CN110807156A - Interest recommendation method and system based on user sequence click behaviors - Google Patents
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Abstract
The invention discloses an interest recommendation method and system based on user sequence clicking behaviors, which comprises the steps of firstly, obtaining historical interactive project data of a user to form a historical interactive project sequence of the user; constructing an interest recommendation model; carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model; extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user; interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations; and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
Description
Technical Field
The invention relates to the technical field of project recommendation, in particular to an interest recommendation method and system based on user sequence click behaviors.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid development of the internet industry, we have entered an information explosion era. The acceptance of individuals is severely "overloaded" by a wide variety of items, rapidly increasing news information, advertising information spread over the sky, and the like. The enormous amount of information on the internet brings great challenges to both information providers and information users: how an information provider aims at showing mass information stored by the information provider to an information user; how a user of information screens out information needed by himself from a plurality of information. The recommendation system is generated according to the series of problems.
So-called recommendation systems, in short, recommend content for a user that is most likely to be of interest based on the user's preferences. The general recommendation method is mainly represented by a collaborative filtering model. The most common collaborative filtering methods mainly include a neighborhood method and a matrix decomposition model. The neighborhood method carries out recommendation by measuring the distance between the items and the similarity matrix, and the algorithms based on the neighborhoods are divided into two categories, one category is a collaborative filtering algorithm based on users, and the other category is a collaborative filtering algorithm based on articles. While matrix factorization estimates user preferences for items by the inner product between user and item embedding. Although the two methods described above have achieved significant results in terms of recommendation, these methods simply refer to items that the user has interacted with as a collection of items, and recommend them by analyzing the collection. In short, they simply recommend by mining static correlations between users and items. They only focus on the long-term static preferences of the user, ignore the transition of preferences in the hidden user sequence behavior, and are not able to model complex relationships in the sequence data, and are therefore not suitable for sequence recommendation. The sequential recommendation does not simply recommend items interacted with by the user as a collection of items, but rather recommends items as a sequence of items, taking into account not only the long-term static general preferences of the user, but also the short-term dynamic sequential patterns of the user.
In order to solve the problems of the general recommendation system, the sequence recommendation system based on the user interaction behavior has received extensive attention in recent years in academia and industry. The purpose of the sequential recommendation system is to predict the successive items that a user is likely to interact with in the future given the items that the user has interacted with in the past. When people surf the internet, a great deal of user interaction (such as clicking, commenting, collecting and the like of the user) is recorded, and the rich user interaction behaviors provide opportunities for knowing the interests and hobbies of the user. People effectively recommend the users by analyzing the sequential behaviors of the users and mining the user preferences implicit in the interaction behaviors.
In recent years, as a branch of machine learning, deep learning has become a considerable force, and has started to play a large role in the fields of machine translation, image recognition, voice recognition, and the like. Meanwhile, deep learning models, such as RNN, GRU, LSTM, etc., are also beginning to be used step by step in the field of sequence recommendation. At the same time, some studies have also begun to attempt to improve the performance and interpretability of sequence recommendations using attention-driven mechanisms due to their strong potential to model sequential data in tasks such as machine translation. During the development process, the inventor finds that the general sequence recommendation system takes the item interacted by the user as a whole as an input, firstly, the general sequence recommendation system does not consider the internal structure of the sequence, namely the sequence is composed of sessions, and the interest of the user in the same session is different from one session to another: user behavior is similar in each session and different between different sessions. Second, they only consider the sequence patterns between items and ignore the sequence transformation relationships between items.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an interest recommendation method and system based on user sequence click behaviors, firstly, according to the fact that the preferences of users in a certain time period are similar, the interaction sequences of the users are divided into different conversations, then, the sequence recommendation is realized by combining the user interests in each conversation and the interest interaction among different conversations, and the problems that the existing sequence recommendation method ignores the internal structure of user sequence behaviors and ignores the conversion relation among items are effectively solved.
