CN106910104A - Application of the negative sequence pattern based on individual event missing in commercial product recommending - Google Patents

Application of the negative sequence pattern based on individual event missing in commercial product recommending Download PDF

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CN106910104A
CN106910104A CN201710018624.XA CN201710018624A CN106910104A CN 106910104 A CN106910104 A CN 106910104A CN 201710018624 A CN201710018624 A CN 201710018624A CN 106910104 A CN106910104 A CN 106910104A
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sequence
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董祥军
邱萍
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Qilu University of Technology
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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
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • 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
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • 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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Abstract

The present invention relates to be based on the single application for the negative sequence pattern of item not occurring in commercial product recommending, the present invention is applied during customers buying behavior analysis is carried out, the true buying behavior of customer can be fully represented, so as to find that customer really frequently buys or do not buy the behavior of commodity.So customer is when commodity are bought, can recommend some other client's purchase frequency than the product of larger related purchase to it using the present invention, so as to increase the Transaction apparatus meeting of customer, website browsing person is changed into buyer, improve cross-selling ability, the loyalty of client is improved, so as to improve the economic benefit of website.

Description

Application of the negative sequence pattern based on individual event missing in commercial product recommending
Technical field
The present invention relates to be based on application of the negative sequence pattern of individual event missing in commercial product recommending, belong to negative sequence pattern Applied technical field.
Background technology
With the fast development of internet, shopping at network is from large size city to small and medium cities again to rural area gradually generation Having replaced traditional shopping form turns into the dominant form of shopping.Shopping at network is built upon on the basis of mutual trusting and mutual benefiting, gram The obstacle of region and time is taken, for consumer provides abundant merchandise news.Consumer can be multiple by browsing Duo Jia retail shops Website, it might even be possible to which sea washes in a pan to obtain more price competitiveness, personalized, the commodity of customization.So brought to consumer Bigger shopping, more commodity selections occupy big advantage in shopping.Therefore, shopping at network in recent years is in quick-fried Hairdo increases, and is all increased with geometry several levels every year, at the same time many large-scale e-commerce websites, such as Amazon, Arriba Taobao and day cat store, Jingdone district under bar etc. all have accumulated substantial amounts of customer transactional data.How these data are made full use of Effective analysis, tissue are carried out to customers buying behavior to utilize, and how to recognize client's hobby as much as possible and value orientation, It is that client provides personalized service to optimize website design, as the problem that e-commerce development compels highly necessary to solve.
Compared with traditional management style, the information contents of products of shopping at network is big, and quantity, species are enriched, and range of choice is wide. Under traditional shopping environment, the source that consumer obtains merchandise news accumulates mainly by life, and collection process is more long, the letter for obtaining Breath is more unilateral.Under shopping at network environment, consumer can just be collected with time of concentration, search the substantial amounts of letter for having a underlying commodity Breath.Online type of merchandize is enriched, and some commodity consumptions person wants purchase and traditional retail shop is not easily found, it is possible to by network Purchase is easily inquired about in shop, the shortage supplemented with some products of traditional store.But current e-commerce merchants are generally not Can intuitively go to understand client, the related data of acquisition are limited (log-on message of such as user, purchaser record etc.).By right Substantial amounts of client's purchaser record is analyzed and excavates, and finds the frequent access sequence pattern of client, belongs to for different clients Property and shopping online step, using different commercial product recommending forms, the in good time commodity appropriate to lead referral, and optimize electronics The putting position of business web site commodity, can effectively increase the Transaction apparatus meeting of client, and website browsing person is changed into buyer, Cross-selling ability is improved, the loyalty of client, and the service quality and economic benefit for improving shopping website is improved.
Sequence pattern can not only analyze the relationship schedule between client trading commodity, can also analyze the pass between transaction It is rule.So that seller is not only able to be had to client according to the situation of client trading commodity targetedly recommending, Er Qieneng Later buying and selling of commodities situation is enough predicted according to current buying and selling of commodities situation such that it is able to preferably arrange the pendulum of commodity Put.Whether its main purpose is to study the precedence relationship of commodity purchasing, finds out rule therein, i.e., do not only need to know commodity It is purchased, and it needs to be determined that the sequencing of the commodity and other commodity purchasings, for example, a typical case of on-line purchase DVD Order be purchase " Star War ", it is very possible afterwards to continue to buy " Caesarean counterattacks ", then be that " Jie Da warrior returns for purchase Come ".Therefore sequence pattern it can be found that in database certain a period of time in Frequent episodes, i.e., which business within this time period The comparing that product can be bought by client is more, more or few standard is determined by minimum support.Each sequence is according to transaction One of Time alignment set, the sequence that minimum support meets different frequent degrees to excavate can be set.But in application Series pattern analysis customers buying behavior, when solving the problems, such as individual commodity recommendation, they only account for the event for having occurred, Referred to as positive sequence pattern (Positive Sequential Pattern, PSP) is excavated.
