CN104794367B - Medical treatment resource scoring based on hidden semantic model is with recommending method - Google Patents

Medical treatment resource scoring based on hidden semantic model is with recommending method Download PDF

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CN104794367B
CN104794367B CN201510240697.4A CN201510240697A CN104794367B CN 104794367 B CN104794367 B CN 104794367B CN 201510240697 A CN201510240697 A CN 201510240697A CN 104794367 B CN104794367 B CN 104794367B
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matrix
user
resource items
resource
hidden
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CN104794367A (en
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周异
周曲
金博
齐开悦
陈凯
查宏远
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Nanjing Ji Yun Information Technology Co Ltd
Ningbo Ke Nuopu Information Technology Co Ltd
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Nanjing Ji Yun Information Technology Co Ltd
Ningbo Ke Nuopu Information Technology Co Ltd
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Abstract

The invention provides a kind of medical treatment resource scoring based on hidden semantic model with recommending method, step:The first step, obtain and recommend resource item data, and carry out data filtering and cleaning;Second step, use the parameters in the learning automaton training SVD++ models of continuous action;3rd step, for user, to all resource items in set, score in predicting value of the user to each resource items is calculated based on hidden semantic model respectively;4th step, TopN recommendation lists are obtained using the sort algorithm after improvement.The present invention will predict appraisal result and to the degree of correlation of most related hidden class as the foundation of generation recommendation list, it reference is also made to the calibration coefficients of resource items in itself simultaneously, the resource items that can ensure to recommend are more likely to cause the interest of patient, while it is also ensured that resource items have sufficiently high scoring to go to obtain liking for patient.

Description

Medical treatment resource scoring based on hidden semantic model is with recommending method
Technical field
The present invention relates to the enigmatic language justice model score prediction of medical treatment resource in medical big data field and TopN to recommend application, Specifically, refer to be based on enigmatic language justice in medical big data with what gradient descent algorithm was combined based on learning automaton The medical treatment resource scoring of model is with recommending method.
Background technology
Scoring is widely used already with recommendation function in current internet product, commercial product recommending such as Taobao, Facebook friend recommendation, news recommendation of Baidu etc., the either associated topic of search engine web site, picture are recommended, electricity The hot-sale products of sub- business web site is recommended, personalized product is recommended, or the friend recommendation of various social network sites, popular application push away Recommend etc., recommend it is really omnipresent, it is all-pervasive, and the proportion due to recommendation shared by caused value is always all It is not in any more.For above-mentioned recommendation problem, proposed algorithm is core technology.Proposed algorithm is the historical behavior according to user With the feature of resource items, certain recommended models is established, with certain mathematics or statistical method, thus it is speculated that it is most possible to go out user Like or resource items interested.For many years, there are a considerable amount of proposed algorithms, such as:Pushing away based on collaborative filtering Recommend algorithm, the proposed algorithm based on graph model, the proposed algorithm based on correlation rule, the proposed algorithm based on hidden semantic model, Score in predicting algorithm based on neighborhood etc..Because various algorithms have the advantage and disadvantage of its own property decision, thus suitable for not Same environment and scene.However, in general, proposed algorithm is still a problem for needing to be further spread out research.
Through retrieval, currently without the explanation or report found with similar techniques of the present invention, domestic and international class is also not yet collected into As data.
The content of the invention
The present invention is directed to the deficiencies in the prior art, and problem to be solved is that hidden semantic model is commented in medical resource Divide and the application effect in TopN recommendations, it is proposed that a kind of medical treatment resource scoring based on hidden semantic model is with recommending method.This Invention is improved in the sequence link of classical enigmatic language justice model training algorithm first, is added on the basis of former enigmatic language justice algorithm The hidden Coefficients of class correlation factor is entered.Hidden semantic model after improvement proposed by the present invention is recommended by score in predicting and TopN can be with The recall rate under every kind of recommendation list length is improved, and the article that can to shoot straight moves forward in lists, is grown in list Spend it is limited in the case of, improve recommendation effect.
