CN108288220A - A kind of Technologies of Recommendation System in E-Commerce based on user interest variation - Google Patents
A kind of Technologies of Recommendation System in E-Commerce based on user interest variation Download PDFInfo
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- CN108288220A CN108288220A CN201810301409.5A CN201810301409A CN108288220A CN 108288220 A CN108288220 A CN 108288220A CN 201810301409 A CN201810301409 A CN 201810301409A CN 108288220 A CN108288220 A CN 108288220A
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Abstract
The present invention provides a kind of Technologies of Recommendation System in E-Commerce based on user interest variation, including modeling module, recommendation generation module and user terminal；Modeling module builds user commodity rating matrix U and user's scoring time matrix R for obtaining user data from the server of e-commerce website according to the user data of acquisition；Recommend generation module to be used to generate commercial product recommending list according to user commodity rating matrix U and user's scoring time matrix R, and commercial product recommending list is pushed into user terminal；User terminal is for receiving commercial product recommending list.The problem of present invention may can change over time for the interest of user, the interest of user is changed problem by structural damping function to take into account, the hobby of user can be more accurately held, the subsequently accuracy to user's Recommendations is improved.
Description
Technical field
The present invention relates to the technical fields of Technologies of Recommendation System in E-Commerce, and in particular to a kind of electricity based on user interest variation
Sub- commercial affairs commending system.
Background technology
In recent years, the appearance of e-commerce makes commodity circulation that revolutionary transformation have occurred, first, model may be selected in consumer
It encloses and is greatly widened, second is that the decrease of regional limitation.But also band is a series of while flourish asks for e-commerce
Topic, if Amazon has millions of kinds of commodity, eBay China to have about 2,000,000 retail shops, the energy and knowledge of consumer in contrast
It is all very limited, it is difficult to be quickly found out the commodity of oneself needs.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of Technologies of Recommendation System in E-Commerce changed based on user interest.
The purpose of the present invention is realized using following technical scheme：
A kind of Technologies of Recommendation System in E-Commerce based on user interest variation, including modeling module, recommendation generation module and use
Family terminal；
Modeling module is built for obtaining user data from the server of e-commerce website according to the data of acquisition
User-commodity rating matrix U and user-scoring time matrix R；
Generation module is recommended to be used to generate commercial product recommending according to user-commodity rating matrix and user-scoring time matrix
List, and commercial product recommending list is pushed into user terminal；
User terminal is for receiving commercial product recommending list.
Beneficial effects of the present invention are：Compared with prior art, the present invention can pushing away with the time for the interest of user
The interest of user is changed problem by structural damping function and taken into account by the problem of moving and may changing, can be more accurate
The hobby of true assurance user, improves the subsequently accuracy to user's Recommendations.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is a kind of Technologies of Recommendation System in E-Commerce structure chart changed based on user interest of the present invention；
Fig. 2 is the frame construction drawing that the present invention recommends generation module.
Reference numeral：Modeling module 1；Recommend generation module 2；User terminal 3；Information processing submodule 21 recommends submodule
Block 22；Similarity calculated 220；Prediction scoring unit 221.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of Technologies of Recommendation System in E-Commerce based on user interest variation, the system includes modeling module 1, pushes away
Recommend generation module 2 and user terminal 3.
Modeling module 1 from the server of e-commerce website for obtaining user data, according to the user data of acquisition
Build user-commodity rating matrix U and user-scoring time matrix R.
Generation module 2 is recommended to be used to generate commodity according to user-commodity rating matrix U and user-scoring time matrix R and push away
List is recommended, and commercial product recommending list is pushed into user terminal 3.
User terminal 3 is for receiving commercial product recommending list.
Preferably, user terminal 3 is smart mobile phone or tablet computer.
Preferably, user-commodity rating matrix U=(uij)m×n, the user-rating matrix R=(rij)m×n, wherein
uijIt is score values of the user i to commodity j, rijBeing user i scores time of origin to commodity j, i=1,2 ..., m, j=1, and 2 ...,
n。
Preferably, referring to Fig. 2, it includes information processing submodule 21 and recommendation submodule 22 to recommend generation module 2.
Information processing submodule 21 is used for user-commodity rating matrix U and user-scoring time matrix R processing,
It obtains that user can be described over time to the interest attenuation degree of integration value of commercial productainterests attenuation degree.
Submodule 22 is recommended to be used to, according to obtained interest attenuation degree of integration value and user-commodity rating matrix U, calculate
User scores to the prediction of commodity, carries out descending arrangement to commodity according to obtained prediction scoring, is selected from the commodity after sequence
It selects top n commodity and generates commercial product recommending list, and the commercial product recommending list is transmitted to user terminal 3, wherein N is self-defined
Recommendations number.