But with going deep into for research, researcher has found to imply substantial amounts of useful information in not generation event, and this A little information are at all unavailable in simple positive sequence mode excavation.Then researcher will not occur again event consider exist It is interior, it is proposed that negative sequence mode excavation.Negative sequence pattern (Negative Sequential Pattern, NSP) is excavated and not only examined The event having occurred and that is considered, has been also directed to event, what it can analyze deeper into ground and understand in data potential has contained Justice.For example, a represents bread, b represents coffee, and c represents tea,Represent client's purchase sequence pattern, the mode declaration pattern specification Within certain a period of time, the client after it have purchased commodity a, in the case of without purchase commodity b, have purchased commodity c without It is other commodity.Nowadays the value of negative sequence pattern increasingly approved by people, in intelligent checking system and much information Application field plays irreplaceable effect, especially the buying behavior analysis of client is applied on commercial product recommending, even more To commodity Successful Transaction and dealing money serves huge impetus.
Website user's purchase order data in e-commerce platform are the data source excavated.With 5 clients in 2 months Transaction as a example by, if table 1 is the transaction database that is sorted by keyword by Customer ID and exchange hour.One Transaction Information Storehouse a, affairs represent a transaction, and an individual event represents the commodity of transaction, and the letter record in individual event attribute is commodity ID。
Table 1
Data prediction is carried out, the transaction database of table 1 is organized into the sequence library of table 2.
Table 2
Customer ID Client's purchase sequence
1 <{c}{i}>
2 <{a,b}{c}{a,d,f,g}>
3 <{c,e,g,h}>
4 <{c}{c,d,g,h}{i}>
5 <{i}>
One client's all of transaction record within certain time period constitutes an orderly sequence, and sequence is used<>Represent. In the sequence, item/item collection is sequential, and each Xiang Dou represents a kind of commodity of transaction, and element refers to then the client at certain One all commodity of disposable purchase of specific time point, represents, the client may purchase in the different time periods with { } or () Same part product is bought, i.e., one item may occur in a different elements for sequence.As ID is 2 client's purchase sequence in table 2 It is classified as<{a,b}{c}{a,d,f,g}>, the client respectively for the first time and third time do shopping when have purchased commodity a, wherein a, B }, { c }, { a, d, f, g } these three Item Sets can be described as the element of sequence, and a, b, c, d, f, g are then referred to as item, if an element Middle only one of which, then bracket can be omitted, and such as the element { c } in the sequence can directly write c.
However, existing negative sequence pattern mining algorithm is fewer, for example, NSPM, PNSP, Neg-GSP, e-NSP etc.. Most of constraints in these algorithms is proposed to realizing negative sequence mode excavation and improve efficiency of algorithm.It is true On, these constraintss are relatively stricter so that many significant sequence patterns cannot be mined.They are in actual applications It is not necessary to.For example, negative element is constrained, the minimal negative unit that it requires to bear candidate is element, has both allowed whole element (ab) it is negative, is expressed asWithout allowing have negative term in element (ab), i.e.,Do not allow, this is very tight Lattice, the commodity that the expression customer that it cannot be real has bought in once doing shopping, and the commodity that may be bought but not buy；Form is about Beam, it requires that the negative element in sequence can not continuously occur, bothCan not occur, and in actual life In this sequence be recurrent, it can represent the record of shopping twice of customer, and first time customer have purchased commodity b's A is not bought simultaneously, second customer does not buy b while have purchased commodity c.But we are any currently without finding It is related to relax the research of the negative mode method for digging of these constraintss.Because can be brought after relaxing constraint much having How the problem of challenge, for example, producing the negative candidate of enormous amount, provide increasingly complex bearing and calculated comprising definition and height Spend etc..
