CN116522007A - Recommendation system model-oriented data forgetting learning method, device and medium - Google Patents
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Abstract
The invention discloses a data forgetting learning method, a device and a medium for a recommendation system model, wherein the data forgetting learning method comprises the following steps: firstly, obtaining a calculation function of a recommended system model for a sample under a training data set, outputting the calculation function as a prediction for the sample, training the model to be optimal on the training data set, and obtaining a model after erasing an unavailable data set through the optimal model and influence function estimation. According to the method, the training architecture, the model architecture and the deployment mode of the original model are not changed, the model parameters which are not important for data erasure are deleted, and the accuracy and the data erasure efficiency of data erasure based on the influence function are improved.
Description
Technical Field
The invention relates to the field of data processing systems or methods, in particular to a data forgetting learning method, device and medium for a recommendation system model.
Background
The recommendation system is a key basic tool for solving the information explosion problem in the current mobile internet era, really influences activities such as life, entertainment and travel of people, and is the most commonly used interaction medium between a service provider and a user. Recommendation systems typically infer user interests from historical interaction information of users, which must be remembered by a model deployed to contain parameters. However, a model also requires that some historical data be erased in some cases, such as users requiring deletion of their individual's historical information for privacy, and the system itself requires deletion of some information for attack purposes, etc. Such information to be erased is referred to as unusable data for convenience. It should be noted that these unusable data to be erased are not only erased from the database, but rather that it should be emphasized how to erase these unusable data from the parameters of the model.
In current recommendation systems, the erasure of unusable data is accomplished primarily in dependence upon retraining. The first is full retraining, i.e. training the recommendation system model from scratch to achieve the goal of data erasure without using unusable data, which is often faced with more time consuming, and the recommendation system is a real-time system, and therefore is not practical. The second type of retraining is partial retraining, which is a method in which data is divided into different independent parts at the time of initial training, different parts of data are used to train different sub-models, and when a request for erasing unusable data is received, only a small part of the sub-models need to be retrained, thereby increasing model retraining efficiency. However, this approach requires that the unavailable data only affects a small fraction of the sub-models, and this assumption also limits the practical application of the approach, since the distribution of the unavailable data is often unknown and unrestricted. In addition to the retraining method, some research works are to record gradient update information of the training process and then retrospectively cancel gradient update information of unavailable data to achieve the purpose of data erasure, however, the method ignores the interaction between different samples.
In a technical field outside the recommendation system, there are also research efforts to use the influence function (influence function) to achieve data erasure, but it cannot be directly applied to the recommendation system because it cannot evaluate the influence of erasure-unavailable data on other data calculation functions. In addition, direct application can also cause massive computational overhead problems. The invention improves the influence function, so that the influence caused by the change of the calculation function of other data caused by erasure of unavailable data can be measured, and the calculation acceleration is realized through a pruning scheme.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a data forgetting learning method, device and medium for a recommendation system model, which do not change the training architecture, model architecture and deployment mode of an original model, and improve the accuracy and the data erasing efficiency of erasing data based on an influence function.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a data forgetting learning method for a recommendation system model, where the data forgetting learning method includes the following steps:
s1, definitionIs a parameter of->The recommendation system model of the representation is +.>In training data set->The following calculation function, which is output as a prediction for the sample, and is +.>The optimal model ∈>Expressed as->Wherein->For the recommendation system model in the data set +.>The sum of the loss functions below, expressed as +.>，Representing data set +.>One of them, < >>Representing the user->Representing articles->Representing user +.>For articles->Is->Representing a loss function;
s2, in the data setThe unavailable data set is +.>The remaining data set is +.>The model after erasing the data is +.>Expressed as->Estimating ∈according to the influence function>The estimated result is recorded as +.>The resulting erase unavailable data set +.>The latter model is denoted->。
Further, different data sets are usedThe erasure-disabled data set is used in a different way for the input samples>The model calculation functions of (2) are also different.
Still further, the erasing of the unusable data setThe specific calculation process of the model calculation function of (a) is as follows:
(1) If data setUser item pair input in->Is a recommendation system model calculation functionWill leave the data set +.>All of the sample points satisfying the conditionRecorded as a calculation function change dataset +.>The recommendation system model is based on the dataset +.>And the remaining data setLower calculation data set +.>The difference between the loss functions of all sample points in (a) is +.>，/>Denoted as->；
(2) Computing unusable data setsIn dataset +.>The lower loss function->，Denoted as->；
(3) Based on the obtained、/>Defined as->Based on +.>Intensity plus->And->Optimal model->，/>Expressed as:wherein->Representing a disturbance term, deriving +.>，/>；
(4) Defining unusable data setsThe influence function for the recommendation system model is +.>，Expressed as: />Wherein->Representation pair->Conduct and->Expressed as a Hessian matrix,>an inverse matrix denoted as a Hessian matrix;
(5) Then based on the influence functionEstimate->Is that
Erase unavailable data set +.>The model after that is: />。
Furthermore, when the data forgetting learning method is used for data erasure, acceleration is realized by a pruning method, and model parameters which are not important for data erasure are deleted.