To achieve the above object, the present invention is achieved by the following technical solutions.
A kind of medical treatment resource scoring based on hidden semantic model comprises the following steps with recommending method:
The first step, in medical big data, obtain and recommend resource item data, and carry out data filtering and cleaning, form money Source item set;
Second step, using the parameters in the learning automaton training SVD++ models of continuous action;
3rd step, based on hidden semantic model, user-resource items rating matrix is had according to the SVD++ models Central Plains after training R, score in predicting value of the user to all resource items in resource items set is calculated respectively, it is pre- to obtain user-resource item rating Measured value matrix R ';
4th step, according to the user obtained in the 3rd step-resource items score in predicting value matrix R ', obtain TopN after sequence and push away Recommend list;
Wherein,
The SVD++ models are specially: Including following parameter:
Matrix P, for having weighed preference of the user for each hidden class, wherein, PuFor the parameter in matrix P, table Show and represent preference coefficients of the user u to hidden class;
Matrix Q, resource items and the degree of correlation of each hidden class are represented, wherein,For the parameter in matrix Q, resource is represented Coefficient correlation between item i and hidden class;
Matrix Y, the factor of influence for resource items in user's history record to hidden class, wherein, yJ, fFor the parameter in matrix Y, Represent that j-th of resource items is to the factor of influence of f-th of hidden class in user's history record;
Vectorial bu, biased as user, weigh user u scoring benchmark;
Vectorial bi, biased as resource items, weigh resource items i scoring benchmark;
M, as integral biased, weigh the scoring benchmark of whole score-system;
N (u), represent user u historical viewings or the resource items list of purchase.
Preferably, the second step specifically comprises the following steps:
Step 1, it is matrix P, matrix Q, matrix Y and vectorial bu, vectorial biIn independent continuous of each parameter configuration one Action learning automatic machine, respective (μ, the σ) parameter pair of each parameter is safeguarded, form training set;Wherein, μ is to become in each parameter The average of secondary element, σ are the variance of variable element in each parameter;
Step 2, by iterating to calculate Filled function parameter, in each iteration, any row record in training set is read;
Step 3, using corresponding square in the renewal changing ideas training set any row record of continuous action learning automaton Battle array Pu, matrix Qi, matrix Yi, vectorial buWith vectorial biParameter, model training is completed after iteration for several times.
Preferably, the renewal thought of the continuous action learning automaton is specially:Continuous action learning automaton Candidate actions set is number axis the preceding paragraph successive value, and its probability density function is Gaussian Profile, and its mean μ and variances sigma are all It can change with different feed back of environment in each iteration.
Preferably, the 3rd step is specially:
All resource items in enigmatic language justice model hypothesis resource items set are divided into F hidden classes, and all resource items can be distinguished It is grouped into this F hidden classes;
Original user-resource items rating matrix R is resolved into matrix P and matrix Q product;
After carrying out singular value decomposition to matrix P and matrix Q, matrix P ' and matrix Q ' are obtained, makes matrix P ' and matrix Q ' phases Multiply, obtain restructuring matrix user-resource items score in predicting value matrix R ', scoring of the user to all resource items is predicted with this.
Preferably, the 4th step is specially:
In the middle taking-ups of user-resource items score in predicting value matrix R ' and the hidden class f of user's degree of correlation highest, as arranging again The foundation of sequence;
Using hidden class f as calibration coefficients, all resource items are resequenced according to hidden class f coefficient correlation, as last Recommendation list improve, formed TopN recommendation lists.
Preferably, the 4th step includes as follows:
Step I ', willArranged according to descending, resource number corresponding to TopN scoring before taking-up, wherein,For score in predicting value;
Step II ', for active user u, traverse user u matrix P corresponds to row PuIn each element, find out maximum Value Pu max, and obtain Pu maxCorresponding hidden class-mark kmax
Step III ', for each resource items i in TopN, take out resource items i and correspond to kthmaxThe phase relation of individual hidden class NumberTake out calibration coefficients b corresponding to resource items i simultaneouslyi
Step IV ', calculate each resource items i and user u degree of correlation coefficientAnd by TopN In resource according to CI, uDescending rearrange, as consequently recommended order.
