CN110991601B - Neural network recommendation method based on multi-user behavior - Google Patents
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Abstract
The invention provides a neural network recommendation method based on multi-user behaviors, which comprises the following steps: s1, mining the relevance between non-target behaviors and target behaviors, and obtaining the confidence coefficient from the non-target behaviors to the target behaviors; s2, a neural network recommendation model based on multiple behaviors of the user is used for mining the relation between the multiple behaviors and the user preferences, wherein the relation comprises the steps of constructing a behavior feature matrix of the user and the object, applying a neural collaborative filtering model, and solving the model by using a behavior classification learning strategy based on the confidence coefficient obtained in the S1 to obtain an object recommendation list. According to the neural network recommendation method based on multi-user behaviors, firstly, user behavior data are divided according to time periods, occurrence probability from non-target behaviors to target behaviors is calculated, behavior feature matrixes of users and articles are constructed according to interactive behaviors of the users and the articles, then a neural collaborative filtering model is utilized to represent complex relations between behaviors and user favorites, and finally, a model is solved by using a strategy of behavior classification learning to obtain a recommendation list.
Description
Technical Field
The invention relates to the field of recommendation systems, in particular to a neural network recommendation method based on multi-user behaviors.
Background
There are two main types of recommendation methods based on multi-user behavior at present. One is a method of using joint matrix decomposition, in which a user is represented as a plurality of feature matrices according to different behaviors, and interactions between the user and an article are predicted by performing inner products with the feature matrices of the article. One is a method using bayesian personalized ordering, which orders items according to the relative importance of the interaction behavior of a user with a specific item, and generates a recommendation list. The main problem with the first type of approach is that it represents a linear relationship between user behavior and preferences, and it is difficult to abstract more complex nonlinear relationships. The second type of method has the main problem that the relative importance degree of interaction behavior needs to be set manually, and large errors exist.
In recent years, the development of the machine learning field provides a guide for a recommendation system. The neural network can effectively acquire nonlinear relations, a model of a recommendation method can be designed by using the neural network, and deeper and complex relations between user behaviors and preferences can be abstracted. Furthermore, there is a certain correlation between the various behaviors of the user, and the degree of this correlation may change over time, that is, the importance of a particular behavior to reflect its preferences may change. To fully account for this dynamic change, a Markov model is used in our algorithm to capture the dynamic change in the correlation between behaviors and to learn the model using a strategy of behavior classification learning as one of the parameters of the neural network model.
Disclosure of Invention
The recommendation method based on multiple users in the prior art has the following common problems: it is difficult to abstract the complex nonlinear relationship between user behavior and preference, and there is a large error in the recommendation result.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the invention discloses a neural network recommendation system based on multi-user behaviors, which comprises a behavior association algorithm and a neural network recommendation model based on the multi-user behaviors;
the behavior association algorithm is used for mining the association between the target behavior and obtaining the confidence coefficient from the non-target behavior to the target behavior;
the neural network recommendation model based on the user multi-behavior is used for mining the relation between the multi-behavior and the user preference, wherein a behavior feature matrix of the user and the object is constructed, a neural collaborative filtering model is applied, the model is solved by using a behavior classification learning strategy according to the confidence coefficient obtained by a behavior association algorithm, and finally an object recommendation list is obtained.
The invention discloses a neural network recommendation method based on multi-user behaviors, which comprises the following steps:
s1, mining the relevance of non-target behaviors and target behaviors by using a behavior relevance analysis algorithm, and obtaining the confidence coefficient from the non-target behaviors to the target behaviors;
s2, a neural network recommendation model based on multiple behaviors of the user is used for mining the relation between the multiple behaviors and the user preferences, wherein the relation comprises the steps of constructing a behavior feature matrix of the user and the object, applying a neural collaborative filtering model, and solving the model by using a behavior classification learning strategy based on the confidence coefficient obtained in the S1 to obtain an object recommendation list.
In a preferred embodiment, S1 includes the following steps:
s11, using U to represent a user set, I to represent an article set, B to represent a non-target behavior set, O to represent a target behavior set, and using behavior data of the user UThe set is denoted as D u Each set of data in the dataset represents an action that the user has taken place on item i; wherein non-target behavior b j And target behavior o w The relationship between is expressed as an association rule R: b j →o w The confidence of this association is denoted as c (b j →o w ) For representation at b j In the case of occurrence, by association rule "b j →o w "occurrence of push out o w Probability of (2);
s12, association rule b j →o w Confidence sequence C of (2) jw Has the following expression form:
C jw ＝{c(b j →o w ) 1 ,c(b j →o w ) 2 ,…c(b j →o w ) t }
wherein b j For j-th non-target behavior, o w For the w-th target behavior, t is the current time period, c (b j →o w ) t For association rule b j →o w At data subset D ut (
Is for data set D u Subsets of data divided by time period).
