CN109033463A - A kind of community's question and answer content recommendation method based on end-to-end memory network - Google Patents
A kind of community's question and answer content recommendation method based on end-to-end memory network Download PDFInfo
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Abstract
The invention discloses a kind of community's question and answer content recommendation method based on end-to-end memory network, first acquisition title are pre-processed as data set and to data set, and data set is divided into training set, verifying collects and test set；Then end-to-end memory network model is established according to data set；Finally using stochastic gradient descent (SGD) Optimized model that there is AdaGrad to update rule.
Description
Technical field
The present invention relates to commending contents fields, more particularly, to a kind of community's question and answer based on end-to-end memory network
Content recommendation method.
Background technique
Web Community's question and answer are that current people solve the problems, such as and share the main using platform of knowledge experience, for example know,
Range of information is extensive, is not but that everyone is interested, therefore needs the interested commending contents of user increasing user to user
Viscosity.
Summary of the invention
Present invention aim to address said one or multiple defects, propose a kind of community based on end-to-end memory network
Question and answer content recommendation method.
To realize the above goal of the invention, the technical solution adopted is that:
A kind of community's question and answer content recommendation method based on end-to-end memory network, comprising the following steps:
S1: obtaining title and pre-process as data set and to data set, data set is divided into training set, verifying collects
And test set；
S2: end-to-end memory network model is established according to data set；
S3: stochastic gradient descent (SGD) Optimized model that there is AdaGrad to update rule is used.
Preferably, data ensemble average described in step S1 is divided into training set, verifying collection and test set.
Preferably, entitled user described in step S1 is browsing the content mark with historical behavior in community's question and answer
Topic.
Preferably, the end-to-end memory models include single-layer model and multilayered model；The wherein single-layer model packet
Include memory component, input module and output precision；
Wherein memory component indicates: storing the title set D={ x of historical behavior1,x2...xn, using size be dim × | V
| matrix A by each word wij∈xiIt is embedded into the memory vector { a of d dimensionijIn, so that aij=Awij.Entire sentence collection { xiMake
Memory vector { a that dimension is d is converted to matrix Ai}；
Input module indicates: positive browsing title q is vector b by B matrix conversion, calculates b and each memory aiBetween
With degree, formula: pi=Softmax (bTai)；Wherein Softmax (zi)=eZi/∑jeZj, p is the probability vector in input；
Output precision indicates: the title set D={ x of historical behavior1,x2...xn, being converted to dimension using Matrix C is d's
Output vector ci, output o is output vector ciWith probability vector weighted sum, formula:
Final prediction f=Softmax (W (o+b))；
The multilayered model is then that the title q of input module is the sum of upper hop input header b and output o, i.e. next layer of k
+ 1 input is the output o from layer kkWith input bkSummation, formula: bk+1=ok+bk；
Wherein each layer has the embeded matrix A of oneselfk, Ck, for being embedded in input { xi}。
Preferably, the multilayered model further includes sentence expression, each sentence xi={ xi1, xi2..., xin, it is embedding
Enter each word and sum to obtained vector, and time expression is added, word vector is the 0-1 vector that a length is V, is made
Obtain ai=∑jAxij+TA(i)；Wherein TA(i) be code time information Special matrix TAThe i-th row；Similarly, output insertion is used
Matrix Tc, ci=∑jCxij+TC(i)., TAAnd TCAll learn during the training period.
Preferably, the multilayered model further includes Word similarity, in the positive browsing title q of first layer, utilizes word phase
To be more than that 0.8 keyword is added in q with similarity in q in memory like degree, avoid in memory with the meaning in q or phase
Closely, different title weight is too low for word；
In the corpus being made of all pretreated titles, the keyword of positive browsing title is selected, and it is remaining
Keyword carries out Word similarity two-by-two and calculates, calculation formula:
Wherein yi is for what w1 and w2 started branch at i-th layer
Number.
Preferably, the evaluation criteria of the model is accuracy, recall rate and F1 score.
Compared with prior art, the beneficial effects of the present invention are:
End-to-end memory network can remember a large number of users behavior, and the time is added, and make the interest prediction of user is more acurrate can
It leans on.Supervision item is reduced using training end to end.Containing attention mechanism, so that different titles has different weights, it can
The point of interest of prediction is ranked up, and then the emphasis point recommended would also vary from, the big point of interest ranking of weight is high, then institute
This point of interest content recommended is more than other point of interest contents.Word similarity is added, keeps prediction more accurate.