Compared with prior art, the invention has the advantages that:
1st, the present invention declines training algorithm resource item rating number is less, scoring based on the original gradient of hidden semantic model In the sparse data set of matrix, the deficiency that test error rises, prediction accuracy declines be present, it is proposed that CALA-TM models Training algorithm, the learning automaton for introducing continuous action replace original gradient to decline training algorithm progress model training, and should Use in score in predicting, in practice it has proved that CALA-TM models remain to converge to relatively low in the case where user's rating matrix is sparse Test error, compensate for original gradient and decline the deficiency that training algorithm prediction accuracy declines.
2nd, the present invention declines training algorithm each in the case of advantage, by two kinds considering CALA and original gradient Algorithm is combined, and furthermore present LAGD-TM model training algorithms, and optimal initial value point is found first by learning automaton, Then decline training algorithm using gradient and carry out iteratively faster continuation Optimal Parameters, in practice it has proved that LAGD-TM algorithms can enter one Step reduces test error, while reduces the model training time compared to CALA-TM algorithms.
3rd, the present invention realizes application of the hidden semantic model in TopN recommendations, the best LAGD-TM of combined training effect Model training algorithm, and improvement is proposed to the sequence link of former proposed algorithm (gradient decline training algorithm), by adding Hidden class correlation factors reorder to former recommendation list, generally improve the recall rate under every kind of recommendation list length, And the resource items for shoot straight move forward in lists, the ratio that Top10 and Top20 recommends hit is added, is grown in list Spend it is limited in the case of, improve recommendation effect.
4th, the present invention using predict appraisal result and to the degree of correlation of most related hidden class all as generate recommendation list according to According to, while reference is also made to the calibration coefficients of resource items in itself, it is ensured that the resource items of recommendation are more likely to cause the emerging of patient Interest, while it is also ensured that resource items have sufficiently high scoring to go to obtain liking for patient.
Embodiment
Embodiments of the invention are elaborated below:The present embodiment is carried out lower premised on technical solution of the present invention Implement, give detailed embodiment and specific operating process.It should be pointed out that to one of ordinary skill in the art For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention Protect scope.
Present embodiments provide a kind of medical treatment resource scoring based on hidden semantic model and recommendation method, including following step Suddenly:
The first step, in medical big data, obtain and recommend resource item data, and carry out data filtering;
Second step, the parameters in SVD++ models are trained using innovatory algorithm;
3rd step, for user, to all resource items in set, the user is calculated respectively using new algorithm to each money The score in predicting value of source item;
4th step, TopN recommendation lists are obtained using the sort algorithm after improvement.
Specially:
All resource items of enigmatic language justice model hypothesis (article) can be divided into F hidden classes (potential classification), all resources Item can be grouped into this F hidden classes respectively.Original user-resource items rating matrix R is resolved into matrix P and square by new algorithm Battle array Q product, wherein matrix P have weighed preference of the user for each hidden class, and matrix Q represents resource items and each hidden class Degree of correlation.Matrix P after singular value decomposition is multiplied with matrix Q, obtains restructuring matrix R ', user is predicted with this Scoring to all resource items.Due to containing the resource items in all set in rating matrix R, and each user may be only right The resource items of wherein only a few have scoring, so matrix R is a sparse matrix, matrix R is often one large-scale under actual environment Sparse matrix, now problem translated into the singular value decomposition of Large sparse matrix.
In a practical situation, scoring datum line of each different user to same asset item is different, and each resource items obtain The datum line of scoring is also different, and (such as resource items of the most people to high score even if some user does not like, will not also be given Too low fraction), and different types of resource item rating datum line is also different, therefore parameter m is introduced as integral biased, measurement The scoring benchmark of whole score-system;Introduce vectorial buBiased as user, weigh user u scoring benchmark;Introduce vectorial biMake Biased for resource items, weigh resource items i scoring benchmark.