S13, the confidence coefficient is in the range of 0,1]The interval can be divided into n subintervals (n is a super parameter), and n integers are respectively corresponding to the interval to represent states; obtaining a confidence state sequence S according to the confidence sequence and the conversion rules jw ：
S jw ＝{s(b j →o w ) 1 ,s(b j →o w ) 2 ,…s(b j →o w ) t }；
Wherein b j For j-th non-target behavior, o w For the w-th target behavior, t is the current time period, s (b j →o w ) t For association rule b j →o w At data subset D ut (
Is for data set D u Subsets of data divided by time period).
S14, calculating an association rule b by using the confidence state sequence j →o w The transition probability p of each state to any other state xy ＝p(s x →s y ) j Form a state transition matrix P jw ：
S15, according to the confidence state sequence obtained in S13, the final state is S (b j →o w ) t The state vector is represented by single hot code
The predicted next state vector is:
S16, letting index be
Subscript corresponding to maximum value in (b), confidence state s (b) of time period t j →o w ) t+1 Index, i.e. confidence of next state c (b j →o w ) t+1 ＝s(b j →o w ) t+1 Corresponds to the median value of the confidence interval.
In a preferred embodiment, the specific steps at S2 are as follows:
s21, encoding IDs of the user and the article respectively by using independent thermal encoding according to a characteristic matrix P of the user and a characteristic matrix Q of the article, and constructing a behavior characteristic matrix according to the multi-behavior characteristics of the user;
s22, inputting the behavior feature matrix obtained in the step S21, and using a neural collaborative filtering algorithm to represent a learning function of the behavior;
s23, performing independent learning on non-target behaviors and performing multi-behavior learning strategies of target behavior joint learning, and generating a recommendation list for a user according to the result of the joint learning.
In a preferred embodiment, the specific step S21 is as follows:
s211, generating a user characteristic matrix P and an article characteristic matrix Q, and respectively encoding the IDs of the user and the article by using single thermal encoding to obtain M×E and N×E matrixes;
s212, adding K features to the M×E user feature matrix, wherein the K features correspond to the occurrence times of K behaviors of the user; similarly, K features are added for the N x E article feature matrix, the number of times of occurrence of K behaviors related to the article is respectively corresponding to the number of times of occurrence of K behaviors related to the article, and the sizes of the constructed user feature matrix P and the article feature matrix Q are M x (E+K) and N x (E+K) respectively;
S213.
and->
The unique heat encoded vectors representing user u and item i, respectively, may be obtained using the following formulas, namely, the ith row of the matrix P and the ith row of Q:
in a preferred embodiment, the output of the neural network in S22 is
Wherein the method comprises the steps of
For a functional representation with respect to feature vectors, σ is a sigmoid function that converts the output into probabilities.
In a preferred embodiment, the
The method comprises 3 commonly used function expression methods, namely a general matrix decomposition method (GMF), a multi-layer perceptron Method (MLP) and a neural matrix decomposition method (NeuMF), and the three methods are described as follows;
the general matrix factorization method (GMF) is to add weights to each term on the result of matrix factorization:
where h represents a weight vector.
The multi-layer perceptron Method (MLP) learns by a nonlinear method:
…
z L ＝ReLU(W L z L-1 +b L )
wherein L represents the number of hidden layers in the multi-layer perceptron, W x And b x The weight and bias items of the x layer are respectively expressed, and the linear rectification function (ReLU) is used as an activation function by default;
the neural matrix decomposition method (NeuMF) combines GMF and MLP according to a certain weight:
where h represents a weight vector, z L Is the result of the last layer of the hidden layer obtained through MLP learning.
In a preferred embodiment, the step S23 includes the following steps:
s231, independently learning non-target behaviors;
s2311 user-item is in non-target behavior b j Is represented as a matrix Y j ，Y j The size of (2) is M×N, M is the number of users, and N is the number of articles.