Furthermore, in the pruning method, the specific process of deleting the unimportant model parameters is as follows:
(1) Data setAll users and item sets in (1) are denoted +.>And (2) andfor each element thereinCalculating a set of elements with which statistics interact, denoted +.>And sets the maximum number of iterations K, for all elements +.>Initializing an importance score for it +.>Cutting ratio per iteration；
(2) Initializing an empty setTraversing data set +.>Is>Pairing user articlesAdded to->Execute any->Update->The method comprises the steps of carrying out a first treatment on the surface of the After the traversal is completed, updateFinally let->；
(3) If it isInitializing a null set->Let all->Traversing->For any->Update->And will->Added to->The method comprises the steps of carrying out a first treatment on the surface of the After traversal is completed, update->Let->；
(4) If it isContinuing to execute the step (3) to obtain +.> ，Representing the model parameters corresponding to v and returning +.>The method comprises the steps of carrying out a first treatment on the surface of the According to the->Will->The other parameters are marked as +.>Then there is，/>，/>For the model parameters important for erasing data +.>For model parameters that are not important for erasing data, the +.>Is only considered +.>Is a variation of (c).
Further, an influence function according to the recommendation system model
Will->Replaced by->And +.>Fix->Taking this as a constant, then ∈ ->Namely, the method is simplified as follows:
wherein->Erase unavailable data set +.>The model after that is:wherein->。
Further, if saidThe parameter number of (2) is->（/>) The computational complexity of the model update is reduced toOr->Wherein->For influence->Sample size of->For model parameters>Is the time complexity.
Further, the loss functionIs a binary cross entropy loss function.
In a second aspect, the present invention provides a data recommendation device, comprising a memory storing computer executable instructions and a processor configured to execute the computer executable instructions, the computer executable instructions implementing the data forgetting learning method when executed by the processor.
In a third aspect, the present invention provides a computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the data forgetting learning method.
Compared with the prior art, the invention has the following beneficial effects:
the method improves the accuracy of erasing data based on the influence function, and only considers the loss function when the influence of the traditional influence function calculation data on the modelItem, ignore->And->The prediction function (i.e., the calculation function) that measures other data is the affected part of the removed data, which is widely present in the recommender model, and only taking this affected part into account, complete erasure of the data can be achieved, thereby achieving the same target results as training the recommender model from scratch.
The invention improves the efficiency of erasing data. In one aspect, the present invention is directed to avoiding direct estimationThis quantity is then directly added to the original model to obtain a model of erasure unusable data, avoiding retraining, and in the event that erasure data is not too much, assisting in achieving faster erasure. On the other hand, the present invention proposes that the pruning method is accelerated by +.>Transition to->Further speeding up, deleting model parameters that are not important for data erasure.
The method is a post-processing method, does not need to change the model framework and training framework of the original model, only needs to obtain the gradient of the model and can access the model, can be conveniently and directly grafted to the deployed system model, and is beneficial to the wide application of the method.
