CN102495864A - Collaborative filtering recommending method and system based on grading - Google Patents
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Abstract
The invention discloses a collaborative filtering recommending method and system based on grading, which relates to the technical field of collaborative filtering recommendation. In the method, the similarity among users is calculated by using the statistical characteristics of history grading data of the users, and a project which is not evaluated by a current user is calculated through other users which have high similarity with the current user, so that the problem of incapability of calculating similarity or inaccurate similarity caused by difficulty in finding a common grading item among the users is solved, and accurate and rapid project recommendation can be realized.
Description
Technical field
The present invention relates to the collaborative filtering recommending technical field, particularly a kind of collaborative filtering recommending method and system based on scoring.
Background technology
The epoch of information explosion have been brought us in the fast development of Internet technology; Appear in the time of magnanimity information; Not only make the user be difficult to therefrom find to control oneself interested content, and make a large amount of information of knowing for the people less become " the dark information " in the network, can't be obtained by the general user.Commending system utilizes historical selection information of user or the potential hobby of similarity relation excavation user, and then recommends through setting up the binary relation between user and the project (for example: product, film, music, program etc.).
Had many classical commending systems at present, the collaborative filtering recommending system is the commending system that is suggested the earliest and is used widely.The score data that its core concept just is based on the similar nearest-neighbors of scoring produces recommendation to the targeted customer.Because nearest-neighbors is closely similar to scoring and the targeted customer of project, so the targeted customer can approach through the weighted mean value that nearest-neighbors is marked to this project the scoring of scoring item not.Typestry is the commending system based on collaborative filtering that puts forward the earliest, and the targeted customer it may be noted that and relatively more similar other users of own hobby.GroupLens is based on the automation collaborative filtered recommendation system of user's scoring, is used for film and news and recommends.Other systems that utilize collaborative filtering method to recommend also have the books commending system of Amazon.com, joke commending system of Jester or the like.
Compare with general commending system, the collaborative filtering recommending system has two big advantages: the one, there is not special requirement to recommending object, and can handle music, film etc. and be difficult to carry out the object that text structureization is represented; The 2nd, have the ability of recommending fresh information, can find the user potential but the interest preference oneself do not discovered as yet.
Traditional collaborative filtering recommending system utilizes common scoring item calculating similarity between different user, and the similarity calculation method of main flow comprises: cosine similarity method and relevant similarity method; The targeted customer predicts the weighted mean value of the scoring of project through the bigger neighbours of similarity the scoring of scoring item not.Can find out that the recommendation precision of collaborative filtering recommending system depends on the accuracy that similarity is calculated between the user.Yet in the huge network system of user and the number of entry, under the extremely sparse situation of user's score data, be difficult to find common scoring item between the user, thereby cause between the user similarity result of calculation inaccurate even can't calculate similarity.When the collaborative filtering recommending system has obtained widespread use, also be faced with a lot of problems, for example how new user being recommended or how to recommend new product is the cold start-up problem to the user, the sparse property problem of marking, algorithm scalability problem etc.In addition, traditional collaborative filtering recommending algorithm is along with number of users increases, the linear increasing of calculated amount, real-time performance worse and worse, response speed is also more and more slower simultaneously.
Summary of the invention
The technical matters that (one) will solve
The technical matters that the present invention will solve is: how in the huge collaborative filtering recommending system of user and the number of entry under the extremely sparse situation of user's score data, solve and be difficult to find between the user common scoring and cause calculating similarity or the inaccurate problem of similarity.
(2) technical scheme
For solving the problems of the technologies described above, the invention provides a kind of collaborative filtering recommending method based on scoring, may further comprise the steps:
S1: travel through all users in the current network system, obtain all users' historical score data;
S2: the statistical nature according to all users historical score data is separately confirmed the similarity degree between each user;
S3: select k other user the highest with active user's similarity degree, according to said k other user to active user's historical score data of scoring item not, come to the active user not scoring item predict;
S4: predicting the outcome of each user screened, produce the recommended project to each user.
Preferably, the similarity degree among the step S2 between each user calculates through formula,
Wherein, sim (u v) is the similarity degree between user u and the user v,
With
Be the tri-vector of respective user u and user v successively,
S
uAnd R
uBe average, variance and the extreme difference of the historical score data of respective user u successively,
S
vAnd R
vBe average, variance and the extreme difference of the historical score data of respective user v successively.