The present embodiment Improvement training algorithm based on SVD++ models, the gradient based on classics decline training algorithm The characteristics of and deficiency, on this basis, it is proposed that the training algorithm based on learning automaton.Learning automaton has anti-noise acoustic energy The advantages of power is strong, global convergence performance is high, wherein, compared to the learning automaton of discrete movement, the study of continuous action is automatic Machine (CALA) is more suitable in continuously spatially majorized function.The medical treatment resource based on hidden semantic model that the present embodiment proposes is commented Divide with recommending method, be a kind of enigmatic language justice model training innovatory algorithm based on continuous action learning automaton.Therefore, second step Parameters (CALA-TM model trainings algorithm) in training SVD++ models specifically comprise the following steps:
Step 1, it is matrix P, matrix Q, matrix Y and vectorial bu, vectorial biIn each parameter configuration one it is independent CALA, respective (μ, σ) parameter pair is safeguarded, form training set;Wherein, μ is the average of variable element in each parameter, and σ is every The variance of variable element in individual parameter;
Step 2, parameter is continued to optimize by iterative calculation, in each iteration, reads a line record in training set;It is right In some user u and resource items i, obtain user-resource items score in predicting value matrix R ', and with original user-resource item rating Matrix R relatively obtains error eU, i
Step 3, matrix P is recorded using any row in training set corresponding to CALA renewal changing ideasu, matrix Qi, square Battle array Yi, vectorial buWith vectorial biIn parameter, by certain number iteration complete model training;Wherein, the renewal of the CALA The candidate actions set that thought is specially CALA is number axis the preceding paragraph successive value, and its probability density function is Gaussian Profile, Its mean μ and variances sigma can all change with different feed back of environment in each iteration.
Further, in the step 1, concretely comprise the following steps:
Step 1.1, each group of matrix P element of random initializtion:μp(u, k), σp(u, k), matrix Q elements:μq(i, k), σq (i, k) and matrix Y element:μy(i, k), σy(i, k), wherein, matrix P and matrix Q μ values initial value are between (0, max_ Rating/sqrt (F)) between random number, max_rating is the peak of prediction scoring, and F is the number of hidden class, matrix Y μ be initialized as 0;σ initial value is uniformly set to 1/sqrt (F);I is resource item number, and k is hidden class number, and u is number of users;
Step 1.2, b is initializeduVector element (μbu(u), σbu) and b (u)iVector element (μbi(i), σbi(i)), wherein, μ is uniformly initialized as max_rating/4, and σ is uniformly arranged to max_rating;
Further, in the step 2, concretely comprise the following steps:
Step 2.1, for some user u and resource items i, according to the probability distribution of each parameter, be randomly derived p (u, k), Q (i, k), buAnd b (u)i(i) value, for all neighborhood article j ∈ N (u), y (j, k) value, wherein k ∈ are randomly derived [1, F], y (j, k) are matrix Y parameter, and p (u, k) is matrix P parameters, and q (i, k) is matrix Q parameters, bu(u) biased for user, bi (i) biased for resource items;
Step 2.2, prediction score value is calculated using the sample point x and mean μ obtained at random respectively
Wherein:For sample point x prediction score value,Score value, μ are predicted for mean μy(j, k) is average μ calculates middle entry;
Step 2.3, using true score value and prediction score value computing environment feedback β:
Wherein, βxThe environmental feedback calculated for prediction score value, βμThe environmental feedback calculated for true score value;
Step 2.4, according to the mean μ and variances sigma of each parameter of CAlA Policy Updates:
Wherein φ (σ)=σl;forσ≤σl
φ (σ)=σ;For σ > σl
Parameter C is a constant positive number, parameter σlMinimum lower limit as σ is used for preventing from converging to non-optimal point, λ ginsengs Number is learning rate, and its size needs to determine by testing,For σ value functions.
