CN106530017A - Online store discount coupon automatic acquisition and shopping combination recommendation method - Google Patents
Online store discount coupon automatic acquisition and shopping combination recommendation method Download PDFInfo
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- CN106530017A CN106530017A CN201611176137.8A CN201611176137A CN106530017A CN 106530017 A CN106530017 A CN 106530017A CN 201611176137 A CN201611176137 A CN 201611176137A CN 106530017 A CN106530017 A CN 106530017A
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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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0219—Discounts or incentives, e.g. coupons or rebates based on funds or budget
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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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
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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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0222—During e-commerce, i.e. online transactions
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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]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
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Abstract
The invention discloses an online store discount coupon automatic acquisition and shopping combination recommendation method. The method comprises the steps of 1) performing a search according to target commodity item information input by a user, and selecting a plurality of web pages from search results to establish a web page set WPt; 2) acquiring a usage mode of a discount coupon D and a web page set Wc corresponding to an applicable candidate commodity list; 3) calculating the similarity between a web page w1 in which each commodity i is located and each web page w2 in the web page set WPt of target commodities pt, and taking average similarity as the similarity between the commodity i and each target commodity; and 4) selecting a plurality of commodity pairs with the highest similarity, and adding n candidate commodities to M according to a commodity quantity of the target commodities for each selected commodity pair; judging whether the existing candidate commodity list meets an application condition of the discount coupon D or not; and if the application condition is not met, deleting the commodity pair in a candidate commodity pair set, and if the application is met, outputting the shopping combination M of the discount coupon D.
Description
Technical field
The invention belongs to technical field of the computer network, and in particular to a kind of on-line shop's reward voucher is obtained and shopping portfolio automatically
The method of recommendation.
Background technology
The each big shopping website for occurring at present, is conventional marketing tool using reward voucher.Reward voucher being capable of attracting holding
Client, improves Sales Volume of Commodity, widens one's influence.
User currently with the mode of these reward vouchers is：Generally browse webpage or reward voucher downloaded by mobile phone A pp,
Search the suitable commodity of reward voucher.The rule that uses of reward voucher considers to give and roll over according to the total number of packages of purchase commodity or total amount
Button.
Reward voucher is using generally two class conditions of satisfaction：According to the occupation mode of commodity number, such as A commodity, B business is reduced
Product price；According to the occupation mode of shopping total amount, such as completely A is first, reduces B first.In order to meet the use condition of reward voucher, user
Have to be browsed in shiploads of merchandise, select collocation commodity.This process wastes time and energy, and in substantial amounts of favor information
In be easily lost oneself real commodity interested.
The content of the invention
For technical problem present in prior art, it is an object of the invention to provide a kind of on-line shop's reward voucher is obtained automatically
Take the method recommended with shopping portfolio.User only needs to describe its end article information, and system collects reward voucher, Jin Erfa automatically
The commodity that existing reward voucher can be bought, according to goods matching degree and discount degree, provide shopping portfolio suggestion.
The technical scheme is that：
A kind of on-line shop's reward voucher obtains the method recommended with shopping portfolio automatically, and its step is：
1) scanned for according to each end article item information of user input, some webpages are chosen from Search Results and is built
Vertical collections of web pages WPt；Wherein, each end article item information Lt includes Brand b, trade name n and commodity amount q；
2) reward voucher is obtained from coupon webpage, for a reward voucher D, obtain the use pattern of reward voucher D；
3) search for corresponding collections of web pages W of candidate's items list that reward voucher D can be appliedc；
4) extract WcThe hierarchical relationship of the title of each commodity, price, brand and commodity and description text in collections of web pages
This；
5) 4) calculation procedure is extracted each commodity i places webpages w1 collections of web pages W respectively with each end article ptPtIn
The similarity of each webpage w2, using the meansigma methodss of the similarity for obtaining as the commodity i and end article pt similarity；
6) choose similarity highest before k commodity to as candidate's commodity to set C={ (pt, pc, sim) }；Wherein,
Pt is end article, and pc is candidate's commodity, and sim is similarity；
7) candidate's commodity are randomly selected to the commodity in set C to (pt, pc, sim)；According to the commodity of end article pt
Quantity n, n candidate commodity pc is added in candidate list M；According to the use pattern of reward voucher D, current candidate's commodity are judged
Whether list meets the application conditions of reward voucher D；If be unsatisfactory for, (pt, *, sim) is deleted in candidate's commodity are to set
Commodity pair, wherein * represents any candidate's commodity；Repeat step is 6), 7)；If it is satisfied, then a shopping of output reward voucher D
Combination M.
