CN113127604A - Comment text-based fine-grained item recommendation method and system - Google Patents

Comment text-based fine-grained item recommendation method and system Download PDF

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CN113127604A
CN113127604A CN202110505981.5A CN202110505981A CN113127604A CN 113127604 A CN113127604 A CN 113127604A CN 202110505981 A CN202110505981 A CN 202110505981A CN 113127604 A CN113127604 A CN 113127604A
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article
comment text
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杨振宇
王皓
刘国敬
崔来平
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Qilu University of Technology
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Abstract

The disclosure provides a comment text-based fine-grained item recommendation method and a comment text-based fine-grained item recommendation system, which comprise the following steps: respectively acquiring a user comment text set and an article comment text set; calculating a multi-granularity incidence matrix between a user and an article by using a fine-granularity feature interaction network to obtain a 3D interaction image; inputting the 3D interactive image into a fully-connected neural network and a traditional factorization machine to realize the grading prediction of a user-article, and realizing the recommendation of the article according to the grading result; according to the scheme, the user and article comments are coded by a multi-level expansion convolution structure, loss of fine-grained information in the comments is avoided, feature interaction of the user and the article comments is established under multiple granularities, multi-grained information is fused and processed by using 3D convolution, relevant information under multiple granularities in the comments is effectively highlighted, and the reasonability and the accuracy of article recommendation are effectively improved.

Description

Comment text-based fine-grained item recommendation method and system
Technical Field
The disclosure belongs to the technical field of article recommendation, and particularly relates to a comment text-based fine-grained article recommendation method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In many works, it has proven effective to use comment text to boost the effectiveness of recommendation systems. The comment-based recommendation system not only alleviates the cold start problem, but also allows for finer grained user and item representations. Earlier work has focused primarily on topic modeling and language modeling approaches. In recent years, researchers have achieved good results using deep learning methods. ConvMF uses a Convolutional Neural Network (CNN) as an automatic feature extractor to encode users (items) into a low-dimensional vector representation. The score predictions are then combined using Probability Matrix Factorization (PMF). Deep countn (deep collaborative Neural networks) uses two parallel convolution architectures to infer potential feature representations from user and item reviews. The user and item feature representations are concatenated and used as input to a Factoring Machine (FM) for scoring predictions. Since the importance of different words to modeling is different, D-Attn introduces a word-level attention mechanism, giving different words different weights. DAML focuses on local information between users and item reviews and on interactions between the two. The DAML integrates the scoring feature and the comment feature into a unified frame, and realizes high-order nonlinear interaction of the features by using a nerve factor decomposition machine to complete final scoring prediction. The model interpretability is enhanced. The MPCN extracts important comments from the comments of users and items and then matches them word by word. The method can not only utilize the comments with the most abundant information to predict, but also carry out deeper character interaction.
With the great success of deep learning in the fields of natural language processing and the like, the performance of score prediction is greatly improved by many recommendation models based on the technology. In these works, the convolutional neural network architecture extracts potential features from corresponding reviews of users and items, respectively. The comments are represented by using a dense word embedding matrix, followed by application of a fixed-size sliding window to capture the contextual information. The convolutional neural network-based technology better promotes the understanding of semantic information in the comments, and the method leads to the remarkable improvement of the existing bag-of-words mode-based scoring prediction method. However, one of the significant disadvantages is that it is difficult for a person to effectively understand the features extracted by the neural network, which limits the interpretability of the recommendation system.
The key to a comment-based recommendation system is the capture of different granularity of information in the text of the comment. Generally speaking, the same user tends to have different preferences for different items, which collectively reflect the comments he writes. Meanwhile, important semantic features in the comments are hidden in text segments of different granularities. The illustration in the figure illustrates this problem. Different granularity of text (e.g., words, phrases, and sentences) may reveal user preferences for different items. For example, the word: "best", "great", "disapporting"; the phrase: "quality great battle life", "screen looks hooks amplitude" and the sentence: "I use all go with product read but it clicked a bit orange this year".