Further, in the step 3, concretely comprise the following steps:
Step 3.1, iteration is continued cycling through, until βμValue converges to sufficiently small.For single CALA, with iterations Increase, μ has a very big convergence in probability (point obtains optimal environmental feedback) at optimum point, and σ can converge to it is minimum Lower limit σl.Above-mentioned model, in order to carry out score in predicting, it is necessary to obtain scoring in advance using formula (3-8) after the completion of model training Measured value.For above-mentioned CALA-TM algorithms, because each parameter is trained using independent CALA, therefore its is iterative Form be all identical, and in classical GD-TM algorithms (gradient decline training algorithm), because y (i, k) parameter is added according to i Sum, it is also required to carry out in its iteration recurrence formula once to add and one-level complexity is increased in total algorithm, still The problem is not present in CALA-TM algorithms.It is automatic compared to the optimal way that gradient declines training algorithm straight line descending manner, study The iterative process of machine is screw type, due to the randomness of the uncertain and selection action of environmental feedback, error in an iterative process Can't straight line decline, there may come a time when to go up, but in condition compared with harsh environment, this iterative manner can be avoided effectively Off-target value and be absorbed in local optimum, this advantage of learning automaton just compensate for the deficiency of gradient descent algorithm.It is real Proof is trampled in the case where scoring number is less, the constringency performance of CALA-TM training algorithms is better than classical GD-TM training and calculated Method.
Preferably, the 3rd step associative learning automatic machine and the respective advantage of gradient descent algorithm, give LAGD-TM Model training algorithm.Optimum value can be accurately converged in view of CALA-TM algorithms, and classical GD-TM algorithms can be fast The low training error of prompt drop, so first find best base on schedule using the learning automaton of continuous action in LAGD-TM algorithms, Gradient descent algorithm is recycled to carry out iteratively faster convergence.
LAGD-TM algorithms comprise the following steps that (implication of wherein each parameter is same as above):
Step 1, P, Q, Y matrix and b are initialized according to CALA-TM algorithmic rulesu、biEach variable element is equal in vector Value μ and variances sigma;
Step 2, setting iterations are N, are iterated according to CALA-TM algorithmic rules and seek ginseng;
Step 3, matrix P, matrix Q, matrix Y and vectorial b are setu, vectorial biIn the final of each variable element converge to Initial value of the average as next step algorithm (step 4 and step 5);
Step 4, reset learning rate α;
Step 5, setting iterations are M, are iterated according to classical GD-TM algorithmic rules and seek ginseng;Wherein N's and M takes Value needs to determine optimum value by testing repeatedly.
For score in predicting application, after the completion of above-mentioned model training, can equally be entered using preference pattern (3-8) formula Row score in predicting.
By above-mentioned arthmetic statement, in LAGD-TM algorithms, CALA-TM algorithms are employed first and find best base On schedule, when M values are larger, for the learning automaton of each parameter, substantially close to convergence, can avoid connecing Local optimum is absorbed in the training got off herein, and algorithm is further iterated using classical GD-TM algorithms, can to instruct Practice error on this basis further to decline, so as to improve final test error.Further, since the speed of classical GD-TM algorithms Degree is also faster than CALA-TM, therefore LAGD-TM is also better than CALA-TM algorithms on the training time.
Preferably, in above-mentioned 4th step based on TopN typical case's proposed algorithm SVD++ of hidden semantic model by four processes group Into being model determination, model training, score in predicting and sequence respectively.The present embodiment recommends method to make with the TopN based on SVD++ For the starting point of research, the performance that hidden semantic model TopN recommends each link is analyzed.TopN based on hidden semantic model recommends to calculate The committed step of method is as follows:
Step I, use the parameters in LAGD-TM Algorithm for Training SVD++ models;
Step II, for user u, to all items i ∈ I in set, the user is calculated respectively to every using formula (3-9) The score in predicting value of individual article
Step III, TopN recommendation lists are obtained using the sort algorithm after improvement.