The content of the invention
Summary of the invention
The present invention excessively strictly causes the loss in mining process many significant for constraints in existing algorithm Negative sequence pattern, and cannot really represent the buying behavior of customer these problems, it is proposed that the negative sequence based on individual event missing Pattern mining algorithm NegI-NSP.The minimal negative unit that it not only allows to bear candidate is item (rather than element), for example, it is allowed to unit There is negative term in plain (ab), can be expressed asOrAnd allow there is continuous negative element in sequence, for example, sequenceIt is allowed.So it is not only able to reflect the sequencing of commodity purchasing in customer purchasing behavior, also The true buying behavior of customer in shopping every time can be reflected.
The main thought of NegI-NSP algorithms is：First, positive sequence is excavated by existing positive sequence pattern mining algorithm Row pattern；Then, using converting the negative candidate sequence collection of discourse on politics generation, and support using known positive sequence pattern calculates negative The support of candidate；Finally, screened by minimum support (being specified by user) and obtain final negative sequence pattern.
The negative sequence pattern that more can really reflect customer purchasing behavior for obtaining is excavated by the algorithm, so that can More fully to analyze customers buying behavior so that seller can predict later business according to current merchandise sales situation Product are sold, it is also possible to find out in shopping again customer's purchase well or do not buy the rule of commodity, so that reasonable arrangement business Product are put, and improve offtake.
Detailed description of the invention
Technical scheme is as follows：
Application of the negative sequence pattern based on individual event missing in commercial product recommending, including step is as follows：
(1) define a data sequence and include a negative sequence
Customer will buy originally but the commodity without purchase are negative term, and behavior does not occur also referred to as；The once shopping of customer It is recorded as element；Element comprising at least one negative term is negative element；Shopping record of the customer within a period of time is referred to as to count According to sequence, the sequence comprising at least one negative element is referred to as negative sequence；
MPSE refers to a customer positive daughter element of the maximum in once shopping record；For example, a negative elementWhereinWithCustomer's specifically commodity of the shopping without purchase are represented, and b and d represent customer and specifically do shopping The commodity that have purchased.Its maximum positron element representation isParticularly, individual event element is born not comprising just , we are represented with sky, for example negative elementIts maximum positron element representation is
1-negMPSE refers to negative element siDaughter element, the daughter element include MPSE (si) and a negative term e,One The 1-negMPSE of individual individual event negative element is itself；MPSE(si) refer to negative element siThe positive daughter element of maximum；
MPS (ns) refers to the positive subsequence of maximum of the negative sequence ns that the commodity bought by customer are constituted, and it is by ns Comprising all positve terms constituted according to former order；For example：One negative sequence Represent without purchase Commodity, and b c d represent the commodity that have purchased.Its positive subsequence of maximum be MPS (ns)=<c d>, particularly, a positive sequence The positive subsequence of maximum of row is itself；
NSI-MSE refers to the subsequence of negative sequence ns, and the subsequence includes individual event negative element siAnd MPSE (si+1)；
Define data sequence ds=<d1 d2 ... dt>Comprising negative sequence ns=<s1 s2... sm>；Will take into full account To each negative term s in nsiNot in the interval occurred in ds, and this interval is by siMPS (ns) is divided into left subsequence Determined with right subsequence, both first final position d of left subsequencefseWith the whole starting position d of right subsequencelsbBetween area Between.
IfThen
Negative sequence ns=<s1 s2... sm>It is to be made up of m element, wherein comprising n negative element.