S2312 Each non-target behavior b j There is an independent prediction function and loss function. b j The prediction function of (2) is as follows:
wherein the method comprises the steps of
Is the article in action b j Bias item on->
Is a functional representation; />
S2313 for Single action b j Likelihood functions of (2) are as follows:
wherein Y is j + Representing a behavior matrix Y j Item 1 in (C), Y j - Representing a behavior matrix Y j An item of 0;
s2314 non-target behavior b j The loss function of (2) is as follows:
optimizing the loss function of each non-objective behavioral function independently using a stochastic gradient descent method;
s232, performing joint learning on target behaviors;
s2321, user and article are in target behavior o w Is represented as a matrix Z w ，Z w The size of (2) is M x N, M is the number of users, N is the number of articles,
s2322 for target behavior o w For that reason, consider non-target behavior and its relevance.
Wherein c jw For the confidence coefficient c (b) obtained in step S16 j →o w ) t+1 ，
Is the article in behavior o w The bias term on the upper part of the table,
is a functional representation;
s2323 for a single target behavior o w Likelihood functions of (2) are as follows:
wherein the method comprises the steps of
Representing a behavior matrix Z w Item 1 in>
Representing a behavior matrix Z w An item of 0;
s2324. the joint probability function of all non-target behaviors is expressed as:
s2325, taking the negative logarithm of the probability function, the following loss function can be obtained, namely the objective function to be optimized:
s.t.
wherein lambda is w For target action o w The importance of (2) can be obtained through priori knowledge;
s233, constructing a recommendation list according to the prediction results obtained in the S231 and the S232;
predicting the probability of target behavior occurrence according to the previous step
Weighting the likelihood of all target behaviors may reflect the preference of user u for item i: />
And after the R values of all the articles are ranked from large to small, recommending the articles ranked in front to the user.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the behavior association analysis algorithm provided by the invention is an association analysis algorithm based on a Markov model, the user behavior data is divided according to time periods, and the occurrence probability from non-target behavior to target behavior is calculated. In the multi-behavior neural network recommendation model, the interaction behaviors of the user and the article are considered, a behavior feature matrix of the user and the article is constructed, and the complex nonlinear relation between the user behavior and the preference is represented by utilizing neural collaborative filtering. Solving the model by using a strategy of behavior classification learning: and for the target behavior, the influence of the non-target behavior is considered, and the joint learning is performed.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
fig. 2 is a schematic diagram of a neural network recommendation model in example 2.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
The first aspect of the invention discloses a neural network recommendation system based on multi-user behaviors, as shown in fig. 1, comprising a behavior association algorithm and a neural network recommendation model based on the multi-user behaviors;
the behavior association algorithm is used for mining the association between the target behavior and obtaining the confidence coefficient from the non-target behavior to the target behavior;
the neural network recommendation model based on the user multi-behavior is used for mining the relation between the multi-behavior and the user preference, wherein a behavior feature matrix of the user and the object is constructed, a neural collaborative filtering model is applied, the model is solved by using a behavior classification learning strategy according to the confidence coefficient obtained by a behavior association algorithm, and finally an object recommendation list is obtained.
Example 2
The invention discloses a neural network recommendation method based on multi-user behaviors, which comprises the following steps:
s1, mining the relevance of non-target behaviors and target behaviors by using a behavior relevance analysis algorithm, and obtaining the confidence coefficient from the non-target behaviors to the target behaviors;
s2, as shown in FIG. 2, a neural network recommendation model based on multiple behaviors of a user is used for mining the relation between the multiple behaviors and user preferences, wherein the relation comprises the steps of constructing a behavior feature matrix of the user and the object, applying a neural collaborative filtering model, and solving the model by using a behavior classification learning strategy based on the confidence coefficient obtained in S1 to obtain an object recommendation list.
In a preferred embodiment, S1 includes the following steps:
s11, using U to represent a user set, I to represent an article set, B to represent a non-target behavior set, O to represent a target behavior set, and using the behavior data set of the user U as D u Each set of data in the dataset represents an action that the user has taken place on item i; wherein non-target behavior b j And target behavior o w The relationship between is expressed as an association rule R: b j →o w The confidence of this association is denoted as c (b j →o w ) For representation at b j In the case of occurrence, by association rule "b j →o w "occurrence of push out o w Probability of (2);
s12, association rule b j →o w Confidence sequence C of (2) jw Has the following expression form:
C jw ＝{c(b j →o w ) 1 ,c(b j →o w ) 2 ,…c(b j →o w ) t }
wherein b j For j-th non-target behavior, o w For the w-th target behavior, t is the current time period, c (b j →o w ) t For association rule b j →o w At data subset D ut (
Is for data set D u Subsets of data divided by time period).