By above step description it can be found that the model training link and last sequence link that TopN recommends all can be right Last recommendation results have an impact.For above-mentioned algorithm steps, it is contemplated that LAGD-TM model training algorithms have preferably property Can, therefore hidden semantic model is trained using LAGD-TM algorithms in step 1, illustrate to change sequence in step 3 below Enter.
Preferably, in above-mentioned 3rd step for the link that sorts, as it was previously stated, TopN proposed algorithms are by the pre- of models fitting Test and appraisal divide RuiAs unique sequence index, its limitation has so been done, and in general, a user always has its preference Type of items, also just have coefficient correlation pufThe hidden class f of highest one, and with the hidden Coefficients of class correlation QifRelatively high resource Item i will be more likely to be paid close attention to by user, meanwhile, sufficiently high prediction scoring RuiAnd ensure that resource items obtain the weight that user likes Want factor, it is therefore desirable to which coefficient correlation and prediction two aspect factors of scoring are combined into consideration.Analyzed based on more than, this implementation Example gives a kind of improved sort algorithm RCSM, the algorithm taken out with the user hidden class f of u degree of correlation highests, as again The foundation of sequence, all items are according to the coefficient correlation Q with the hidden classifRearrangement, as last recommendation list.
TopN after improvement recommends comprising the following steps that for sort algorithm RCSM:
Step I ', willArranged according to descending, Item Number corresponding to TopN scoring before taking-up;
Step II ', for active user u, travel through its matrix P and correspond to row PuIn each element, find out maximum Pu max, and obtain hidden class-mark k corresponding to itmax
Step III ', for each resource items i in TopN, take out its corresponding kthmaxThe coefficient correlation of individual hidden classTake out calibration coefficients b corresponding to resource items i simultaneouslyi
Step IV ', calculate each resource items i and user u degree of correlation coefficientAnd by TopN In article according to CI, uDescending rearrange, as consequently recommended order.
Arthmetic statement more than, algorithm after improvement is by prediction appraisal result and correlation to most related hidden class Foundation of the degree all as generation recommendation list, while reference is also made to the calibration coefficients b of resource items in itselfi, it is ensured that recommend Resource items be more likely to cause the interest of user, while it is also ensured that resource items have sufficiently high scoring to go to obtain user's Like.
In the present embodiment:
Still have some limitations because gradient declines training algorithm, when sample matrix is sparse, more likely fall into Enter local optimum, the innovatory algorithm that the present embodiment proposes has that noise resisting ability is strong, global convergence performance based on learning automaton The advantages of high, wherein, compared to the learning automaton of discrete movement, the learning automaton of continuous action is more suitable for continuous empty Between upper majorized function;
Innovatory algorithm improves to some extent in prediction accuracy, but due to algorithm self character determine its iteration speed compared with Slowly, the model training time is longer.But for commending system, that is, offline recommendation is used in, improves the training of model as far as possible Time is also valuable, so angle of the present embodiment from training for promotion speed, takes into account the requirement of the lifting degree of accuracy, it is right Training algorithm is further improved, and it is applied into score in predicting;
Sort algorithm after improvement taken out with the hidden class of user's degree of correlation highest, as the foundation of rearrangement, own Resource items are resequenced according to the coefficient correlation of the hidden class, are improved as last recommendation list.Algorithm after improvement will be pre- Survey appraisal result and to the degree of correlation of most related hidden class all as generating the foundation of recommendation list, while reference is also made to resource items The calibration coefficients of itself, it is ensured that the resource items of recommendation are more likely to cause the interest of user, while it is also ensured that resource Item has sufficiently high scoring to go to obtain liking for user.
The specific embodiment of the present invention is described above.It is to be appreciated that the invention is not limited in above-mentioned Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow Ring the substantive content of the present invention.