Detailed Description
Example 1:
the embodiment discloses a data forgetting learning method facing a recommendation system model, which comprises the following steps:
s1, definitionIs a parameter of->The recommendation system model of the representation is +.>In training data set->The following calculation function, which is output as a prediction for the sample, and is +.>The optimal model ∈>Expressed as->Wherein->For the recommendation system model in the data set +.>The sum of the loss functions below, expressed as +.>，Representing data set +.>One of them, < >>Representing the user->Representing articles->Representing user +.>For articles->Is->Represents a loss function, loss function->The method specifically comprises the steps of binary cross entropy loss function;
s2, in the data setThe unavailable data set is +.>The remaining data set is +.>The model after erasing the data is +.>Expressed as->Estimating ∈according to the influence function>The estimated result is recorded as +.>The resulting erase unavailable data set +.>The latter model is denoted->。
When using different data setsIn the case of different calculation modes for the input samples, the erase unavailable data set is adopted>The model calculation functions of (2) are also different. Erase unusable data set +.>The specific calculation process of the model calculation function of (a) is as follows:
(1) If data setUser item pair input in->Is a recommendation system model calculation functionWill leave the data set +.>All of the sample points satisfying the conditionRecorded as a calculation function change dataset +.>The recommendation system model is based on the dataset +.>And the remaining data setLower calculation data set +.>The difference between the loss functions of all sample points in (a) is +.>，Denoted as->；
(2) Computing unusable data setsIn dataset +.>The lower loss function->，Denoted as->；
(3) Based on the obtained、/>Defined as->Based on the basis ofIntensity plus->And->Optimal model->，/>Expressed as:wherein->Representing a disturbance term, deriving +.>，/>；
(4) Defining unusable data setsThe influence function for the recommendation system model is +.>，Expressed as: />Wherein->Representation pair->Conduct and->Expressed as a Hessian matrix,>an inverse matrix denoted as a Hessian matrix;
(5) Then based on the influence functionEstimate->Is that
Erase unavailable data set +.>The model after that is: />。
In this embodiment, when the data forgetting learning method performs data erasure, acceleration is realized by a pruning method, and model parameters which are not important for data erasure are deleted. In the pruning method, the specific flow of deleting unimportant model parameters is as follows:
(1) Data setAll users and item sets in (1) are denoted +.>And for each element thereinCalculating a set of elements with which statistics interact, denoted +.>And sets the maximum number of iterations K, for all elements +.>Initializing an importance score for it +.>Cutting ratio per iteration；
(2) Initializing an empty setTraversing data set +.>Is>Pairing user articlesAdded to->Execute any->Update->The method comprises the steps of carrying out a first treatment on the surface of the TraversingAfter completion, updateFinally let->；
(3) If it isInitializing a null set->Let all->Traversing->For any->Update->And will->Added to->The method comprises the steps of carrying out a first treatment on the surface of the After traversal is completed, update->Let->；
(4) If it isContinuing to execute the step (3) to obtain +.> ，Representing the model parameters corresponding to v and returning +.>The method comprises the steps of carrying out a first treatment on the surface of the According to the->Will->The other parameters are marked as +.>Then there is，/>，/>For the model parameters important for erasing data +.>For model parameters that are not important for erasing data, the +.>Is only considered +.>Is a variation of (c).
Influence functions according to the recommendation system model
Will->Replaced by->And +.>Fix->Taking this as a constant, then ∈ ->Namely, the method is simplified as follows:
wherein->Erase unavailable data set +.>The model after that is:wherein->. If->The parameter number of (2) is->（/>) The computational complexity of the model update is reduced to +.>Or->Wherein->For influence->Sample size of->For model parameters>Is the time complexity.
In order to verify the effectiveness of the learning method disclosed in example 1 applied to the recommendation model system, the following experimental verification was performed using the real data set. The data set is a common public data set Amazon, and the division and the setting of the data set follow the common mode of data erasure study in a recommended model system to erase attack data in the data so as to realize better model performance. Experiments were performed based on two commonly used recommended models MF, LGCN, in contrast to the different data erasure methods Retrain, recEraser and SISA, etc., where Retrain is a method that is fully trained from scratch, which yields the model performance versus the erasure data that should yield performance. The experimental results are shown in table 1:
table 1 data erasure effects with different erasure methods
From the data erasing effects recorded in table 1, it can be found that the learning method disclosed in this embodiment is consistent with the standard result corresponding to strin, compared with other data erasing methods, and better results than those before erasing the data are obtained, so that it can be proved that the learning method of this embodiment can realize accurate data erasing.
In addition, efficiency experiments can be performed on the data set, and the time efficiency of the learning method and the Retrain method disclosed by the embodiment in erasing data with different ratios is tested respectively, and the experimental results are shown in table 2:
TABLE 2 time Performance comparison results
From the time performance comparison results recorded in table 2, it can be found that the recommendation method disclosed in this embodiment can achieve data erasure faster when deleting data with different proportions. By combining the results of table 1 and table 2, it was verified that the method of embodiment 1 can quickly and accurately achieve data erasure.
The invention can be applied to any recommendation system which is based on data driving and can be derived, and is used for responding to the request of considering data erasure such as privacy protection of a user or security of a platform or other parties, and the like, and assisting the realization of ecology of the recommendation system which is friendly, legal and compliant. The invention can be integrated into a deployed recommendation system in a software mode in a specific implementation level, and can also be installed on a website in an online mode to directly provide data erasure request interfaces for different users.
Example 2:
the embodiment discloses a data recommendation device, which comprises a memory and a processor, wherein the memory stores computer executable instructions, the processor is configured to execute the computer executable instructions, and the computer executable instructions are executed by the processor to realize the data forgetting learning method disclosed in the first embodiment.