(1) if m>2t+1, then ds is not comprising ns；
(2) m=1 is worked as, during n=1, ifThen ds does not include ns；
(3) forAndWherein EidS- nsRepresent the negative element in ns Set, EidS+ nsThe set of the positive element in ns is represented, if met
A () is as i=1, and (s1,id(s1))∈EidS- ns,(s2,id(s2))∈EidS+ ns, ((lsb=1) or (lsb> 1), ifParticularly, element_length (s1(the element_length of)=1 (s1) represent s1Length), thenThen ds includes ns；
B () is as i=2, and (s1,id(s1))∈EidS- nsAnd element_length (s1)=1, (s2,id(s2))∈EidS- ns, ((lsb=1) or (lsb>1), if WithAs (s1,id(s1))∈EidS- nsAnd element_length (s1)>1, or (s1,id(s1))∈EidS+ ns, (s2,id(s2))∈EidS- ns, ((lsb=1) or (lsb>1) when, ifEspecially , if element_length (s2)>1, thenThen ds includes ns；
(c) as i=m, (sm-1,id(sm-1))∈EidS+ nsOr (sm-1,id(sm-1))∈EidS- ns, element_ length(sm-1)>1,(sm,id(sm))∈EidS- ns, ((fse=t) or (0<fse<T), ifSpecial element_length (sm)>1, thenWhen (sm-1,id(sm-1))∈EidS- ns∧element_length(sm-1)=1, (sm,id(sm))∈EidS- ns,(0<fse<T), such as ReallyThen ds includes ns；
D () is when 2<i<During m, (si-1,id(si-1))∈EidS+ nsOr (si-1,id(si-1))∈EidS- ns, element_ length(si-1)>1, (si,id(si))∈EidS- ns, ((si+1,id(si+1))∈EidS+ nsOr element_length (si)> 1),(fse>0 ∧ lsb=fse+1) or (fse>0∧lsb>Fse+1), if If element_length (si)>1, thenAs (si-1,id(si-1))∈EidS- nsAnd element_length (si-1)=1, (si,id(si))∈EidS- ns,(fse>0 and lsb>Fse+1), ifThen ds includes ns；Wherein fse=FSE (MPS (<s1 s2 ... si-1>), ), ds lsb=LSB (MPS (<si...sm>),ds)；
(2) Shopping Behaviors of client are analyzed using NegI-NSP algorithms, specific steps include：
A, using positive sequence mining algorithm GSP excavate obtain all of positive sequence pattern, i.e., certain a period of time in, client The commodity frequently bought；
Candidate sequence (negative is born in B, the positive sequence pattern obtained based on step A excavations, generation accordingly Sequential candidates, NSC), the negative candidate sequence is used to judge within certain a period of time, which commodity client That buys is more, and which commodity client does not buy；
C, the support that negative candidate sequence is calculated using the support of the corresponding positive sequence pattern of negative candidate sequence；
The negative sequence pattern for meeting minimum support requirement is filtered out in D, the negative candidate sequence generated from step B, i.e.,：From The negative sequence pattern of the minimum support set more than user is filtered out in the negative candidate sequence of step B generations, using these sieves The buying behavior of negative sequence pattern after choosing to client is analyzed.Businessman has according to analysis result and targetedly pushes away to client Commodity are recommended, the putting position and quantity of commodity are arranged according to client's buying habit.
According to currently preferred, the step B, the positive sequence pattern for obtaining is excavated based on step A, generation is corresponding negative Candidate sequence, specific steps include：For comprising the k positive sequence pattern of element, its negative candidate sequence is by changing positive sequence Any m negative element is obtained in row pattern；M is integer and 1≤m≤k, is 1 when not having continuous length in positive sequence pattern Element when, then the maximum of m be k.
For example：It is based on<(abc)cd>Positive sequence schema creation bears candidate sequence accordingly, and a, b, c, d refer to certain commodity, Including：
M=1,
M=2,
In order to be based on the irredundant negative candidate sequence of positive sequence schema creation and combine the true shopping of customer in reality Behavior, we generate negative candidate sequence with a kind of efficient method, and its basic thought is to change Arbitrary Term in positive sequence pattern It is negative term, if there is continuous individual event element in positive sequence pattern, simultaneously can not changes into continuous negative individual event element Item in negative element, and any multinomial element can not entirely change into negative term.