Claims (4)

1. a kind of medical treatment resource scoring based on hidden semantic model is with recommending method, it is characterised in that comprises the following steps:
The first step, in medical big data, obtain and recommend resource item data, and carry out data filtering and cleaning, form resource items Set;
Second step, using the parameters in the learning automaton training SVD++ models of continuous action;
3rd step, based on hidden semantic model, user-resource items rating matrix R is had according to the SVD++ models Central Plains after training, point Score in predicting value of the user to all resource items in resource items set is not calculated, obtains user-resource items score in predicting value Matrix R';
4th step, according to the user obtained in the 3rd step-resource items score in predicting value matrix R', TopN is obtained after sequence and recommends row Table;
Wherein,
The SVD++ models preference (u, i) is specially:
Including following parameter:
Matrix P, for having weighed preference of the user for each hidden class, wherein, PuFor the parameter in matrix P, user is represented Preference coefficients of the u to hidden class;
Matrix Q, resource items and the degree of correlation of each hidden class are represented, wherein,For the parameter in matrix Q, represent resource items i with Coefficient correlation between hidden class;
Matrix Y, the factor of influence for resource items in user's history record to hidden class, wherein, yj,fFor the parameter in matrix Y, represent Factor of influence of j-th of resource items to f-th of hidden class in user's history record;
Vectorial bu, biased as user, weigh user u scoring benchmark;
Vectorial bi, biased as resource items, weigh resource items i scoring benchmark;
M, as integral biased, weigh the scoring benchmark of whole score-system;
N (u), represent user u historical viewings or the resource items list of purchase;
4th step is specially:
Taking-up and the hidden class f of user's degree of correlation highest in user-resource items score in predicting value matrix R', as rearrangement Foundation;
Using hidden class f as calibration coefficients, all resource items are resequenced according to hidden class f coefficient correlation, are pushed away as last List improvement is recommended, forms TopN recommendation lists;
4th step comprises the following steps:
Step I ', willArranged according to descending, resource number corresponding to TopN scoring before taking-up, wherein,To comment Divide predicted value;
Step II ', for active user u, traverse user u matrix P corresponds to row PuIn each element, find out maximum Pumax, and obtain PumaxCorresponding hidden class-mark kmax
Step III ', for each resource items i in TopN, take out resource items i and correspond to kthmaxThe coefficient correlation of individual hidden classTake out calibration coefficients b corresponding to resource items i simultaneouslyi
Step IV ', calculate each resource items i and user u degree of correlation coefficientAnd by TopN Resource is according to Ci,uDescending rearrange, as consequently recommended order.
2. the medical treatment resource scoring according to claim 1 based on hidden semantic model is with recommending method, it is characterised in that institute Second step is stated to specifically comprise the following steps:
Step 1, it is matrix P, matrix Q, matrix Y and vectorial bu, vectorial biIn the one independent continuous action of each parameter configuration Learning automaton, respective (μ, the σ) parameter pair of each parameter is safeguarded, form training set;Wherein, μ is variable member in each parameter The average of element, σ are the variance of variable element in each parameter;
Step 2, by iterating to calculate Filled function parameter, in each iteration, any row record in training set is read;
Step 3, using corresponding matrix P in the renewal changing ideas training set any row record of continuous action learning automatonu、 Matrix Qi, matrix Yi, vectorial buWith vectorial biParameter, model training is completed after iteration for several times.
3. the medical treatment resource scoring according to claim 2 based on hidden semantic model is with recommending method, it is characterised in that institute The renewal thought of continuous action learning automaton is stated specifically, the candidate actions set of continuous action learning automaton is on number axis One section of successive value, and its probability density function is Gaussian Profile, its mean μ and variances sigma all can be with environment in each iteration Different feedbacks and change.
4. the medical treatment resource scoring according to claim 1 based on hidden semantic model is with recommending method, it is characterised in that institute Stating the 3rd step is specially:
All resource items in enigmatic language justice model hypothesis resource items set are divided into F hidden classes, and all resource items can be grouped into respectively In this F hidden classes;
Original user-resource items rating matrix R is resolved into matrix P and matrix Q product;
After carrying out singular value decomposition to matrix P and matrix Q, matrix P' and matrix Q' are obtained, matrix P' is multiplied with matrix Q', obtains To restructuring matrix user-resource items score in predicting value matrix R', scoring of the user to all resource items is predicted with this.
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