However, the inventor finds that the existing work usually learns a single feature vector for each user or article by integrating the scoring papers of the users and articles, and then realizes scoring prediction by means of factorization and the like. These methods have limitations in capturing fine-grained user preferences and item attributes because they simply assign a single feature vector to the user's item, resulting in information at multiple granularities in the review not being captured significantly.
Disclosure of Invention
In order to solve the problems, the invention provides a fine-grained article recommendation method and a fine-grained article recommendation system based on comment texts, the scheme provides a fine-grained feature interaction network, user and article comments are coded by a multi-level expansion convolution structure, loss of fine-grained information in the comments is avoided, feature interaction of the user and the article comments is constructed under multiple granularities, multi-grained information is fused and processed by using 3D convolution, relevant information under multiple granularities in the comments is effectively highlighted, and reasonability and accuracy of article recommendation are effectively improved.
According to a first aspect of the embodiments of the present disclosure, there is provided a comment text-based fine-grained item recommendation method, including:
respectively acquiring a user comment text set and an article comment text set;
calculating a multi-granularity incidence matrix between a user and an article by using a fine-granularity feature interaction network to obtain a 3D interaction image;
inputting the 3D interactive image into a fully-connected neural network and a traditional factorization machine to realize the grading prediction of a user-article, and realizing the recommendation of the article according to the grading result;
the fine-grained feature interaction network specifically includes: performing multi-granularity semantic feature extraction on each comment text in the user comment text set and the article comment text set by using a hierarchical expansion convolution model to obtain a multi-granularity comment expression set of the user and the article; and calculating a multi-granularity incidence matrix between the user and the article, and fusing the incidence matrix into a 3D interactive image through a 3D convolutional neural network.
Further, the convolution is performed on a continuous subsequence of the input at each step by using a hierarchical dilation convolution model different from the standard convolution, wherein the dilation convolution at different levels skips δ input elements at a time, and the dilation operation is specifically described as:
Figure BDA0003058407230000031
wherein the content of the first and second substances,
Figure BDA0003058407230000032
is vector concatenation, b is a bias term, ReLU is a non-linear activation function, xtRepresenting the context center word, W is a convolution kernel of size 2W +1, W is a parameter for adjusting the size of the convolution kernel, and δ is an expansion rate, which is an integer not less than 1.
Further, the calculating a multi-granularity association matrix between the user and the article specifically includes: the interaction of features between users and articles is realized by constructing a correlation matrix on each granularity, and given the nth and mth multi-granularity comment representations of the users and the articles, the correlation matrix of the ith layer of granularity is realized by the following dot product operation:
Figure BDA0003058407230000033
wherein, L ═ 1, 2.., L]As to the number of semantic granularities,
Figure BDA0003058407230000034
Figure BDA0003058407230000035
element (1) of
Figure BDA0003058407230000036
Representing the relevance of the ith and jth features in the user and item reviews at the granularity of l.
According to a second aspect of the embodiments of the present disclosure, there is provided a comment text-based fine-grained item recommendation system, including:
the data acquisition unit is used for respectively acquiring a user comment text set and an article comment text set;
the multi-granularity feature extraction unit is used for respectively extracting multi-granularity semantic features of each comment text in the user comment text set and the article comment text set by utilizing the hierarchical expansion convolution model to obtain multi-granularity comment expression sets of the user and the article;
the interactive image generation unit is used for constructing a multi-granularity incidence matrix between the user and the article based on the multi-granularity comment expression of the user and the article, and fusing the incidence matrix into a 3D interactive image through a 3D convolutional neural network;
and the item recommending unit is used for inputting the 3D interactive image into the fully-connected neural network and the traditional factorization machine to realize the grading prediction of the user-item and realize the recommendation of the item according to the grading result.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the memory, where the processor implements the comment text-based fine-grained item recommendation system when executing the program.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a comment text based fine-grained item recommendation system as described above.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) the scheme of the present disclosure provides a fine-grained feature interaction network, and the fine-grained feature interaction network is used to calculate a multi-grained incidence matrix between a user and an article, and has the advantages that: multi-level user/item representation and fine-grained feature interaction, the network does not represent each user by a single abstract vector, but uses hierarchical extended convolution to construct a comment-based multi-level representation in a unified module; by convolution with layered stack expansion, the received input width of each layer grows exponentially, while the number of parameters increases only linearly. At the same time, the output of each layer is retained as a feature map across text segments of different lengths, without loss of coverage because no form of pooling or stepping convolution is used; in this way, the scheme can step through local relevance and long-term dependency to obtain semantic features in comments, including word, phrase, and sentence hierarchies, at different granularities. According to the scheme, the user and item comments are coded by a multi-level expansion convolution structure, so that the loss of fine-grained information in the comments is avoided.