S13, the confidence coefficient is in the range of 0,1]The interval can be divided into n subintervals (n is a super parameter), and n integers are respectively corresponding to the interval to represent states; obtaining a confidence state sequence S according to the confidence sequence and the conversion rules jw ：
S jw ＝{s(b j →o w ) 1 ,s(b j →o w ) 2 ,…s(b j →o w ) t }；
Wherein b j For j-th non-target behavior, o w For the w-th target behavior, t is the current time period, s (b j →o w ) t For association rule b j →o w At data subset D ut (
Is for data set D u Subsets of data divided by time period).
S14, calculating an association rule b by using the confidence state sequence j →o w The transition probability p of each state to any other state xy ＝p(s x →s y ) j Form a state transition matrix P jw ：
S15, according to the confidence state sequence obtained in S13, the final state is S (b j →o w ) t The state vector is represented by single hot code
The predicted next state vector is:
S16, letting index be
Subscript corresponding to maximum value in (b), confidence state s (b) of time period t j →o w ) t+1 Index, i.e. confidence of next state c (b j →o w ) t+1 ＝s(b j →o w ) t+1 Corresponds to the median value of the confidence interval.
In a preferred embodiment, the specific steps at S2 are as follows:
s21, encoding IDs of the user and the article respectively by using independent thermal encoding according to a characteristic matrix P of the user and a characteristic matrix Q of the article, and constructing a behavior characteristic matrix according to the multi-behavior characteristics of the user;
s22, inputting the behavior feature matrix obtained in the step S21, and using a neural collaborative filtering algorithm to represent a learning function of the behavior;
s23, performing independent learning on non-target behaviors and performing multi-behavior learning strategies of target behavior joint learning, and generating a recommendation list for a user according to the result of the joint learning.
In a preferred embodiment, the specific step S21 is as follows:
s211, generating a user characteristic matrix P and an article characteristic matrix Q, and respectively encoding the IDs of the user and the article by using single thermal encoding to obtain M×E and N×E matrixes;
s212, adding K features to the M×E user feature matrix, wherein the K features correspond to the occurrence times of K behaviors of the user; similarly, K features are added for the N x E article feature matrix, the number of times of occurrence of K behaviors related to the article is respectively corresponding to the number of times of occurrence of K behaviors related to the article, and the sizes of the constructed user feature matrix P and the article feature matrix Q are M x (E+K) and N x (E+K) respectively;
S213.
and->
The unique heat encoded vectors representing user u and item i, respectively, may be obtained using the following formulas, namely, the ith row of the matrix P and the ith row of Q:
in a preferred embodiment, the output of the neural network in S22 is
Wherein the method comprises the steps of
For a functional representation with respect to feature vectors, σ is a sigmoid function that converts the output into probabilities.
In a preferred embodiment, the
The method comprises 3 commonly used function expression methods, namely a general matrix decomposition method, a multi-layer perceptron method and a neural matrix decomposition method, and the three methods are briefly introduced as follows:
the general matrix decomposition method is to add weights to each term on the result of matrix decomposition:
where h represents a weight vector.
The multi-layer perceptron method is to learn by a nonlinear method:
…
z L ＝ReLU(W L z L-1 +b L )
wherein L represents the number of hidden layers in the multi-layer perceptron, W x And b x The weight and the bias item of the x layer are respectively expressed, and the linear rectification function is used as the activation function by default;
the neural matrix decomposition combines the GMF and the MLP according to a certain weight:
where h represents a weight vector, z L Is the result of the last layer of the hidden layer obtained through MLP learning.
In a preferred embodiment, the step S23 includes the following steps:
s231, independently learning non-target behaviors;
s2311 user-item is in non-target behavior b j Is represented as a matrix Y j ，Y j The size of (2) is M×N, M is the number of users, and N is the number of articles.
S2312 Each non-target behavior b j There is an independent prediction function and loss function. b j The prediction function of (2) is as follows:
wherein the method comprises the steps of
Is the article in action b j Bias item on->
Is a functional representation;
s2313 for Single action b j Likelihood functions of (2) are as follows:
wherein Y is j + Representing a behavior matrix Y j Item 1 in (C), Y j - Representing a behavior matrix Y j An item of 0;
s2314 non-target behavior b j The loss function of (2) is as follows:
optimizing the loss function of each non-objective behavioral function independently using a stochastic gradient descent method;
s232, performing joint learning on target behaviors;
s2321, user and article are in target behavior o w Is represented as a matrix Z w ，Z w The size of (2) is M x N, M is the number of users, N is the number of articles,
s2322 for target behavior o w For that reason, consider non-target behavior and its relevance.