Detailed description of the invention
Fig. 1 is flow chart of the present invention.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
Below in conjunction with drawings and examples, the present invention is further elaborated.
Embodiment 1
A kind of community's question and answer content recommendation method based on end-to-end memory network, referring to FIG. 1, the following steps are included:
S1: obtaining title and pre-process as data set and to data set, data set is divided into training set, verifying collects
And test set；
S2: end-to-end memory network model is established according to data set；
S3: stochastic gradient descent (SGD) Optimized model that there is AdaGrad to update rule is used.
For knowing, know and know compared to Baidu, is more prone to sharing problem and its answer, rather than problem is made
It answers.Each problem is very brief, descriptive strong, therefore using problem as title.For all titles got, will carry out pre-
Processing, first segments each title, stop words and spcial character, such as " " " a " is then deleted, due to what is known
Many in enquirement " why " " how " " experience ", therefore these words can also delete, and avoid common unrelated word weight excessive, cover
The maximum length of required heavy duty word and sentence is set as 50, and the content for being more than needs to intercept.Data set is equally divided into training
Collection, verifying collection and test set.
Select the title of user's history behavior as the memory in model, historical behavior include remove browsing it is newest
It browses title, approve of title, answer title, pay close attention to title, every kind of title is according to selection of time newest 5, since it is desired that pushing away
The related content of the newest interest of user is recommended, therefore sorts to the title chosen by user operation time and constitutes title set D, Mei Gebiao
Topic insertion dimension experiment effect in 300-500 is relatively good, and the dimension the big more can be shown that sentence, and the model hop count of selection preferably exists
3 or so, should not be excessively also unsuitable very few, both effect can be made to reduce.
In the present embodiment, the end-to-end memory models include single-layer model and multilayered model；The wherein single-layer model
Including memory component, input module and output precision；
Wherein memory component indicates: storing the title set D={ x of historical behavior1,x2...xn, using size be dim × | V
| matrix A by each word wij∈xiIt is embedded into the memory vector { a of d dimensionijIn, so that aij=Awij.Entire sentence collection { xiMake
Memory vector { a that dimension is d is converted to matrix Ai}；
Input module indicates: positive browsing title q is vector b by B matrix conversion, calculates b and each memory aiBetween
With degree, formula: pi=Softmax (bTai)；Wherein Softmax (zi)=eZi/∑jeZj, p is the probability vector in input；
Output precision indicates: the title set D={ x of historical behavior1,x2...xn, being converted to dimension using Matrix C is d's
Output vector ci, output o is output vector ciWith probability vector weighted sum, formula:
Final prediction f=Softmax (W (o+b))；
The multilayered model is then that the title q of input module is the sum of upper hop input header b and output o, i.e. next layer of k
+ 1 input is the output o from layer kkWith input bkSummation, formula: bk+1=ok+bk；
Wherein each layer has the embeded matrix A of oneselfk, Ck, for being embedded in input { xi}。
In the present embodiment, the multilayered model further includes sentence expression, each sentence xi={ xi1, xi2..., xin,
It being embedded in each word and sums to obtained vector, and time expression is added, word vector is the 0-1 vector that a length is V,
So that ai=∑jAxij+TA(i)；Wherein TA(i) be code time information Special matrix TAThe i-th row；Similarly, output insertion
With matrix Tc, ci=∑jCxij+TC(i)., TAAnd TCAll learn during the training period.
Wherein each matrix is also all being drawn by training such as A, B, C, W, and training reduces number of parameters for convenience,
First jumps matrix A1=B, final jump matrix WT=CK, the dot-blur pattern A of other every jumps is identical as upper hop output matrix C, i.e.,
Ak+1=Ck, similarly, the matrix T indicated for the timeA, TCParameter is reduced in the same way.