Further, the above-mentioned steps that rerun 1)～7) k1 time, obtain for k1 of a coupon D different shopping
Combination；Then coupons rule, the margin of preference of each shopping portfolio obtained by calculating are utilized；Then output includes target business
Product and the maximum grouping of commodities of the margin of preference.
Further, the margin of preference be (p1-p2)/p1, wherein, in candidate list M commodity price sum be p1, root
It is p2 according to the price used after regular reducing of reward voucher D.
Further, commodity metadata is extracted from webpage w1 and webpage w2, then according to the commodity metadata meter for extracting
Calculate the similarity of commodity.
Further, the commodity metadata is included in Brand, trade name, commodity hierarchical relationship and commodity webpage
The similarity of appearance.
Further, the similarity is sim=c0.sim (w1.b, w2.b)+c1.sim (w1.n, w2.n)+c2.sim
(w1.h, w2.h)+c3.sim (w1.t, w2.t), wherein, c0+c1+c2+c3=1, sim (w1.b, w2.b) represent that brand is similar
Degree, sim (w1.n, w2.n) represent trade name similarity, and sim (w1.h, w2.h) is commodity HD, sim (w1.t,
W2.t) it is descriptive labelling text similarity.
Further, step 2) in, coupon information D is obtained by Configuration network reptile periodic scanning；Wherein, with confidence
Breath includes startup that the entry address of shopping website reward voucher, user start in the user name of shopping website, password, reptile frequently
Rate, the rule of web page crawl and the extracting rule of web data.
Compared with prior art, the positive effect of the present invention is：
Existing commending system, is to browse in record from user, finds user interest.The present invention is mainly from reward voucher angle
Degree, emphasizes to reduce shopping cost using reward voucher.Meanwhile, reward voucher has use condition mostly, and this patent lays particular emphasis on goods of joint,
Meet the requirement of reward voucher, export commodity purchasing combined recommendation.
Existing discounting system, primarily focuses on the discount of single commodity, do not account for website reward voucher using rule and
The similarity of user intention, also actively cannot push shopping suggestion to user.
Existing information extracting system, can extract the metadata of commodity, including trade name, brand, price etc., but
It is not related to information retrieval subsequent analysis and excavates application.
The present invention can reduce burden for users：User only need expression need what end article, configuration site shopping and
The entrance that reward voucher correlation mushroom is pressed, and after web page crawl and extracting rule, reward voucher is obtained automatically, extract commodity metadata letter
Breath, and calculate similarity between commodity and commodity.
The present invention recommends accuracy high：During commodity similarity-rough set, it is considered to Brand, title, hierarchical relationship,
Commodity webpage similarity, meets the shopping target of user as far as possible；In the sequence of combination shopping, Recommendations are considered similar
Degree and discount amplitude.
Description of the drawings
Fig. 1 illustrates the flow chart of data processing figure in the present invention.
Specific embodiment
The present invention is explained in further detail with specific embodiment below in conjunction with the accompanying drawings.