Preferably, among the step S3 through following formula come to the active user not scoring item predict,
Wherein, P
U, iBe the prediction mark of the not scoring item i of active user u, S (u) was for carrying out user's set of scoring, r to the not scoring item i of active user u among k other user
X, iBe the scoring of user x to the not scoring item i of active user u, user x is certain element among the S (u).
Preferably, when among the step S4 predicting the outcome of each user being screened, compare through average, if greater than average, then as the recommended project with the prediction mark of active user's not scoring item and active user's historical score data.
The invention also discloses a kind of collaborative filtering recommending system, comprising based on scoring:
The historical data computing module is used for traveling through all users of current network system, obtains all users' historical score data;
The similarity degree computing module is used for confirming the similarity degree between each user according to the statistical nature of all users historical score data separately;
Prediction module is used to select k other user the highest with active user's similarity degree, according to said k other user to active user's historical score data of scoring item not, come to the active user not scoring item predict;
Screening module is used for predicting the outcome of each user screened, and produces the recommended project to each user.
Preferably, the similarity degree in the similarity degree computing module between each user calculates through formula,
Wherein, sim (u v) is the similarity degree between user u and the user v,
With
Be the tri-vector of respective user u and user v successively,
S
uAnd R
uBe average, variance and the extreme difference of the historical score data of respective user u successively,
S
vAnd R
vBe average, variance and the extreme difference of the historical score data of respective user v successively.
Preferably, in the prediction module through following formula to the active user not scoring item predict,
Wherein, P
U, iBe the prediction mark of the not scoring item i of active user u, S (u) was for carrying out user's set of scoring, r to the not scoring item i of active user u among k other user
X, iBe the scoring of user x to the not scoring item i of active user u, user x is certain element among the S (u).
Preferably, when in the screening module predicting the outcome of each user being screened, compare through average, if greater than average, then as the recommended project with the prediction mark of active user's not scoring item and active user's historical score data.
(3) beneficial effect
The present invention utilizes the statistical nature of the historical score data of user to calculate the similarity degree between each user; Through calculating the unvalued project of active user with other higher users of active user's similarity degree; Realized in the huge collaborative filtering recommending system of user and the number of entry under the extremely sparse situation of user's score data; Solved and be difficult to find between the user common scoring and cause to calculate similarity or the inaccurate problem of similarity, can realize accurately and project recommendation fast.
Description of drawings
Fig. 1 is the process flow diagram based on the collaborative filtering recommending method of marking according to one embodiment of the present invention;
Fig. 2 only comprises 4 users in the current network system in an embodiment of the present invention, and these 4 users are to the synoptic diagram of the score data of 10 projects;
Fig. 3 is the synoptic diagram that process is calculated the statistic sign matrix of back acquisition in an embodiment of the present invention;
Fig. 4 is the synoptic diagram of similarity degree between each user in an embodiment of the present invention;
Fig. 5 is user's prediction mark of scoring item not in an embodiment of the present invention, and the project recommendation result that obtains of each user;
Fig. 6 be in an embodiment of the present invention with " MovieLens 100K " as data set; Select 80% at random as training set; Remaining 20% as test set, adopts the result of the mean absolute error of method of the present invention and traditional collaborative filtering method with said k other number of users variation respectively;
Fig. 7 is as data set with " MovieLens 100K "; Select 80% at random as training set; Remaining 20% as test set, adopts the result of the root mean square absolute error of method of the present invention and traditional collaborative filtering method with said k other number of users variation respectively.
Embodiment
Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.
Fig. 1 is the process flow diagram based on the collaborative filtering recommending method of marking according to one embodiment of the present invention, and with reference to Fig. 1, the method for this embodiment may further comprise the steps:
S1: travel through all users in the current network system, obtain all users' historical score data;
S2: the statistical nature according to all users historical score data is separately confirmed the similarity degree between each user; In this embodiment, make up each user's tri-vector earlier, calculate the similarity degree between each user according to each user's tri-vector through the included angle cosine value again; With the included angle cosine value between each user characteristics vector as each other similarity degree.Two vector angles are more little, and cosine value is big more, show that the user is similar more;
S3: select k other user the highest with active user's similarity degree, according to said k other user to active user's historical score data of scoring item not, come to the active user not scoring item predict;
S4: predicting the outcome of each user screened, produce the recommended project to each user.