In the present embodiment, the multilayered model further includes Word similarity, in the positive browsing title q of first layer, utilizes word
Similarity, by be more than with similarity in q in memory 0.8 keyword be added q in, avoid memory in in q the meaning or phase
Closely, different title weight is too low for word；
In the corpus being made of all pretreated titles, the keyword of positive browsing title is selected, and it is remaining
Keyword carries out Word similarity two-by-two and calculates, calculation formula:
Wherein yi is for what w1 and w2 started branch at i-th layer
Number.
The result that model prediction the goes out point of interest nearest as user, for each positive browsing title, the interest predicted
Point is according to first 5 of ranking selection.Using point of interest as label, recommend the corresponding hot content of label, such as the result mark predicted
There is " friend " in label, then recommends the hot content for having " friend " label.
In the present embodiment, the evaluation criteria of the model is accuracy, recall rate and F1 score.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (7)
1. a kind of community's question and answer content recommendation method based on end-to-end memory network, which comprises the following steps:
S1: obtaining title and pre-process as data set and to data set, data set is divided into training set, verifying collects and surveys
Examination collection；
S2: end-to-end memory network model is established according to data set；
S3: stochastic gradient descent (SGD) Optimized model that there is AdaGrad to update rule is used.
2. a kind of community's question and answer content recommendation method based on end-to-end memory network according to claim 1, feature
It is, data ensemble average described in step S1 is divided into training set, verifying collection and test set.
3. a kind of community's question and answer content recommendation method based on end-to-end memory network according to claim 1, feature
It is, entitled user described in step S1 is browsing the content title with historical behavior in community's question and answer.
4. a kind of community's question and answer content recommendation method based on end-to-end memory network according to claim 1, feature
It is, the end-to-end memory models include single-layer model and multilayered model；Wherein the single-layer model include memory component, it is defeated
Enter component and output precision；
Wherein memory component indicates: storing the title set D={ x of historical behavior1,x2...xn, using size be dim × | V |
Matrix A is by each word wij∈xiIt is embedded into the memory vector { a of d dimensionijIn, so that aij=Awij.Entire sentence collection { xiUse
Matrix A is converted to the memory vector { a that dimension is di}；
Input module indicates: positive browsing title q is vector b by B matrix conversion, calculates b and each memory aiBetween matching degree,
Formula: pi=Softmax (bTai)；Wherein Softmax (zi)=eZi/∑jeZj, p is the probability vector in input；
Output precision indicates: the title set D={ x of historical behavior1,x2...xn, the output that dimension is d is converted to using Matrix C
Vector ci, output o is output vector ciWith probability vector weighted sum, formula:
Final prediction f=Softmax (W (o+b))；
The multilayered model is then that the title q of input module is upper hop input header b and output the sum of o, i.e. next layer of k+1's
Input is the output o from layer kkWith input bkSummation, formula: bk+1=ok+bk；
Wherein each layer has the embeded matrix A of oneselfk, Ck, for being embedded in input { xi}。
5. a kind of community's question and answer content recommendation method based on end-to-end memory network according to claim 4, feature
It is, the multilayered model further includes sentence expression, each sentence xi={ xi1, xi2..., xin, it is embedded in each word simultaneously
Vector summation to obtaining, and time expression is added, word vector is the 0-1 vector that a length is V, so that ai=∑jAxij
+TA(i)；Wherein TA(i) be code time information Special matrix TAThe i-th row；Similarly, insertion matrix Tc, ci=are exported
∑jCxij+TC(i)., TAAnd TCAll learn during the training period.
6. a kind of community's question and answer content recommendation method based on end-to-end memory network according to claim 4, feature
It is, the multilayered model further includes Word similarity,, will be in memory using Word similarity in the positive browsing title q of first layer
With similarity in q be more than 0.8 keyword be added q in, avoid memory in in q the meaning or it is close, word is different
Title weight is too low；
In the corpus being made of all pretreated titles, the keyword of positive browsing title is selected, with remaining key
Word carries out Word similarity two-by-two and calculates, calculation formula:
Wherein yi is the coefficient that w1 and w2 starts branch at i-th layer.
7. a kind of community's question and answer content recommendation method based on end-to-end memory network according to claim 1, feature
It is, the evaluation criteria of the model is accuracy, recall rate and F1 score.
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| CN110188272A (en) * | 2019-05-27 | 2019-08-30 | 南京大学 | A kind of community's question and answer web site tags recommended method based on user context |
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