Wherein c jw For the confidence coefficient c (b) obtained in step S16 j →o w ) t+1 ，
Is the article in behavior o w The bias term on the upper part of the table,
is a functional representation;
s2323 for a single target behavior o w Likelihood functions of (2) are as follows:
wherein the method comprises the steps of
Representing a behavior matrix Z w Item 1 in>
Representing a behavior matrix Z w An item of 0;
s2324. the joint probability function of all non-target behaviors is expressed as:
s2325, taking the negative logarithm of the probability function, the following loss function can be obtained, namely the objective function to be optimized:
s.t.
wherein lambda is w For target action o w The importance of (2) can be obtained through priori knowledge;
s233, constructing a recommendation list according to the prediction results obtained in the S231 and the S232;
predicting the probability of target behavior occurrence according to the previous step
Weighting the likelihood of all target behaviors may reflect the preference of user u for item i: />
And after the R values of all the articles are ranked from large to small, recommending the articles ranked in front to the user.
The terms describing the positional relationship in the drawings are merely illustrative, and are not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (4)
1. The neural network recommendation method based on multi-user behavior is characterized by comprising the following steps of:
s1, mining the relevance of non-target behaviors and target behaviors by using a behavior relevance analysis algorithm, and obtaining the confidence coefficient from the non-target behaviors to the target behaviors; the method comprises the following specific steps:
s11, using U to represent a user set, I to represent an article set, B to represent a non-target behavior set, O to represent a target behavior set, and using the behavior data set of the user U as D u Data setRepresenting the behaviour of the user on item i; wherein non-target behavior b j And target behavior o w The relationship between is expressed as an association rule R: b j →o w The confidence of this association is denoted as c (b j →o w ) For representation at b j In the case of occurrence, by association rule "b j →o w "occurrence of push out o w Probability of (2);
s12, association rule b j →o w Confidence sequence C of (2) jw Has the following expression form:
C jw ＝{c(b j →o w ) 1 ,c(b j →o w ) 2 ,…c(b j →o w ) t }
wherein b j For j-th non-target behavior, o w For the w-th target behavior, t is the current time period, c (b j →o w ) t For association rule b j →o w At data subset D ut Confidence in (c); wherein the method comprises the steps of
Is for data set D u Data subsets divided by time period;
s13, the confidence coefficient is in the range of 0,1]Dividing the interval into n subintervals, wherein n is a super parameter and corresponds to n integers respectively for representing states; obtaining a confidence state sequence S according to the confidence sequence and the conversion rules jw ：
S jw ＝{s(b j →o w ) 1 ,s(b j →o w ) 2 ,…s(b j →o w ) t }；
Wherein s (b) j →o w ) t For association rule b j →o w At data subset D ut Confidence state in (c);
s14, calculating an association rule b by using the confidence state sequence j →o w The transition probability p of each state to any other state xy ＝p(s x →s y ) j Form a state transition matrix P jw ：
S15, according to the confidence state sequence obtained in S13, the final state is S (b j →o w ) t The state vector is represented by single hot code
The predicted next state vector is:
S16, letting index be
Subscript corresponding to maximum value in (b), confidence state s (b) of time period t j →o w ) t+1 Index, i.e. confidence of next state c (b j →o w ) t+1 ＝s(b j →o w ) t+1 A median value of the corresponding confidence intervals;
s2, mining the relation between the multiple behaviors and the user preferences by using a neural network recommendation model based on the multiple behaviors of the user, wherein the method comprises the steps of constructing a behavior feature matrix of the user and the object, applying a neural collaborative filtering model, and solving the model by using a behavior classification learning strategy based on the confidence coefficient obtained in the S1 to obtain an object recommendation list; the method comprises the following specific steps:
s21, encoding IDs of the user and the article respectively by using independent thermal encoding according to a characteristic matrix P of the user and a characteristic matrix Q of the article, and constructing a behavior characteristic matrix according to the multi-behavior characteristics of the user; wherein:
s211, generating a user characteristic matrix P and an article characteristic matrix Q, and respectively encoding the IDs of the user and the article by using single thermal encoding to obtain M×E and N×E matrixes;
s212, adding K features to the M×E user feature matrix, wherein the K features correspond to the occurrence times of K behaviors of the user; similarly, K features are added for the N x E article feature matrix, the number of times of occurrence of K behaviors related to the article is respectively corresponding to the number of times of occurrence of K behaviors related to the article, and the sizes of the constructed user feature matrix P and the article feature matrix Q are M x (E+K) and N x (E+K) respectively;
S213.