According to currently preferred, the step C, negative candidate sequence is calculated using the support of related positive sequence pattern The support of row, specific steps include：
1. a negative candidate sequence is defined；For example,
1-neglMSnsRefer to the subsequence of negative sequence ns, and the subsequence is by MPS (ns) and a negative term group Into；
1-neglMSSnsRefer to all 1-neglMS in negative sequence nsnsThe arrangement set of composition；For example,
P (1-neglMS) refers to that the positive element in sequence 1-neglMS is constant, and negative element is converted into corresponding positive element； For example：
2. one containing m element and wherein containing the n negative sequence ns of negative element, for 1≤i≤n, in sequence library D, the support sup (ns) of negative sequence ns is calculated by formula (i), formula (ii)：
Shown in the computational methods such as formula (i) of the support sup (ns) of negative sequence ns：
Sup (ns)=| { MPS (ns) }-∪n I=1{p(1-neglMSi)}| (i)
The sid of all data sequences comprising negative sequence ns is obtained using formula (i), { MPS (ns) } refers to all comprising MPS (ns) sid of data sequence, ∪n I=1{p(1-neglMSi) refer to all { p (1-neglMSi) composition sid unions of sets Collection；
When negative sequence ns isWhen, shown in the computational methods such as formula (ii) of the support sup (ns) of negative sequence ns：
It is assumed that sequence<(ac)>Support be 10, support sequence<(abc)>Sid (include sequence<(abc)>Visitor Family ID) set be { 10,20,30 }, support sequence<(ac)d>The set of sid be { 20,30,40 }, then
The pseudo-code of the algorithm for realizing algorithm NegI-NSP of the invention is as follows：
Input：D：Client's purchase sequence database；ms：It is minimum support；
Output：NSP：Set for analyzing the negative sequence pattern of customers buying behavior；
The step (1) is excavated from customer purchasing behavior sequence library with existing positive sequence pattern mining algorithm Go out all of positive sequence pattern；The set of all of positive sequence pattern and its support and sid is all stored in Hash table PSPHash, wherein, the step (2) is the Hash codes of positive sequence pattern as key；
The step (4) is for each positive sequence pattern, by above-mentioned " generation of negative candidate sequence " method next life Into negative candidate sequence NSC；
Step (5) calculates the support of each nsc in NSC by formula (i) and (ii) to step (20)；Step Suddenly (21) to step (23) is to judge to meet the negative sequence pattern NSP of minimum support；
Step (5) calculates the branch for comprising only a nsc for individual event negative element to step (8) by formula formula (ii) Degree of holding, for the support of the nsc comprising multinomial negative element or element number more than 1, is calculated such as step by formula (i) (9) to step (20)；For the latter, it is crucial that how to calculate cHash.size ()；
Step (12) to step (18) is the core concept for calculating cHash.size ()；Will be comprising p (1-neglMSi) { p (1-neglMS are arrived in sid set storagesi) set in, then calculate { p (1-neglMSi) union, then calculate set in The number of the sid for containing；Step (19) row calculates the support of nsc；
If nsc.support >=ms (nsc) so nsc is added into NSP, such as step (21) to step (23)；
Returning result, such as step (26), then screened the sequence pattern that can be used for decision-making with appropriate screening technique, The buying behavior of client is analyzed using the sequence pattern after these screenings, helps businessman to carry out targetedly Recommendations, Commodity are put on demand.
Beneficial effects of the present invention are：
The present invention is applied during customers buying behavior analysis is carried out, and can fully represent the true purchase row of customer For so as to find that customer really frequently buys or do not buy the behavior of commodity.So customer buy commodity when, using this hair Bright product that can be to its some other client's purchase frequency of recommendation than larger related purchase, so as to increase the Transaction apparatus of customer Meeting, buyer is changed into by website browsing person, improves cross-selling ability, the loyalty of client is improved, so as to improve website Economic benefit.
Specific embodiment
The present invention is further qualified with reference to embodiment, but not limited to this.
Embodiment
Application of the negative sequence pattern based on individual event missing in commercial product recommending, including step is as follows：
(1) define a data sequence and include a negative sequence
Customer will buy originally but the commodity without purchase are negative term, and behavior does not occur also referred to as；The once shopping of customer It is recorded as element；Element comprising at least one negative term is negative element；Shopping record of the customer within a period of time is referred to as to count According to sequence, the sequence comprising at least one negative element is referred to as negative sequence；
MPSE refers to a customer positive daughter element of the maximum in once shopping record；For example, a negative elementWhereinWithCustomer's specifically commodity of the shopping without purchase are represented, and b and d represent customer and specifically do shopping The commodity that have purchased.Its maximum positron element representation isParticularly, individual event element is born not comprising just , we are represented with sky, for example negative elementIts maximum positron element representation is
1-negMPSE refers to negative element siDaughter element, the daughter element include MPSE (si) and a negative term e, The 1-negMPSE of one individual event negative element is itself；MPSE(si) refer to negative element siThe positive daughter element of maximum；
MPS (ns) refers to the positive subsequence of maximum of the negative sequence ns that the commodity bought by customer are constituted, and it is by ns Comprising all positve terms constituted according to former order；For example：One negative sequence Represent without purchase Commodity, and b c d represent the commodity that have purchased.Its positive subsequence of maximum be MPS (ns)=<c d>, particularly, a positive sequence The positive subsequence of maximum of row is itself；
NSI-MSE refers to the subsequence of negative sequence ns, and the subsequence includes individual event negative element siAnd MPSE (si+1)；
Define data sequence ds=<d1 d2 ... dt>Comprising negative sequence ns=<s1 s2... sm>；Will take into full account To each negative term s in nsiNot in the interval occurred in ds, and this interval is by siMPS (ns) is divided into left subsequence Determined with right subsequence, both first final position d of left subsequencefseWith the whole starting position d of right subsequencelsbBetween area Between.