The flow chart of data processing of the present invention is as shown in figure 1, its step includes：
1. user expresses purchase information, including end article item information list Lt={ p in systemst=(b, n, q) }, its
In, b represents Brand, and n represents trade name, and q represents commodity amount；
System is according to each commodity p in object listing Ltt=(b, n, q), using Brand b and trade name n, profit
The query interface provided with website, adds the mode Automatic Combined keyword of n using b plus space, obtains system using Meta Search Engine anti-
Top-k (such as k=3) sample collections of web pages W of feedbackPt.And realize descriptive labelling text message, commodity layer in end article webpage
Secondary extraction；Collections of web pages WPtThe information of pt commodity in current shopping website can be more galore described in.Meta Search Engine passes through
Support the reptile analog subscriber with dynamic script click on realizing (Xia Bing, Gao Jun, Wang Tengjiao, Yang Dongqing. it is a kind of efficient dynamic
The effective page acquisition methods of state script website. Journal of Software, 2009,20 (zk):176-183), the extraction of sample web page is by matching somebody with somebody
Put extraction path of the target in html web page to realize.
2. system obtains coupon information D by the web crawlers periodic scanning under system configuration information guiding.System is matched somebody with somebody
Confidence breath include the entry address of shopping website reward voucher, user the user name of shopping website, password, reptile startup startup
Rule that frequency, network are crawled, the extracting rule of web data.
A) when a reward voucher D is obtained, verify if necessary by picture, then added using existing picture recognition technology
To process；
3. application fetches rule, extracts reward voucher use pattern.The use of reward voucher is divided into two big class, according to commodity purchasing
Number is reduced, and is reduced according to commodity purchasing price total amount.
4. the link that reward voucher can apply items list is described in configuring.Point is simulated in reward voucher webpage using reptile
Hit, obtain according to depth-first or breadth-first and find the candidate commodity set W that a reward voucher D can be appliedc。
5., using the extraction procedure of webpage, W is analyzedcThe title of each commodity in collections of web pages, price, brand, commodity
Hierarchical relationship, and description text.These information extracted all are the features as commodity.Wherein, the hierarchical relationship of commodity is
Refer to classification situation of the commodity in whole website, such as books-novel-four great classical masterpieces-Journey to the West.It is to support to introduce hierarchical relationship
In the case where user is not input into Brand, similarity is provided by hierarchical relationship.
6. W is calculatedcIn one end article collections of web pages W of user for obtaining of corresponding each commodity and second stepPtPhase
Like degree.Similarity is defined as each WcEach commodity webpage and WPtThe average similarity of webpage.Wherein, two commodity similarities are sentenced
Surely consider Brand, trade name, commodity hierarchical relationship (classification information), and commodity web page contents similarity.If used
Brand is not specified at family, then consider the similarity of latter three.Brand and title similarity are completed using similarity of character string.
Web page contents refer to that commodity remove Ad navigation information, mainly include the distinctive text description information of commodity.
Two webpages w1 and w2 are given, after commodity metadata is extracted, the comprehensive similarity for defining w1 and w2 is
C0.sim (w1.b, w2.b)+c1.sim (w1.n, w2.n)+c2.sim (w1.h, w2.h)+c3.sim (w1.t, w2.t), wherein,
C0+c1+c2+c3=1, represents that coefficient sum is 1, is arranged by user；Sim (w1.b, w2.b) represents brand similarity, according to
Character string directly determines whether equal；Sim (w1.n, w2.n) represents trade name similarity, extracts the feature of character string, each
Character string is equivalent to a characteristic set, judges commodity similarity according to the similarity of characteristic set；Sim (w1.h, w2.h) is
HD, related according to two commodity equivalent definition hierarchically, equal more high-level, two commodity get over phase
Seemingly；Sim (w1.t, w2.t) is to define similar on descriptive labelling text, and method is equal to the calculating of sim (w1.n, w2.n).It is similar
The result of degree Sim functions feedback is between 0 to 1.