The technical scheme of the interest recommendation method based on the user sequence click behavior provided by the invention is as follows:
an interest recommendation method based on user sequence click behaviors comprises the following steps:
acquiring historical interactive project data of a user to form a historical interactive project sequence of the user;
constructing an interest recommendation model;
carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model;
extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user;
interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations;
and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
The technical scheme of the interest recommendation system based on the user sequence click behavior provided by the invention is as follows:
an interest recommendation system based on user sequence click behavior, the system comprising:
the data acquisition module is used for acquiring historical interactive project data of the user to form a historical interactive project sequence of the user;
the model construction module is used for constructing an interest recommendation model;
the session division module is used for carrying out session division on the historical interactive item sequence of the user by utilizing the interest recommendation model;
the intra-session interest extraction module is used for extracting each intra-session interest obtained after division;
the activation module is used for endowing different weights to the interests in each session to obtain a session interest sequence of the user;
the inter-session interest interaction module is used for interacting the interests among different sessions to obtain a dynamic interaction model among the different sessions;
and the prediction module is used for inputting the session interest sequence of the user into a dynamic interaction model among different sessions to predict and obtain a target item sequence to be recommended.
Another aspect of the present invention provides a computer-readable storage medium, wherein:
a computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring historical interactive project data of a user to form a historical interactive project sequence of the user;
constructing an interest recommendation model;
carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model;
extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user;
interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations;
and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
Another aspect of the present invention provides a processing apparatus, including:
a processing apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps when executing the program:
acquiring historical interactive project data of a user to form a historical interactive project sequence of the user;
constructing an interest recommendation model;
carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model;
extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user;
interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations;
and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
Through the technical scheme, the invention has the beneficial effects that:
(1) the invention divides the click sequence of the user into different conversations, takes the conversation as the minimum operation unit, and can reduce the information loss caused by neglecting or destroying the conversation structure to the maximum extent; and the conversation is used as a basic operation unit, which is more beneficial to parallel processing, and the relative method is very efficient in training efficiency.
(2) After dividing the interaction sequence of the user into different sessions, the embodiment first utilizes an interest extraction layer in a transform session based on multi-head self-attention to extract the interest of the user in each session; compared with the traditional modeling of an interactive sequence through an RNN structure, the relevance of the interest extraction layer in the multi-head self-attention-based Transformer session does not depend on the distance and the distance of the current item and other items no matter how far the spatial distance of the current item and other items is, but depends on the relevance of the two items, so that the sequence item can be better modeled; meanwhile, the attention mechanism is only calculated by simple matrix multiplication, so that the calculation speed is higher, and the parallelization processing is easier.
(3) The extracted session interest utilizes a Bi-GRU inter-session interest interaction layer to model a sequence conversion relation between sessions; and different weights are given to the extracted session features, so that the interest of the user in each session is considered, the session interest of the user is fused with the context information, the method has expressive force, and the recommendation performance is greatly improved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the application and not to limit the invention.
FIG. 1 is a flowchart illustrating a method for interest recommendation based on user sequence click behavior according to an embodiment;
FIG. 2 is a block diagram of an interest recommendation model in accordance with one embodiment;
FIG. 3 is a diagram illustrating a structure of an interest extraction layer in a transform session according to an embodiment;
FIG. 4 is a block diagram of an interest recommendation system based on a user sequence click behavior according to an embodiment.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention 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 application. 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.
Example one
Fig. 1 is a flowchart of an interest recommendation method based on a user sequence click behavior according to this embodiment. According to the interest recommendation method, firstly, the interaction sequences of the users are divided into different conversations according to the similarity of the preferences of the users in a certain time period, then the sequence recommendation is realized by combining the user interest in each conversation and the interest interaction among different conversations, and the problems that the existing sequence recommendation method ignores the internal structure of the user sequence behaviors and ignores the conversion relation among items are effectively solved.
Referring to fig. 1, the interest recommendation method based on user sequence click behavior includes the following steps:
s101, acquiring a historical interactive item sequence of the user.