(2) In order to avoid information loss, the fine-grained feature interaction network constructs feature interaction of user comments and article comments on each semantic granularity. In practical application, the model is based on a hierarchical representation method of comment texts, and a correlation matrix is constructed for each pair of comments from word to sentence levels. In this way, a number of granular features implicit in user and item reviews may be identified and interacted with minimal loss, thereby providing sufficient content relevance cues for predicting accurate scores. Then, we merge multiple incidence matrices of each granular review pair into one 3D image, whose channels represent the degree of relevance of user and item review feature interactions at different granularities. Predicting a user's rating of the item by identifying a higher order salient signal similar to the 3D convolution based image identification hierarchy; according to the scheme, the characteristic interaction of the user and the item comment is constructed under multiple granularities, and the multi-granularity information is fused and processed by using 3D convolution, so that the relevant information in the comment under multiple granularities is effectively highlighted.
(3) The solution is not only superior in performance but also strongly interpretable.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic structural diagram of a fine-grained feature interaction network model according to a first embodiment of the present disclosure;
fig. 2 is a diagram illustrating a hierarchical dilation convolution according to a first embodiment of the disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the embodiment aims to provide a fine-grained item recommendation method based on comment texts.
A fine-grained item recommendation method based on comment texts comprises the following steps:
respectively acquiring a user comment text set and an article comment text set;
calculating a multi-granularity incidence matrix between a user and an article by using a fine-granularity feature interaction network to obtain a 3D interaction image;
inputting the 3D interactive image into a fully-connected neural network and a traditional factorization machine to realize the grading prediction of a user-article, and realizing the recommendation of the article according to the grading result;
the fine-grained feature interaction network specifically includes: performing multi-granularity semantic feature extraction on each comment text in the user comment text set and the article comment text set by using a hierarchical expansion convolution model to obtain a multi-granularity comment expression set of the user and the article; and calculating a multi-granularity incidence matrix between the user and the article, and fusing the incidence matrix into a 3D interactive image through a 3D convolutional neural network.
Specifically, for ease of understanding, the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings:
fig. 1 shows a schematic diagram of a fine-grained feature interactive network model structure, which in turn describes the fine-grained feature interactive network (FFIN) proposed by us. First, we define a recommendation problem based on comments; subsequently, we describe in detail the encoding process of the hierarchical expansion convolution structure in the comment representation module on the original comment text; secondly, the interactive process of the multi-granularity comment representation characteristics of the user and the article is shown; finally, we introduce a scoring prediction layer with a Factorizer (FM) to achieve user scoring of items, where:
problem definition
A comment-based recommendation question may be expressed as: for a given user u and item i, they both correspond to an original set of text sets of comments
Figure BDA0003058407230000061
And
Figure BDA0003058407230000062
the number of comments of the user and item comment sets is N, M, wherein the user comment text sets store comment texts of specific users about different items; and the item comment text set stores comment texts of different users about the specific item. Our goal is to build a predictive model that predicts the user's score for an item through a set of user and item reviews. Assuming that the maximum lengths of the individual comments in a set of user and item comment sets are Tu and Ti, respectively, the original comment texts of the nth user and the mth item may be represented as:
Figure BDA0003058407230000071
Figure BDA0003058407230000072
(II) comment text representation
Since the generation process of multi-granular review representations of users and items is similar, we only show the encoding step of a single review text, and will
Figure BDA0003058407230000073
To simplify to s, the comment length is set to T.