IfThen
Negative sequence ns=<s1 s2... sm>It is to be made up of m element, wherein comprising n negative element.
(1) if m>2t+1, then ds is not comprising ns；
(2) m=1 is worked as, during n=1, ifThen ds does not include ns；
(3) forAndWherein EidS- nsRepresent the negative element in ns Set, EidS+ nsThe set of the positive element in ns is represented, if met
A () is as i=1, and (s1,id(s1))∈EidS- ns,(s2,id(s2))∈EidS+ ns, ((lsb=1) or (lsb> 1), ifParticularly, element_length (s1(the element_length of)=1 (s1) represent s1Length), thenThen ds includes ns；
B () is as i=2, and (s1,id(s1))∈EidS- nsAnd element_length (s1)=1, (s2,id(s2))∈EidS- ns, ((lsb=1) or (lsb>1), if WithAs (s1,id(s1))∈EidS- nsAnd element_length (s1)>1, or (s1,id(s1))∈EidS+ ns, (s2,id(s2))∈EidS- ns, ((lsb=1) or (lsb>1) when, ifEspecially , if element_length (s2)>1, thenThen ds includes ns；
(c) as i=m, (sm-1,id(sm-1))∈EidS+ nsOr (sm-1,id(sm-1))∈EidS- ns, element_ length(sm-1)>1,(sm,id(sm))∈EidS- ns, ((fse=t) or (0<fse<T), ifSpecial element_length (sm)>1, thenWhen (sm-1,id(sm-1))∈EidS- ns∧element_length(sm-1)=1, (sm,id(sm))∈EidS- ns,(0<fse<T), such as ReallyThen ds includes ns；
D () is when 2<i<During m, (si-1,id(si-1))∈EidS+ nsOr (si-1,id(si-1))∈EidS- ns, element_ length(si-1)>1, (si,id(si))∈EidS- ns, ((si+1,id(si+1))∈EidS+ nsOr element_length (si)> 1),(fse>0 ∧ lsb=fse+1) or (fse>0∧lsb>Fse+1), if If element_length (si)>1, thenAs (si-1,id(si-1))∈EidS- nsAnd element_length (si-1)=1, (si,id(si))∈EidS- ns,(fse>0 and lsb>Fse+1), ifThen ds includes ns；Wherein fse=FSE (MPS (<s1 s2 ... si-1>),ds), Lsb=LSB (MPS (<si ... sm>),ds)；
(2) Shopping Behaviors of client are analyzed using NegI-NSP algorithms, specific steps include：
A, using positive sequence mining algorithm GSP excavate obtain all of positive sequence pattern, i.e., certain a period of time in, client The commodity frequently bought；
Candidate sequence (negative is born in B, the positive sequence pattern obtained based on step A excavations, generation accordingly Sequential candidates, NSC), the negative candidate sequence is used to judge within certain a period of time, which commodity client That buys is more, and which commodity client does not buy；Specific steps include：For comprising the k positive sequence pattern of element, its negative marquis Any m negative element is obtained during sequence is selected by changing positive sequence pattern；M is integer and 1≤m≤k, when positive sequence pattern In when there is no the element that continuous length is 1, then the maximum of m is k.
For example：It is based on<(abc)cd>Positive sequence schema creation bears candidate sequence accordingly, and a, b, c, d refer to certain commodity, Including：
M=1,
M=2,
In order to be based on the irredundant negative candidate sequence of positive sequence schema creation and combine the true shopping of customer in reality Behavior, we generate negative candidate sequence with a kind of efficient method, and its basic thought is to change Arbitrary Term in positive sequence pattern It is negative term, if there is continuous individual event element in positive sequence pattern, simultaneously can not changes into continuous negative individual event element Item in negative element, and any multinomial element can not entirely change into negative term.