Preferably, obtaining that user can be described over time to the interest attenuation of commercial productainterests attenuation degree synthesis journey
Angle value, specifically：
(1) according to user-commodity rating matrix U and user-scoring time matrix R, user couple is calculated using attenuation function
The interest attenuation value of commodity, wherein the attenuation function is：
In formula, Ra(t) be user a interest attenuation value, a ∈ { 1,2 ..., m }, t are current time, tajIt is user a to quotient
Product j scoring time of origins, αajIt is personalized weight factors of the user a to commodity j, uajIt is score values of the user a to commodity j, j
∈{1,2,…,n}；
(2) according to user-scoring time matrix R, different user is described to same part commodity using time correlation degree function
Score the degree of correlation of time of origin, wherein time correlation degree function is：
In formula, Sab(t) it is time correlation degree functional value between user a and user b, tajIt is that user a scores to commodity j
Time of origin, tbjBeing user b scores time of origin to commodity j；
(3) it according to the interest attenuation value and time correlation degree functional value of step (1) and step (2), is calculated and is used using following formula
Family a is over time to the interest attenuation degree of integration value of commodity, wherein the calculating formula of interest attenuation degree of integration value is：
In formula, Za(t) be user a interest attenuation degree of integration value, ε is weight factor, and 0 ＜ ε ＜ 1, Ra(t) it is to use
The interest attenuation value of family a, Sab(t) it is time correlation degree functional value between user a and user b,It is b couples of user a and user
The related coefficient that same part commodity j scores.
Advantageous effect：It may change for the interest of user, respectively from same user to the interest of different commodity
Decaying behavior and different user describe the scoring degree of correlation of same commodity the variation feelings of user interest as time goes by
Condition, which, which can care for, objectively reflects the variation tendency of user over time to commodity scoring, and then is conducive to electronics quotient
Business platform is more accurately target user's Recommendations.
Preferably, it includes similarity calculated 220 and prediction scoring unit 221 to recommend submodule 22.
Similarity calculated 220, for calculating the similarity Sim (c, k) between target user c and other users,
In, k ∈ { 1,2 ..., m }, if Sim (c, k) ＞ λth(λthIt is the threshold value of setting), then the user k is added to target user c
Nearest-neighbor Ω, wherein the calculating formula for calculating the similarity Sim (c, k) between target user c and other users
For：
In formula, Simj(c, k) is the similarity value of target user c and user k to commodity j, and n is the commodity for participating in scoring
Number, and j={ 1,2 ..., n }, rcjIt is score values of the target user c to commodity j,It is that target user c puts down all commodity
Equal score value, rkjIt is score values of the user k to commodity j,It is average score values of the user k to all commodity, tcjIt is that target is used
Time when family c scores to commodity j, tkjIt is time when user k scores to commodity j, Zc(t) it is the emerging of user c
Interest decaying degree of integration value, Zk(t) be user k interest attenuation degree of integration value, tmaxBe in user-scoring time matrix R most
Big time, tminIt is minimum time in user-scoring time matrix R.
Advantageous effect：User-commodity rating matrix and user-scoring time matrix are combined to user c and user k
The similarity value of commodity j is calculated, which has fully considered the situation of change of user interest as time goes by, this does
Method more meets objective law, the similarity value accuracy higher between obtained user, accurately recommends quotient to user to be follow-up
Product lay the foundation.
Preferably, prediction scoring unit 221, for according to obtained similarity, calculating target user c to not commenting
The prediction scoring of the commodity divided carries out descending arrangement to commodity according to obtained prediction scoring, is selected from the commodity after sequence
Top n commodity generate commercial product recommending list, and the commercial product recommending list is transmitted to user terminal 3, and N is customized recommendation quotient
Product number.Wherein, it calculates target user c and prediction scoring is carried out to unrated commodity, specifically utilize the prediction of lower section to score public
Formula is calculated：
In formula, Score (c, s) is prediction score values of the target user c to the commodity s that do not score before,It is all commented
Divide user to the average score of commodity s, rksIt is score values of the user k to commodity s, tcurIt is the current time for generating recommendation behavior,
tcsIt is scoring times of the target user c to commodity s, tksIt is scoring times of the user k to commodity s, Sims(c, k) is target user
For c and user k to the similarity value of commodity s, Ω is the nearest neighbor set of target user c, and Ω=1,2 ..., k ...,
M}。
Advantageous effect：When using prediction scoring is carried out to the commodity not scored to the similarity value of commodity between user,
Pass through introducingCarry out the interests change of analog subscriber, which solves user interest variation commodity and tested and assessed in advance
The influence of timesharing improves the accuracy of prediction scoring.In view of currently recommending time and reality to carry out the scoring time to commodity
Influence to commercial product recommending, the algorithm consider the influence that interest changes over time so that recommend output result closer to reality
Situation improves the timeliness of recommendation the considerations of adding to generating the current time information recommended.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (5)
1. a kind of Technologies of Recommendation System in E-Commerce based on user interest variation, characterized in that including modeling module, recommend to generate mould
Block and user terminal；
The modeling module, for obtaining user data from the server of e-commerce website, according to the user data of acquisition
Build user-commodity rating matrix U and user-scoring time matrix R；
The recommendation generation module is used to generate commodity according to user-commodity rating matrix U and user-scoring time matrix R and push away
List is recommended, and the commercial product recommending list is pushed into the user terminal；
The user terminal is for receiving the commercial product recommending list.