Preferably, the similarity degree among the step S2 between each user calculates through formula,
Wherein, sim (u v) is the similarity degree between user u and the user v,
With
Be the tri-vector of respective user u and user v successively,
S
uAnd R
uBe average, variance and the extreme difference of the historical score data of respective user u successively,
S
vAnd R
vBe average, variance and the extreme difference of the historical score data of respective user v successively.For example, (
S
u, R
u) statistical nature of historical score data of expression user u, mean value is represented the average score hobby of user to project, and variance is represented fluctuation or the deviation of user to the project scoring, and extreme difference is represented the scope hobby of user to scoring.Explain that to a certain extent these three values will influence the user to the not scoring in future of scoring item.
Wherein, mean value, variance and the extreme difference of all users historical score data separately calculates through formula,
R
u=max(r
u,i)-min(r
u,i)
Wherein,
S
u, R
uAverage, variance and the extreme difference of representing the historical score data of user u respectively, I (u) are all scoring item set of user u, and size (I (u)) is a user u scoring item sum, r
U, iBe the scoring of user u to project i, max (r
U, i) and min (r
U, i) represent user u scoring maximal value and minimum value respectively.
Preferably, among the step S3 through following formula come to the active user not scoring item predict,
Wherein, P
U, iBe the prediction mark of the not scoring item i of active user u, S (u) was for carrying out user's set of scoring, r to the not scoring item i of active user u among k other user
X, iBe the scoring of user x to the not scoring item i of active user u, user x is certain element among the S (u).
Preferably, when among the step S4 predicting the outcome of each user being screened, compare through average, if greater than average, then as the recommended project with the prediction mark of active user's not scoring item and active user's historical score data.
With a simple example above-mentioned algorithm recommendation process is described below.With reference to Fig. 2, this example comprises the scoring of 4 users to 10 projects, each user 5 projects of marking wherein, and we need be to 5 projects that the targeted customer does not mark prediction of marking, and makes corresponding recommendation.Step-by-step procedures is following:
1, calculates the statistical nature of the historical score data of each user,, obtain statistic as shown in Figure 3 and characterize matrix, promptly describe user characteristics with tri-vector like average, variance and extreme difference.
2, utilize the cosine similarity method to calculate the cosine angle value between each vector, obtain user's similarity matrix, with reference to Fig. 4, the value of similarity degree is big more, explains that similarity degree is high more between the user, and it is similar more to mark, like user u in the example
1With user u
2Similarity degree is the highest, and user u
4With user u
3Similarity degree is the highest.
3, owing to number of users in the example is less; Here we select the nearest-neighbors of 3 users that similarity degree is the highest as the targeted customer; And to the targeted customer not scoring item carry out the similarity weighting, obtain scoring prediction matrix shown in Figure 5, according to prediction score value size scoring item is not sorted; Select the prediction score value to produce recommendation greater than the project of targeted customer's average score, the project of Fig. 5 mid-score overstriking is final recommendation results.
Fig. 6 be in an embodiment of the present invention with " MovieLens 100K " as data set; Select 80% at random as training set; Remaining 20% as test set, adopts the result of the mean absolute error of method of the present invention and traditional collaborative filtering method with said k other number of users variation respectively; Fig. 7 is as data set with " MovieLens 100K "; Select 80% at random as training set; Remaining 20% as test set, adopts the result of the root mean square absolute error of method of the present invention and traditional collaborative filtering method with said k other number of users variation respectively.。
Collaborative filtering recommending method based on scoring proposed by the invention, its advantage is following:
(1) utilizes the statistic of user's score information to calculate similarity between the user, can effectively overcome and be difficult to find between the user common scoring under the sparse situation of scoring and cause to calculate similarity or similarity is calculated inaccurate shortcoming;
(2) to each user, all corresponding unique one group of statistic (average, variance, extreme difference), the similarity between the different user are calculated and all in three dimensions, are carried out, and have avoided between different user common scoring item to count the dimension difference that difference causes;
Faster than the searching of common scoring item, this algorithm advisory speed is faster more than 10 times than traditional collaborative filtering recommending algorithm far away in the calculating of (3) data statistics amount; Improved about 10% recommendation precision simultaneously, made the present invention in actual e-commerce system is used, obtain more performance.