7. using the 6th step calculate all end articles of user and corresponding to current coupon D between all commodity two-by-two
Similarity,
A) candidate list M is set to sky；
B) commodity pair are randomly choosed from the individual commodity centering of comprehensive similarity highest top-k (k could be arranged to 5)
(pt,pc,sim).Pt is the end article of user, and pc is the candidate's commodity in the corresponding items list of coupon, and sim is comprehensive
Close similarity.It is for the multiformity for improving follow-up Shopping List arbitrarily to select one in Top-k；
C) consider the end article pt commodity amount n of user's information input in the first step, n pc is added to into candidate
In list M；
D) type according to reward voucher, judges whether M Shopping Lists currently meet number or the amount of money is limited；
// it is not that reward voucher can buy commodity, pc is in the commodity that coupon can be bought, previously by climbing
Acquisition is taken, this place is to say that can M lists meet the use requirement of coupon, be exactly that full several buttons are several, or it is full how many
Subtract how many
If e) d) step judges to be unsatisfactory for, (pt, *, sim) is deleted in candidate's commodity are to set, wherein pt is c
The end article pt that step is chosen, expression need not consider the end article pt of user in subsequent candidate commodity selection；Repeat b) step.
The purpose of deletion is in order to avoid choosing commodity to subsequently selected impact
If f) d) step judges to meet, a shopping portfolio M for coupon D is exported；
G) coupons rule is utilized, the margin of preference of the shopping portfolio obtained by calculating.Valency is extracted according to commodity in M lists
Lattice, in calculating M lists, price sum is p1, and the price after being reduced using rule according to reward voucher is p2.The margin of preference is (p1-
p2)/p1。
8. the 8th step that reruns k1 time, obtains for k1 of a coupon D different shopping portfolios.Consider the 8th step
Random factor, same coupon can produce multiple different shopping portfolios, increase the selectance of user.
9. it is above to process a coupon, it is assumed that system obtains multiple coupons, then each coupon of circular treatment, obtains
To the set of the corresponding shopping portfolio of all coupons.
// it is to process a coupon from the 4th step to the 9th step, each coupon individually exports multiple Shopping Lists, this
Step is to process multiple coupons, one circulation that has been exactly outer layer bag.
10. the appropriate degree of each shopping portfolio is calculated, and appropriate degree is defined as user intention shopping items and candidate's commodity
The comprehensive similarity sum * margin of preference.Obtain similar with user's request, preferential maximum grouping of commodities.
The appropriate degree sequence of 11. shopping portfolios calculated according to 10 steps, is that user recommends suitably shopping group from large to small
Close.
Embodiment
1) user wishes the acquisition of information for buying commodity
User is input into trade name trademark quantity etc., system in combination trade name and brand in systems, is supporting dynamic
Under the support of script reptile, simulation input inquiry.For example, entitled Journey to the West, brand are People's Literature Publishing House in on-line shop
Inquiry is input into Journey to the West People's Literature Publishing House in entrance by the reptile of the support dynamic script for configuring.Obtain Query Result,
Select the Query Result of top 3.The information in each result is extracted, as the feature of ownership goal commodity, including：Metadata is special
Levy, the text feature of end article.
2) through the URL entrances of configuration, enter into the reward voucher page.
3) using the reptile with dynamic script parsing, realize the acquisition of reward voucher.
4) under the guiding of configuration file, the reptile analog mouse with dynamic script, click are used immediately, are found preferential
The commodity that certificate can be used.
5) into inside candidate's commodity, extract correlated characteristic, including trade name, Brand, price, hierarchical relationship,.
The Similarity Measure of candidate's commodity and each end article top-3 webpages is realized, average similarity is candidate's commodity and target business
The similarity of product.
6) assume that user there are multiple end article wishes, one coupon of systematic collection, and this coupon is corresponding
Candidate's commodity web page listings.
7) similarity of each end article and candidate's commodity is calculated, and the commodity with similarity is formed to set.
8) commodity of similarity maximum Top-k are selected to (end article, candidate's commodity) optional commodity pair.