Specifically, historical interaction item data of a user is obtained, and a historical interaction item sequence of the user is formed, wherein the historical interaction sequence of the user is represented by S ═ { h ═ h1,h2,h3...hn...hNAnd N is the total number of the user history interaction items.
In this embodiment, the interactive item data may be click item data of a user at a shopping website, song data listened to by the user at a music playing software, or movie data watched by the user at a video playing software.
S102, an interest recommendation model is constructed.
FIG. 2 is a schematic diagram of a structure of an interest recommendation model. As shown in FIG. 2, the interest recommendation model comprises a session division layer, an embedding layer, a transform intra-session interest extraction layer, a Bi-GRU inter-session interest interaction layer, an activation layer and a prediction layer.
And S103, session division.
Specifically, a historical interactive item sequence of a user is input into a session division layer of an interest recommendation model, and the session division layer of the interest recommendation model is used for dividing different sessions according to different items and different time spans, for example, the sessions are divided by a time interval of 30 minutes when the user clicks an item on a certain shopping website; or dividing the conversation by the time interval of 1 week when the user listens to the songs by certain music playing software; or divide the session with time intervals of one week when the user watches a movie on certain video playing software. The divided kth session may be denoted as Qk ═ h1,h2,h3…hiAnd f, wherein i is the total number of user interaction items in the kth session. After the session is divided, the mth item in the kth session can be represented as hkm。
In this embodiment, each item in each session has three identifiers, which are respectively the sequence feature identifier of the item, the position identifier of the item in the user click sequence, and the position identifier of the item in the session.
In this embodiment, after the historical interactive item sequence of the user is different sessions, the obtained sparse features of the user behavior and the target item in the session are input to the embedding layer of the interest recommendation model, because the embedding layer does not contain any convolution or cyclic structure, two learnable position embedding needs to be injected in the input process, the sparse features of the user behavior and the target item in the session are converted into low-dimensional dense features through the embedding layer, and the expression is as follows:
wherein, H is an item characteristic embedding matrix, P is an item position embedding matrix of the session where the item is located, and P is an item embedding matrix in the session. Through the embedding layer, a low-dimensional embedding vector Q can be obtained.
Past interest recommendation systems have generally used user-item interaction pairs as the basic unit of operation, in which case the user's intent at a certain point in time is easily overwhelmed by his historical behavior. In view of the above disadvantages, the present embodiment divides the click sequence behavior of the user into different sessions, and takes the session as the smallest operation unit, so as to reduce the information loss caused by ignoring or destroying the session structure to the maximum; and the conversation is used as a basic operation unit, which is more beneficial to parallel processing, and the relative method is very efficient in training efficiency.
And S104, extracting interest in the session.
Specifically, the interests of the users in a single session are similar, for example, if the user browses black hat and black shoes in one session, then it can be inferred that the interest preference of the user in the session is black clothes, so the embodiment inputs the low-dimensional dense feature vectors of the user behaviors and the target items from the embedded layer into the interest extraction layer in the transform session based on the multi-head self-attention mechanism, and extracts the interest of the user in each session.
FIG. 3 is a schematic diagram of the structure of an interest extraction layer in a transform session. As shown in FIG. 3, the low-dimensional dense feature vector Q obtained by the embedding layer first passes through a Multi-Head orientation layer in the interest extraction layer in the transform session, and the Multi-Head orientation can capture the relation of the items in different representation spaces. The Multi-Head Attenttion first projects vector Q linearly into h subspaces (h being the number of heads), then applies h Attention functions in parallel to produce an output list, the output of the ith Head in the kth session being:
headki＝Attention(QkiWi Q,QkiWi K,QkiWi V)
wherein the content of the first and second substances,
for a linear matrix, the Attention function in Multi-Head Attention is the scaled dot product Attention:
then, to impart a model non-linear relationship, the present embodiment connects the output structures of the different heads and inputs it into a feed-forward network:
FFN(x)＝ReLu(xW(1)+b(1))+W(2)+b(2)
where S is the total number of items in session k and ReLU (. cndot.) is a non-linear activation function.
