018-06-05 | 深圳市博鑫创科科技有限公司 | A kind of shopping guide method and balance shopping cart based on balance shopping cart |
| CN108230140A (en) * | 2017-12-29 | 2018-06-29 | 阿里巴巴集团控股有限公司 | The method and apparatus of pushed information, the method and apparatus for determining input default value |
| CN111027993A (en) * | 2018-10-09 | 2020-04-17 | 中国移动通信集团山西有限公司 | Mobile phone recommendation method, device, equipment and storage medium |
| CN115510329A (en) * | 2022-10-11 | 2022-12-23 | 江苏云机汇软件科技有限公司 | Brand marketing software management method based on cloud computing big data |
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| CN107369047A (en) * | 2017-07-26 | 2017-11-21 | 莆田市烛火信息技术有限公司 | A kind of advertisement placement method, apparatus and system |
| CN107508898A (en) * | 2017-08-31 | 2017-12-22 | 努比亚技术有限公司 | A kind of information-pushing method and device, computer-readable recording medium |
| CN107734003A (en) * | 2017-09-26 | 2018-02-23 | 努比亚技术有限公司 | Shopping reminding method, terminal, server and computer-readable recording medium |
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| CN108230140A (en) * | 2017-12-29 | 2018-06-29 | 阿里巴巴集团控股有限公司 | The method and apparatus of pushed information, the method and apparatus for determining input default value |
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| CN111027993A (en) * | 2018-10-09 | 2020-04-17 | 中国移动通信集团山西有限公司 | Mobile phone recommendation method, device, equipment and storage medium |
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| CN115510329A (en) * | 2022-10-11 | 2022-12-23 | 江苏云机汇软件科技有限公司 | Brand marketing software management method based on cloud computing big data |
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