and->
The unique heat coded vectors representing user u and item i, respectively, are used to obtain the corresponding feature vectors, i.e., the ith row of the matrix P and the ith row of Q, using the following formula: />
S22, inputting the behavior feature matrix obtained in the step S21, and using a neural collaborative filtering algorithm to represent a learning function of the behavior;
s23, performing independent learning on non-target behaviors and performing multi-behavior learning strategies of target behavior joint learning, and generating a recommendation list for a user according to the result of the joint learning; wherein:
s231, independently learning non-target behaviors;
s2311 user-item is in non-target behavior b j Is represented as a matrix Y j ，Y j The size of (2) is M multiplied by N, M is the number of users, and N is the number of articles;
s2312 Each non-target behavior b j All have independent prediction and loss functions, b j The prediction function of (2) is as follows:
wherein the method comprises the steps of
Is the article in action b j Bias item on->
Is a functional representation;
s2313 for Single action b j Likelihood functions of (2) are as follows:
wherein the method comprises the steps of
Representing a behavior matrix Y j Item 1 in>
Representing a behavior matrix Y j An item of 0;
s2314 non-target behavior b j The loss function of (2) is as follows:
optimizing the loss function of each non-objective behavioral function independently using a stochastic gradient descent method;
s232, performing joint learning on target behaviors;
s2321. To be usedUser and article in target behavior o w Is represented as a matrix Z w ，Z w The size of (2) is M x N, M is the number of users, N is the number of articles,
s2322 for target behavior o w For the sake of this, consider non-target behavior and its relevance;
wherein c jw For the confidence coefficient c (b) obtained in step S16 j →o w ) t+1 ，
Is the article in behavior o w Bias item on->
Is a functional representation;
s2323 for a single target behavior o w Likelihood functions of (2) are as follows:
wherein the method comprises the steps of
Representing a behavior matrix Z w Item 1 in>
Representing a behavior matrix Z w An item of 0;
s2324. the joint probability function of all non-target behaviors is expressed as:
s2325, taking the negative logarithm of the probability function, obtaining the following loss function, namely an objective function to be optimized:
s.t.
wherein lambda is w For target action o w The importance of (2) is obtained through priori knowledge;
s233, constructing a recommendation list according to the prediction results obtained in the S231 and the S232;
predicting the probability of target behavior occurrence according to the previous step
Weighting the likelihood of all target behaviors may reflect the preference of user u for item i:
and after the R values of all the articles are ranked from large to small, recommending the articles ranked in front to the user.
2. The neural network recommendation method based on multi-user behavior according to claim 1, wherein the output of the neural network in S22 is
Wherein the method comprises the steps of
For a functional representation with respect to feature vectors, σ is a sigmoid function that converts the output into probabilities.
3. The neural network recommendation method based on multi-user behavior according to claim 2, wherein the following steps are performed
Comprising 3 commonly used function representation methods, namely a general matrix decomposition method (GMF), a multi-layer perceptron Method (MLP) and a neural matrix decomposition method (NeuMF), which are described below,
the general matrix factorization method (GMF) is to add weights to each term on the result of matrix factorization:
wherein h represents a weight vector;
the multi-layer perceptron Method (MLP) learns by a nonlinear method:
…
z L ＝ReLU(W L z L-1 +b L )
wherein L represents the number of hidden layers in the multi-layer perceptron, W x And b x The weight and the bias item of the x layer are respectively expressed, and the linear rectification function is used as the activation function by default;
the neural matrix decomposition method (NeuMF) combines GMF and MLP according to a certain weight:
where h represents a weight vector, z L Is the result of the last layer of the hidden layer obtained through MLP learning.
4. A neural network recommendation system based on multi-user behaviors, which is applied to the neural network recommendation method based on multi-user behaviors as claimed in any one of claims 1 to 3, and is characterized by comprising a behavior association algorithm and a neural network recommendation model based on multi-user behaviors;
the behavior association algorithm is used for mining the association between the target behavior and obtaining the confidence coefficient from the non-target behavior to the target behavior;
the neural network recommendation model based on the user multi-behavior is used for mining the relation between the multi-behavior and the user preference, wherein a behavior feature matrix of the user and the object is constructed, a neural collaborative filtering model is applied, the model is solved by using a behavior classification learning strategy according to the confidence coefficient obtained by a behavior association algorithm, and finally an object recommendation list is obtained.
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