Example 3:
the embodiment discloses a computer readable storage medium, and a computer program is stored on the computer readable storage medium, and when the computer program is run by a processor, the data forgetting learning method disclosed in the first embodiment is realized.
Claims (10)
1. A data forgetting learning method facing a recommendation system model is characterized by comprising the following steps:
s1, definitionIs a parameter of->The recommendation system model of the representation is +.>In training data set->The following calculation function, which is output as a prediction for the sample, and is +.>The optimal model ∈>Expressed as->Wherein->For the recommendation system model in the data set +.>The sum of the loss functions below, expressed as +.>，/>Representing data set +.>One of them, < >>Representing the user->Representing articles->Representing user +.>For articles->Is->Representing a loss function;
s2, in the data setThe unavailable data set is +.>The remaining data set is +.>The model after erasing the data isExpressed as->Estimating ∈according to the influence function>The estimated result is recorded as +.>The resulting erase unavailable data set +.>The latter model is denoted->。
2. The recommendation system model oriented data forgetting learning method of claim 1 wherein the pushingThe referral system model uses different data setsThe erasure-disabled data set is used in a different way for the input samples>The model calculation functions of (2) are also different.
3. The recommendation system model oriented data forgetting learning method of claim 2 wherein the erasing of unusable data setsThe specific calculation process of the model calculation function of (a) is as follows:
(1) If data setUser item pair input in->Is a recommendation system model calculation functionWill leave the data set +.>All of the sample points satisfying the conditionRecorded as a calculation function change dataset +.>The recommendation system model is based on the dataset +.>And the left data set->Lower calculation data set +.>The difference between the loss functions of all sample points in (a) is +.>，Denoted as->；
(2) Computing unusable data setsIn dataset +.>The lower loss function->，/>Denoted as->；
(3) Based on the obtained、/>Defined as->Based on +.>Intensity plusAnd->Optimal model->，/>Expressed as:
wherein->Representing a disturbance term, deriving +.>，/>；
(4) Defining unusable data setsThe influence function for the recommendation system model is +.>，/>Expressed as: />Wherein->Representation pair->Conduct and->Expressed as a Hessian matrix,>an inverse matrix denoted as a Hessian matrix;
(5) Then based on the influence functionEstimate->Is->Erase unavailable data set +.>The model after that is:
。
4. the recommendation system model-oriented data forgetting learning method according to claim 3, wherein acceleration is realized by a pruning method when data erasure is performed by the data forgetting learning method, and model parameters which are not important for data erasure are deleted.
5. The recommendation system model-oriented data forgetting learning method according to claim 4, wherein the specific flow of deleting unimportant model parameters in the pruning method is as follows:
(1) Data setAll users and item sets in (1) are denoted +.>And for each element thereinCalculating a set of elements with which statistics interact, denoted +.>And sets the maximum number of iterations K, for all elements +.>Initializing an importance score for it +.>Cutting ratio per iteration；
(2) Initializing an empty setTraversing data set +.>Is>Pairing user articlesAdded to->Execute any->Update->The method comprises the steps of carrying out a first treatment on the surface of the After the traversal is completed, updateFinally let->；
(3) If it isInitializing a null set->Let all->Traversing->For any->Update->And will->Added to->The method comprises the steps of carrying out a first treatment on the surface of the After the traversal is completed, updateLet->；
(4) Continuing to execute the step (3) to obtain ，/>Representing the model parameters corresponding to v and returning +.>The method comprises the steps of carrying out a first treatment on the surface of the According to the->Will->The other parameters are marked as +.>There is->，，/>For the model parameters important for erasing data +.>For model parameters that are not important for erasing data, the +.>Is only considered +.>Is a variation of (c).
6. The push-oriented device of claim 5A data forgetting learning method of a recommendation system model is characterized in that the method is based on an influence function of the recommendation system modelWill beReplaced by->And +.>Fix->Taking this as a constant, then ∈ ->Namely, the method is simplified as follows:whereinErase unavailable data set +.>The model after that is:wherein->。
7. The recommendation system model oriented data forgetting learning method of claim 6 wherein if saidThe parameter number of (2) is->（/>) The computational complexity of the model update is reduced to +.>Or->Wherein->For influence->Sample size of->For model parameters>Is the time complexity.
8. The recommendation system model oriented data forgetting learning method of claim 1 wherein the loss functionIs a binary cross entropy loss function.
9. A data recommendation device comprising a memory storing computer executable instructions and a processor configured to execute the computer executable instructions, wherein the computer executable instructions when executed by the processor implement the data forgetting learning method of any of claims 1 to 8.
10. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when executed by a processor implements the data forgetting learning method according to any one of claims 1 to 8.
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