In summary, the session interest of the kth session can be expressed as:
wherein Avg (. cndot.) is the average pooling.
In addition, the present embodiment uses residual concatenation to reduce the complexity of the model and thus improve the performance of the attention mechanism.
After passing through the above-mentioned interest extraction layer in the transform session, the interaction sequence of the user can be further expressed as I ═ { I ═ I1,I2,……Ik}。
The transform intra-session interest extraction layer based on the multi-head self-attention mechanism can not only extract the interest of the user in each session, but also capture the intrinsic connection between user behaviors in the same session. In order to give the model non-linear characteristics, after connecting vectors of multiple heads from different heads of attention, the connecting result is fed into a feedforward network.
After the interaction sequence of the user is divided into different sessions, the interest of the user in each session is extracted by using an interest extraction layer in the multi-head self-attention-based transform session. Compared with the traditional modeling of interactive sequences through an RNN structure, the relevance of the interest extraction layer in the multi-head self-attention-based Transformer session does not depend on the distance and the distance of the current item and other items regardless of the spatial distance of the current item and other items, so that the sequence items can be better modeled. Meanwhile, the attention mechanism is only calculated by simple matrix multiplication, so that the calculation speed is higher, and the parallelization processing is easier.
And S105, interest interaction between the conversations.
Although the interests of the users are different in different sessions, the different interests hidden in different sessions are mutually influenced and correlated, for example, after the users buy the mobile phones in the previous session behaviors, the users are likely to browse the mobile phone shells next, so the embodiment extracts the interest in i sessions from step 103 and uses the Bi-GRU session interest interaction layer to model the dynamic interaction and conversion relationship among different sessions so as to capture the interaction and evolution of the interests of the users in different historical sessions.
GRUs, as variants of RNN and LSTM, have significant advantages in sequence modeling due to simpler structure. The formula for the GRU is as follows:
where W represents the transition matrix, b represents the offset,
representing a product operation.
Here, in order to more accurately consider the influence of the context on the session, the present embodiment uses a Bi-GRU session interest interaction layer, whose expression is:
wherein the content of the first and second substances,
is a hidden state of the forward GRU,is a hidden layer state of the backward GRU,
are connection symbols.
And S106, activating.
Specifically, different session interests have different effects on whether the user clicks on the target item, so the user assigns different weights to the different session interests. The attention mechanism has been proven to be an effective weight allocation mechanism, so the embodiment utilizes the attention-based activation layer to assign different weights to different session interests so as to improve the accuracy of western region recommendation.
In this embodiment, the expression of the session interest weight is:
wherein α is a weighting factor that determines which session interest needs to be emphasized or ignored, UkAnd outputting the hidden layer vector after passing through the Bi-GRU session interest interaction layer.
After passing through the above interest interaction layer between Bi-GRU sessions, the interaction sequence of the user may be further expressed as U ═ U1,U2,……Uk}。
S107, predicting a target item sequence to be recommended.
Specifically, the interaction sequence I of the user obtained in step 104 and the interaction sequence U of the user obtained in step 106 are accessed to a prediction layer, and an n-dimensional representation of a next target item sequence to be recommended is obtained through prediction.
According to the method, different weights are given to the extracted session interests, the method not only considers the interests of the user in each session, but also fuses the session interests of the user with the context information, so that the method has expressive force and greatly improves the recommendation performance.
Example two
Fig. 4 is a block diagram of an interest recommendation system based on a user sequence click behavior according to the present embodiment. As shown in fig. 4, the system includes:
the data acquisition module is used for acquiring historical interactive project data of the user to form a historical interactive project sequence of the user;
the model construction module is used for constructing an interest recommendation model;
the session division module is used for carrying out session division on the historical interactive item sequence of the user by utilizing the interest recommendation model;
the intra-session interest extraction module is used for extracting each intra-session interest obtained after division;
the activation module is used for endowing different weights to the interests in each session to obtain a session interest sequence of the user;
the inter-session interest interaction module is used for interacting the interests among different sessions to obtain a dynamic interaction model among the different sessions;
and the prediction module is used for inputting the session interest sequence of the user into a dynamic interaction model among different sessions to predict and obtain a target item sequence to be recommended.