C, the support that negative candidate sequence is calculated using the support of the corresponding positive sequence pattern of negative candidate sequence；Specifically Step includes：
1. a negative candidate sequence is defined；For example,
1-neglMSnsRefer to the subsequence of negative sequence ns, and the subsequence is by MPS (ns) and a negative term group Into；
1-neglMSSnsRefer to all 1-neglMS in negative sequence nsnsThe arrangement set of composition；For example,
P (1-neglMS) refers to that the positive element in sequence 1-neglMS is constant, and negative element is converted into corresponding positive element； For example：
2. one containing m element and wherein containing the n negative sequence ns of negative element, for 1≤i≤n, in sequence library D, the support sup (ns) of negative sequence ns is calculated by formula (i), formula (ii)：
Shown in the computational methods such as formula (i) of the support sup (ns) of negative sequence ns：
Sup (ns)=| { MPS (ns) }-∪n I=1{p(1-neglMSi)}| (i)
The sid of all data sequences comprising negative sequence ns is obtained using formula (i), { MPS (ns) } refers to all comprising MPS (ns) sid of data sequence, ∪n I=1{p(1-neglMSi) refer to all { p (1-neglMSi) composition sid unions of sets Collection；
When negative sequence ns isWhen, shown in the computational methods such as formula (ii) of the support sup (ns) of negative sequence ns：
It is assumed that sequence<(ac)>Support be 10, support sequence<(abc)>Sid (include sequence<(abc)>Visitor Family ID) set be { 10,20,30 }, support sequence<(ac)d>The set of sid be { 20,30,40 }, then
The negative sequence pattern for meeting minimum support requirement is filtered out in D, the negative candidate sequence generated from step B, i.e.,：From The negative sequence pattern of the minimum support set more than user is filtered out in the negative candidate sequence of step B generations, using these sieves The buying behavior of negative sequence pattern after choosing to client is analyzed.Businessman has according to analysis result and targetedly pushes away to client Commodity are recommended, the putting position and quantity of commodity are arranged according to client's buying habit.
The pseudo-code of the algorithm for realizing algorithm NegI-NSP of the invention is as follows：
Input：D：Client's purchase sequence database；ms：It is minimum support；
Output：NSP：Set for analyzing the negative sequence pattern of customers buying behavior；
The step (1) is excavated from customer purchasing behavior sequence library with existing positive sequence pattern mining algorithm Go out all of positive sequence pattern；The set of all of positive sequence pattern and its support and sid is all stored in Hash table PSPHash, wherein, the step (2) is the Hash codes of positive sequence pattern as key；
The step (4) is for each positive sequence pattern, by above-mentioned " generation of negative candidate sequence " method next life Into negative candidate sequence NSC；
Step (5) calculates the support of each nsc in NSC by formula (i) and (ii) to step (20)；Step Suddenly (21) to step (23) is to judge to meet the negative sequence pattern NSP of minimum support；
Step (5) calculates the branch for comprising only a nsc for individual event negative element to step (8) by formula formula (ii) Degree of holding, for the support of the nsc comprising multinomial negative element or element number more than 1, is calculated such as step by formula (i) (9) to step (20)；For the latter, it is crucial that how to calculate cHash.size ()；
Step (12) to step (18) is the core concept for calculating cHash.size ()；Will be comprising p (1-neglMSi) { p (1-neglMS are arrived in sid set storagesi) set in, then calculate { p (1-neglMSi) union, then calculate set in The number of the sid for containing；Step (19) row calculates the support of nsc；
If nsc.support >=ms (nsc) so nsc is added into NSP, such as step (21) to step (23)；
Returning result, such as step (26), then screened the sequence pattern that can be used for decision-making with appropriate screening technique, The buying behavior of client is analyzed using the sequence pattern after these screenings, helps businessman to carry out targetedly Recommendations, Commodity are put on demand.