Inspired by the multi-layer extended convolution structure, as shown in fig. 2, we designed a hierarchical extended convolution (HDC) to learn multiple semantic granularity comment representations directly from the comment.
For a given comment text s ═ w1，w2，...，wT]The model is first mapped to the corresponding word vector matrix by an embedding matrix, i.e., X ═ X1，x2，...，xT]Wherein x ist∈RdIs the word-embedded representation of the t-th word in the text, and d is the dimension of the word vector. The HDC then takes the word vector matrix as input, capturing the multi-granular semantic features in the comments.
Unlike standard convolution, which convolves a continuous subsequence of input at each step, dilated convolution has a wider acceptance domain by skipping δ input elements at a time, where δ is the dilation rate. For context-centric word xtAnd a convolution kernel W of size 2W +1, the dilated convolution operation can be formulated as:
Figure BDA0003058407230000074
wherein the content of the first and second substances,
Figure BDA0003058407230000075
is vector concatenation, b is a bias term, ReLU is a non-linear activation function. As shown in fig. 2, the output of each convolutional layer is a weighted combination of the inputs of the previous layer. We start with δ ═ 1 (equivalent to the standard convolution) to ensure that no elements in the original input sequence are missing. Then, by overlapping the convolutions after expansion with a larger expansion rate level, the length of the convolution text is extended in an exponential manner, and the semantic features of different receiving domains can be covered only by using a small number of layers and a proper amount of parameters.
Furthermore, to prevent the disappearance of the gradient or explosion, we apply layer normalization (layer normalization) at the end of each convolutional layer. Since irrelevant information may be introduced into a long-distance semantic unit, we actually design a multi-level expansion rate according to the performance in verification. The output of each stack layer l is saved as text in a particular binA characteristic diagram on the degree level, and the formula is
Figure BDA0003058407230000081
Where f is the number of convolution filters per layer. Given the dilated convolution of the L-layer stack, the multi-granular comment representation can be defined as [ s ]1，s2，...，sL]. In this way, the HDC gradually harvests word sense and semantic features from the word and phrase level at a small expansion rate, capturing long-term dependencies from the sentence level at a large expansion rate. Our review coding module not only outperforms recurrent neural networks in terms of parallelism capability, but also significantly reduces memory consumption compared to a fully attention-based approach.
Through HDC module encoding, the multi-granular review representation of a single user and item can be expressed as:
Figure BDA0003058407230000082
Figure BDA0003058407230000083
wherein the content of the first and second substances,
Figure BDA0003058407230000084
set of comments S for user and item, respectivelyu、SiThe nth and mth comment texts in (1).
(III) feature interaction Module
Multi-granular set of comment representations for a given user and item
Figure BDA0003058407230000085
And
Figure BDA0003058407230000086
we achieve feature interaction between users and items by building a correlation matrix at each granularity. Nth and mth multi-granular review representations, i.e., l-th layer grain, for a given user and itemThe correlation matrix of degrees can be realized by the following dot product operation:
Figure BDA0003058407230000087
wherein, L ═ 1, 2.., L]Is the number of semantic granularities that are,
Figure BDA0003058407230000088
Figure BDA0003058407230000089
element (1) of
Figure BDA00030584072300000810
Representing the relevance of the ith and jth features in the user and item reviews at the granularity of l.
To summarize the multi-granularity features in the reviews of the entire user and item, the FFIN fuses the incidence matrices obtained from all interactions of the user and item into one 3D interactive image, with the formula:
Figure BDA00030584072300000811
wherein n × m represents the total number of the incidence matrices under a single granularity, and each pixel point in the 3D interactive image is obtained by:
Figure BDA0003058407230000091
specifically, each pixel point is a spliced vector of features at all granularities, and represents the degree of interaction between a user and an article at multiple granularities.