2. Technologies of Recommendation System in E-Commerce according to claim 1, characterized in that the user terminal be smart mobile phone or
Tablet computer.
3. Technologies of Recommendation System in E-Commerce according to claim 2, characterized in that the user-commodity rating matrix U=
(uij)m×n, the user-rating matrix R=(rij)m×n, wherein uijIt is score values of the user i to commodity j, rijIt is i couples of user
Commodity j scoring time of origins, i=1,2 ..., m, m is number of users, and j=1,2 ..., n, n is commodity number.
4. Technologies of Recommendation System in E-Commerce according to claim 3, characterized in that the recommendation generation module includes at information
It manages submodule and recommends submodule；
Described information handle submodule, for the user-commodity rating matrix U and user-scoring time matrix R into
Row processing, obtains that user can be described over time to the interest attenuation degree of integration value of commercial productainterests attenuation degree；
The recommendation submodule, the interest attenuation degree of integration value obtained for basis and the user-commodity rating matrix U,
It calculates user to score to the prediction of commodity, descending arrangement is carried out to commodity according to obtained prediction scoring, from the commodity after sequence
Middle selection top n commodity generate commercial product recommending list, and the commercial product recommending list is transmitted to user terminal, wherein N is certainly
The Recommendations number of definition.
5. Technologies of Recommendation System in E-Commerce according to claim 4, characterized in that described to obtain that user is described at any time
The interest attenuation degree of integration value to commercial productainterests attenuation degree is elapsed, specifically：
(1) it according to the user-commodity rating matrix U and the user-scoring time matrix R, is calculated and is used using attenuation function
Interest attenuation value of the family to commodity, wherein the attenuation function is：
In formula, Ra(t) be user a interest attenuation value, a ∈ { 1,2 ..., m }, t are current time, tajIt is user a to commodity j
Score time of origin, αajIt is personalized weight factors of the user a to commodity j, uajIt is score values of the user a to commodity j, j ∈
{1,2,…,n}；
(2) according to the user-scoring time matrix R, different user is described to same part commodity using time correlation degree function
Score the degree of correlation of time of origin, wherein the time correlation degree function is：
In formula, Sab(t) it is time correlation degree functional value between user a and user b, tajWhen being that commodity j scorings occur for user a
Between, tbjBeing user b scores time of origin to commodity j；
(3) according to the interest attenuation value and time correlation degree functional value of step (1) and step (2), using following formula calculate user a with
Time elapses to the interest attenuation degree of integration values of commodity, wherein the calculating formula of the interest attenuation degree of integration value is：
In formula, Za(t) be user a interest attenuation degree of integration value, ε is weight factor, and 0 ＜ ε ＜ 1, Ra(t) it is user a
Interest attenuation value, Sab(t) it is time correlation degree functional value between user a and user b,It is user a and user b to same
The related coefficient that part commodity j scores.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109087021A (en) * | 2018-08-21 | 2018-12-25 | 平安科技（深圳）有限公司 | Sublet the method and terminal device of room assessment |
| CN117132356A (en) * | 2023-08-29 | 2023-11-28 | 重庆大学 | Recommendation method, device and system based on self-adaptive user interest change period |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104281956A (en) * | 2014-10-27 | 2015-01-14 | 南京信息工程大学 | Dynamic recommendation method capable of adapting to user interest changes based on time information |
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2018
- 2018-04-04 CN CN201810301409.5A patent/CN108288220A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104281956A (en) * | 2014-10-27 | 2015-01-14 | 南京信息工程大学 | Dynamic recommendation method capable of adapting to user interest changes based on time information |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109087021A (en) * | 2018-08-21 | 2018-12-25 | 平安科技（深圳）有限公司 | Sublet the method and terminal device of room assessment |
| CN109087021B (en) * | 2018-08-21 | 2024-01-05 | 平安科技（深圳）有限公司 | Method for evaluating renting house and terminal equipment |
| CN117132356A (en) * | 2023-08-29 | 2023-11-28 | 重庆大学 | Recommendation method, device and system based on self-adaptive user interest change period |
| CN117132356B (en) * | 2023-08-29 | 2024-02-13 | 重庆大学 | Recommendation method, device and system based on self-adaptive user interest change period |
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