9) consider the quantity of the original end article of user, candidate's commodity are increased in candidate's items list.
10) judge whether coupon meets use condition, if be unsatisfactory for, in the commodity with similarity degree in set
Delete the commodity pair related to end article just now.
11) if it is satisfied, then output candidate's items list, calculates appropriate degree；In view of the random factor of Top-k, each
The multiple candidate's items lists of the actual correspondence of coupon.
12) for other coupons are operated successively, multiple candidate's items lists are each produced, are sorted according to appropriate degree,
It is supplied to end user.
Claims (7)
1. a kind of on-line shop's reward voucher obtains the method recommended with shopping portfolio automatically, and its step is：
1) scanned for according to each end article item information of user input, some webpages are chosen from Search Results and sets up one
Collections of web pages WPt；Wherein, each end article item information Lt includes Brand b, trade name n and commodity amount q；
2) reward voucher is obtained from coupon webpage, for a reward voucher D, obtain the use pattern of reward voucher D；
3) search for corresponding collections of web pages W of candidate's items list that reward voucher D can be appliedc；
4) extract WcThe hierarchical relationship of the title of each commodity, price, brand and commodity and description text in collections of web pages；
5) 4) calculation procedure is extracted each commodity i places webpages w1 collections of web pages W respectively with each end article ptPtIn it is each
The similarity of webpage w2, using the meansigma methodss of the similarity for obtaining as the commodity i and end article pt similarity；
6) choose similarity highest before k commodity to as candidate's commodity to set C={ (pt, pc, sim) }；Wherein, pt is
End article, pc are candidate's commodity, and sim is similarity；
7) candidate's commodity are randomly selected to the commodity in set C to (pt, pc, sim)；According to the commodity amount of end article pt
N, n candidate commodity pc is added in candidate list M；According to the use pattern of reward voucher D, current candidate's items list is judged
Whether the application conditions of reward voucher D are met；The business that (pt, *, sim) if be unsatisfactory for, is deleted in candidate's commodity are to set
Product pair, wherein * represent any candidate's commodity；Repeat step is 6), 7)；If it is satisfied, then a shopping portfolio of output reward voucher D
M。
2. the method for claim 1, it is characterised in that the above-mentioned steps that rerun 1)～7) k1 time, obtain for one
The different shopping portfolios of k1 of individual coupon D；Then coupons rule is utilized, the preferential of resulting each shopping portfolio is calculated
Amplitude；Then the grouping of commodities that output is comprising end article and the margin of preference is maximum.
3. method as claimed in claim 2, it is characterised in that the margin of preference is (p1-p2)/p1, wherein, candidate list
In M, commodity price sum is p1, and the price used after regular reducing according to reward voucher D is p2.
4. the method as described in claim 1 or 2 or 3, it is characterised in that commodity unit number is extracted from webpage w1 and webpage w2
According to, then according to extract commodity metadata calculate commodity similarity.
5. method as claimed in claim 4, it is characterised in that the commodity metadata includes Brand, trade name, business
The similarity of product hierarchical relationship and commodity web page contents.
6. method as claimed in claim 5, it is characterised in that the similarity be sim=c0.sim (w1.b, w2.b)+
C1.sim (w1.n, w2.n)+c2.sim (w1.h, w2.h)+c3.sim (w1.t, w2.t), wherein, c0+c1+c2+c3=1, sim
(w1.b, w2.b) represents brand similarity, and sim (w1.n, w2.n) represents trade name similarity, and sim (w1.h, w2.h) is business
Product HD, sim (w1.t, w2.t) are descriptive labelling text similarities.
7. the method as described in claim 1 or 2 or 3, it is characterised in that step 2) in, periodically swept by Configuration network reptile
Retouch acquisition coupon information D；Wherein, configuration information includes the entry address of shopping website reward voucher, user in shopping website
Initiation culture, the rule of web page crawl and the extracting rule of web data that user name, password, reptile start.
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