Since the scoring behavior of the user is usually personalized with subjectivity, different preferences are shown when different articles are faced. We identify a prominent matching signal from the entire image using a hierarchical 3D convolutional neural network and max pooling operations. Three-dimensional convolution is an extension of typical two-dimensional convolution, with both the filter and the step size being three-dimensional cubes. Formally, the high-order pixel on the z-th feature map of the t-th layer is calculated at (k, i, j) in the following way:
Figure BDA0003058407230000092
wherein ELU is a nonlinear activation function, z' represents a characteristic diagram of a previous layer,
Figure BDA0003058407230000093
and b(t)A 3D convolution kernel and an offset term, respectively, of size Wt×Ht×Rt. Then, significant information is extracted by a max pooling operation:
Figure BDA0003058407230000094
wherein the content of the first and second substances,
Figure BDA0003058407230000095
and
Figure BDA0003058407230000096
is the size of the 3D pooling operation. The output of the last layer is to splice the comprehensive interaction vectors between the users and the article comments, and the vectors are expressed as
Figure BDA0003058407230000097
(IV) score prediction module
In this subsection, we use the prediction vectors generated by the feature interaction module described above
Figure BDA0003058407230000098
Input into a fully connected neural network and a conventional Factoring Machine (FM) to achieve a final user-item score prediction. The implementation of a fully-connected neural network can be formulated as follows:
Figure BDA0003058407230000099
wherein, W0∈Rn×eAnd b0∈RnRespectively, a weight parameter and a bias term for a fully connected network.
FM accepts a real-valued feature vector and models the interaction between features using decomposition parameters. It is defined as follows
Figure BDA0003058407230000101
Wherein the content of the first and second substances,
Figure BDA0003058407230000102
is a real-valued input feature vector.<.，.>Is a dot product operation. Parameter { v }1，...，vn}，υ∈RnIs for modeling an interaction pair (x)i，xi) The decomposition parameter of (2).
Figure BDA0003058407230000103
And
Figure BDA0003058407230000104
respectively, a global bias term and a linear regression component. Weight parameter of FM. Output of FM
Figure BDA0003058407230000105
Is a scalar quantity representing the predicted scores of user and item interactions.
Further, different articles are ranked from large to small according to the prediction scores of the articles, and the articles with high prediction scores are recommended to the user.
Example two:
the embodiment aims to provide a fine-grained item recommendation system based on comment texts.
A comment text-based fine-grained item recommendation system comprising:
the data acquisition unit is used for respectively acquiring a user comment text set and an article comment text set;
the multi-granularity feature extraction unit is used for respectively extracting multi-granularity semantic features of each comment text in the user comment text set and the article comment text set by utilizing the hierarchical expansion convolution model to obtain multi-granularity comment expression sets of the user and the article;
the interactive image generation unit is used for constructing a multi-granularity incidence matrix between the user and the article based on the multi-granularity comment expression of the user and the article, and fusing the incidence matrix into a 3D interactive image through a 3D convolutional neural network;
and the item recommending unit is used for inputting the 3D interactive image into the fully-connected neural network and the traditional factorization machine to realize the grading prediction of the user-item and realize the recommendation of the item according to the grading result.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment one. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The fine-grained article recommendation method and system based on the comment text can be realized, and have a wide application prospect.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (10)

1. A fine-grained item recommendation method based on comment texts is characterized by comprising the following steps:
respectively acquiring a user comment text set and an article comment text set;
calculating a multi-granularity incidence matrix between a user and an article by using a fine-granularity feature interaction network to obtain a 3D interaction image;
inputting the 3D interactive image into a fully-connected neural network and a traditional factorization machine to realize the grading prediction of a user-article, and realizing the recommendation of the article according to the grading result;
the fine-grained feature interaction network specifically includes: performing multi-granularity semantic feature extraction on each comment text in the user comment text set and the article comment text set by using a hierarchical expansion convolution model to obtain a multi-granularity comment expression set of the user and the article; and calculating a multi-granularity incidence matrix between the user and the article, and fusing the incidence matrix into a 3D interactive image through a 3D convolutional neural network.