The invention also discloses a kind of collaborative filtering recommending system, it is characterized in that, comprising based on scoring:
The historical data statistical module is used for traveling through all users of current network system, obtains all users' historical score data;
The similarity degree computing module is used for confirming the similarity degree between each user according to the statistical nature of all users historical score data separately;
Prediction module is used to select k other user the highest with active user's similarity degree, according to said k other user to active user's historical score data of scoring item not, come to the active user not scoring item predict;
Screening module is used for predicting the outcome of each user screened, and produces the recommended project to each user.
Preferably, the similarity degree in the similarity degree computing module between each user calculates through formula,
Wherein, sim (u v) is the similarity degree between user u and the user v,
With
Be the tri-vector of respective user u and user v successively,
S
uAnd R
uBe average, variance and the extreme difference of the historical score data of respective user u successively,
S
vAnd R
vBe average, variance and the extreme difference of the historical score data of respective user v successively.
Preferably, in the prediction module through following formula to the active user not scoring item predict,
Wherein, P
U, iBe the prediction mark of the not scoring item i of active user u, S (u) was for carrying out user's set of scoring, r to the not scoring item i of active user u among k other user
X, iBe the scoring of user x to the not scoring item i of active user u, user x is certain element among the S (u).
Preferably, when in the screening module predicting the outcome of each user being screened, compare through average, if greater than average, then as the recommended project with the prediction mark of active user's not scoring item and active user's historical score data.
Above embodiment only is used to explain the present invention; And be not limitation of the present invention; The those of ordinary skill in relevant technologies field under the situation that does not break away from the spirit and scope of the present invention, can also be made various variations and modification; Therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (8)
1. the collaborative filtering recommending method based on scoring is characterized in that, may further comprise the steps:
S1: travel through all users in the current network system, obtain all users' historical score data;
S2: the statistical nature according to all users historical score data is separately confirmed the similarity degree between each user;
S3: select k other user the highest with active user's similarity degree, according to said k other user to active user's historical score data of scoring item not, come to the active user not scoring item predict;
S4: predicting the outcome of each user screened, produce the recommended project to each user.
2. the method for claim 1 is characterized in that, the similarity degree among the step S2 between each user calculates through formula,
Wherein, sim (u v) is the similarity degree between user u and the user v,
With
Be the tri-vector of respective user u and user v successively,
S
uAnd R
uBe average, variance and the extreme difference of the historical score data of respective user u successively,
S
vAnd R
vBe average, variance and the extreme difference of the historical score data of respective user v successively.
3. method as claimed in claim 2 is characterized in that, among the step S3 through following formula come to the active user not scoring item predict,
Wherein, P
U, iBe the prediction mark of the not scoring item i of active user u, S (u) was for carrying out user's set of scoring, r to the not scoring item i of active user u among k other user
X, iBe the scoring of user x to the not scoring item i of active user u, user x is certain element among the S (u).
4. like each described method in the claim 1~3; It is characterized in that; When among the step S4 predicting the outcome of each user being screened; Average through with the prediction mark of active user's not scoring item and active user's historical score data compares, if greater than average, then as the recommended project.
5. the collaborative filtering recommending system based on scoring is characterized in that, comprising:
The historical data statistical module is used for traveling through all users of current network system, obtains all users' historical score data;
The similarity degree computing module is used for confirming the similarity degree between each user according to the statistical nature of all users historical score data separately;
Prediction module is used to select k other user the highest with active user's similarity degree, according to said k other user to active user's historical score data of scoring item not, come to the active user not scoring item predict;
Screening module is used for predicting the outcome of each user screened, and produces the recommended project to each user.
6. system as claimed in claim 5 is characterized in that the similarity degree in the similarity degree computing module between each user calculates through formula,
Wherein, sim (u v) is the similarity degree between user u and the user v,
With
Be the tri-vector of respective user u and user v successively,
S
uAnd R
uBe average, variance and the extreme difference of the historical score data of respective user u successively,
S
vAnd R
vBe average, variance and the extreme difference of the historical score data of respective user v successively.
7. system as claimed in claim 6 is characterized in that, in the prediction module through following formula to the active user not scoring item predict,
Wherein, P
U, iBe the prediction mark of the not scoring item i of active user u, S (u) was for carrying out user's set of scoring, r to the not scoring item i of active user u among k other user
X, iBe the scoring of user x to the not scoring item i of active user u, user x is certain element among the S (u).
8. like each described system in the claim 5~7; It is characterized in that; When in the screening module predicting the outcome of each user being screened; Average through with the prediction mark of active user's not scoring item and active user's historical score data compares, if greater than average, then as the recommended project.
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