Claims (3)

1. application of the negative sequence pattern based on individual event missing in commercial product recommending, it is characterised in that as follows including step：
(1) define a data sequence and include a negative sequence
Customer will buy originally but the commodity without purchase are negative term, and behavior does not occur also referred to as；The once shopping record of customer It is element；Element comprising at least one negative term is negative element；Shopping record of the customer within a period of time is referred to as data sequence Row, the sequence comprising at least one negative element is referred to as negative sequence；
MPSE refers to a customer positive daughter element of the maximum in once shopping record；
1-negMPSE refers to negative element siDaughter element, the daughter element include MPSE (si) and a negative term e,One list The 1-negMPSE of item negative element is itself；MPSE(si) refer to negative element siThe positive daughter element of maximum；
MPS (ns) refers to the positive subsequence of maximum of the negative sequence ns that the commodity bought by customer are constituted, and it in ns by including All positve terms constituted according to former order；
NSI-MSE refers to the subsequence of negative sequence ns, and the subsequence includes individual event negative element siAnd MPSE (si+1)；
Define data sequence ds=<d1 d2 ... dt>Comprising negative sequence ns=<s1 s2 ... sm>；
(2) Shopping Behaviors of client are analyzed using NegI-NSP algorithms, specific steps include：
A, using positive sequence mining algorithm GSP excavate obtain all of positive sequence pattern, i.e., certain a period of time in, client is frequent The commodity of purchase；
Candidate sequence is born in B, the positive sequence pattern obtained based on step A excavations, generation accordingly, and the negative candidate sequence is used to sentence Break within certain a period of time, it is many which commodity client buys, and which commodity client does not buy；
C, the support that negative candidate sequence is calculated using the support of the corresponding positive sequence pattern of negative candidate sequence；
The negative sequence pattern for meeting minimum support requirement is filtered out in D, the negative candidate sequence generated from step B, i.e.,：From step The negative sequence pattern of the minimum support set more than user is filtered out in the negative candidate sequence of B generations, after these screenings Buying behavior of the negative sequence pattern to client be analyzed.
2. application of the negative sequence pattern based on individual event missing according to claim 1 in commercial product recommending, its feature exists In the step B excavates the positive sequence pattern for obtaining based on step A, and candidate sequence is born in generation accordingly, and specific steps include： For comprising the k positive sequence pattern of element, its negative candidate sequence be by changing positive sequence pattern in any m negative element obtain Arrive；M is integer and 1≤m≤k, and when not having element that continuous length is 1 in positive sequence pattern, then the maximum of m is k.
3. application of the negative sequence pattern based on individual event missing according to claim 1 in commercial product recommending, its feature exists In the step C calculates the support of negative candidate sequence, specific steps bag using the support of related positive sequence pattern Include：
1. a negative candidate sequence is defined；
1-neglMSnsRefer to the subsequence of negative sequence ns, and the subsequence is made up of MPS (ns) and a negative term；
1-neglMSSnsRefer to all 1-neglMS in negative sequence nsnsThe arrangement set of composition；
2. one containing m element and wherein containing the n negative sequence ns of negative element, for1 ≤ i≤n, in sequence library D, the support sup (ns) of negative sequence ns is calculated by formula (i), formula (ii)：
Shown in the computational methods such as formula (i) of the support sup (ns) of negative sequence ns：
Sup (ns)=| { MPS (ns) }-∪n I=1{p(1-neglMSi)}| (i)
The sid of all data sequences comprising negative sequence ns is obtained using formula (i), { MPS (ns) } refers to all comprising MPS (ns) Data sequence sid, ∪n I=1{p(1-neglMSi) refer to all { p (1-neglMSi) composition sid union of sets collection；
When negative sequence ns isWhen, shown in the computational methods such as formula (ii) of the support sup (ns) of negative sequence ns：
CN201710018624.XA 2017-01-11 2017-01-11 Application of the negative sequence pattern based on individual event missing in commercial product recommending Pending CN106910104A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111949711A (en) * 2020-08-18 2020-11-17 齐鲁工业大学 Commodity recommendation system based on decision-making high-utility negative sequence rule mining and working method thereof

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111949711A (en) * 2020-08-18 2020-11-17 齐鲁工业大学 Commodity recommendation system based on decision-making high-utility negative sequence rule mining and working method thereof
CN111949711B (en) * 2020-08-18 2021-06-01 齐鲁工业大学 Commodity recommendation system based on decision-making high-utility negative sequence rule mining and working method thereof

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