2. A comment text based fine grain item recommendation method according to claim 1, wherein the use of the hierarchical dilation convolution model to convolve a continuous subsequence of inputs at each step differently from the standard convolution, the different levels of dilation convolution operate by skipping δ input elements at a time, and the dilation operation is specifically described as:
Figure FDA0003058407220000011
wherein the content of the first and second substances,
Figure FDA0003058407220000012
is vector concatenation, b is a bias term, ReLU is a non-linear activation function, xtRepresenting the context center word, W is a convolution kernel of size 2W +1, W is a parameter for adjusting the size of the convolution kernel, and δ is an expansion rate, which is an integer not less than 1.
3. A comment text based fine grain item recommendation method as claimed in claim 2 wherein to prevent the disappearance or explosion of the gradient, the last application layer of the dilation convolution at each layer is normalized.
4. The method for recommending fine-grained items based on comment texts as claimed in claim 1, wherein said calculating a multi-grained association matrix between the user and the item specifically comprises: the interaction of features between users and articles is realized by constructing a correlation matrix on each granularity, and given the nth and mth multi-granularity comment representations of the users and the articles, the correlation matrix of the ith layer of granularity is realized by the following dot product operation:
Figure FDA0003058407220000021
wherein, L ═ 1, 2.., L]As to the number of semantic granularities,
Figure FDA0003058407220000022
Figure FDA0003058407220000023
element (1) of
Figure FDA0003058407220000024
Representing the relevance of the ith and jth features in the user and item reviews at the granularity of l.
5. The fine-grained item recommendation method based on comment text as claimed in claim 1, wherein the incidence matrixes are fused into a 3D interactive image through a 3D convolutional neural network, wherein each pixel point in the 3D interactive image is a spliced vector of features at all granularities, and represents the degree of interaction between a user and an item at multiple granularities.
6. The comment text-based fine-grained item recommendation method according to claim 1, wherein the 3D interactive image is input into a fully-connected neural network and a traditional factorization machine to realize user-item rating prediction, and specifically comprises the following steps: inputting the 3D interactive image into a full-connection neural network to obtain a real-value feature vector, inputting the feature vector into a factor decomposition machine, modeling the interaction between features by using decomposition parameters, and outputting a prediction score of the interaction between the user and the article.
7. The comment text-based fine-grained item recommendation method according to claim 1, wherein before multi-grained semantic feature extraction is performed on comment texts by adopting a hierarchical expansion convolution model, each comment text is mapped into a corresponding word vector matrix through an embedding matrix.
8. A comment text-based fine-grained item recommendation system is characterized by comprising:
the data acquisition unit is used for respectively acquiring a user comment text set and an article comment text set;
the multi-granularity feature extraction unit is used for respectively extracting multi-granularity semantic features of each comment text in the user comment text set and the article comment text set by utilizing the hierarchical expansion convolution model to obtain multi-granularity comment expression sets of the user and the article;
the interactive image generation unit is used for constructing a multi-granularity incidence matrix between the user and the article based on the multi-granularity comment expression of the user and the article, and fusing the incidence matrix into a 3D interactive image through a 3D convolutional neural network;
and the item recommending unit is used for inputting the 3D interactive image into the fully-connected neural network and the traditional factorization machine to realize the grading prediction of the user-item and realize the recommendation of the item according to the grading result.
9. An electronic device comprising a memory, a processor, and a computer program stored and executed on the memory, wherein the processor when executing the program implements a comment text based fine grain item recommendation system as recited in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a comment text based fine grain item recommendation system as recited in any one of claims 1 to 7.
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