EXAMPLE III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring historical interactive project data of a user to form a historical interactive project sequence of the user;
constructing an interest recommendation model;
carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model;
extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user;
interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations;
and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
Example four
The present embodiment provides a processing apparatus, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the following steps:
acquiring historical interactive project data of a user to form a historical interactive project sequence of the user;
constructing an interest recommendation model;
carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model;
extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user;
interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations;
and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. An interest recommendation method based on user sequence click behaviors is characterized by comprising the following steps:
acquiring historical interactive project data of a user to form a historical interactive project sequence of the user;
constructing an interest recommendation model;
carrying out session division on the historical interactive item sequence of the user by using an interest recommendation model;
extracting the interests in each session obtained after the division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user;
interacting the interests among different conversations to obtain a dynamic interaction model among the different conversations;
and inputting the session interest sequence of the user into a dynamic interaction model among different sessions, and predicting to obtain a target item sequence to be recommended.
2. The method of claim 1, wherein the interest recommendation model comprises a session division layer, an embedding layer, an intra-session interest extraction layer, an inter-session interest interaction layer, an activation layer, and a prediction layer.
3. The interest recommendation method based on user sequence click behavior as claimed in claim 1, wherein the step of session partitioning the historical interactive item sequence of the user comprises:
inputting the obtained historical interactive item sequence of the user into a session division layer of an interest recommendation model, dividing the items in the historical interactive item sequence of the user into a plurality of different sessions through the session division layer according to different time spans, wherein each item in the sessions has three identifiers which are respectively a sequence characteristic identifier of the item, a position identifier of the item in a user click sequence and a position identifier of the item in the session;
and inputting the obtained features of the items in the conversation into an embedding layer of the interest recommendation model, and obtaining the low-dimensional features of the items in the conversation through the embedding layer.
4. The interest recommendation method based on user sequence click behavior according to claim 1, wherein the step of extracting the interest in each session obtained after the division comprises:
inputting the low-dimensional feature vector of the item in the conversation output by the embedding layer of the interest recommendation model into the interest extraction layer in the conversation of the interest recommendation model;
and extracting the interest of the user in each session through an intra-session interest extraction layer.
5. The method of claim 4, wherein the step of weighting the interests in each session comprises:
and inputting the interest in each conversation into an activation layer of the interest recommendation model, and giving different weights to different conversation interests by using the activation layer to obtain a conversation interest sequence of the user.
6. The interest recommendation method based on user sequence click behavior as claimed in claim 1, wherein the weight of the session interest is:
wherein α is a weight factor, IkW is the session interest of the kth session and is the weight coefficient.
7. The method of claim 1, wherein the step of interacting interests between different sessions comprises:
inputting the extracted interest of the user in each session into a session interest interaction layer of an interest recommendation model;
and modeling the dynamic interaction and conversion relation among different conversations by utilizing the conversation interest interaction layer.
8. An interest recommendation system based on user sequence click behaviors is characterized by comprising:
the data acquisition module is used for acquiring historical interactive project data of the user to form a historical interactive project sequence of the user;
the model construction module is used for constructing an interest recommendation model;
the session division module is used for carrying out session division on the historical interactive item sequence of the user by utilizing the interest recommendation model;
the intra-session interest extraction module is used for extracting intra-session interests obtained after division;
the activation module is used for endowing different weights to the interests in each session to obtain a session interest sequence of the user;
the inter-session interest interaction module is used for interacting the interests among different sessions to obtain a dynamic interaction model among the different sessions;
and the prediction module is used for inputting the session interest sequence of the user into a dynamic interaction model among different sessions to predict and obtain a target item sequence to be recommended.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for interest recommendation based on user sequence click behavior according to any one of claims 1 to 7.
10. A processing apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for interest recommendation based on user sequence click behavior according to any of claims 1-7 when executing the program.
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