CN114862496A - Session recommendation method, device and medium based on user personalized modeling - Google Patents
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Abstract
The invention discloses a conversation recommendation method, a device and a medium based on user personalized modeling, wherein the method comprises the following steps: acquiring a session recommendation data set; modeling the session data set after data enhancement into a sequence structure, and inputting the sequence structure into an RNN (radio network) to obtain a preliminary session vectorization representation data set; extracting user intentions to obtain session vectorization representation expressing the multiple intentions of the user; extracting global information and local information represented by session vectorization; fusing the extracted information to obtain final session vectorization representation; and calculating the final session vectorization representation, calculating cosine similarity with all the articles in the session data set, and taking the article with the highest similarity as a recommendation result. The invention can effectively capture the consumption patterns of similar users, so that a new recommendation algorithm can more effectively predict the real requirements of the users, thereby giving more accurate recommendation results, and can be widely applied to the technical fields of artificial intelligence, deep learning and recommendation systems.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, deep learning and recommendation systems, in particular to a conversation recommendation method, device and medium based on user personalized modeling.
Background
With the rapid increase of the information amount on the internet, it is very difficult for the vast users to find the required information from the huge information sea; for a network platform providing services, the ultimate aim is to provide contents which are interested by users, and the users are retained to the maximum extent, so that the platform benefits are improved. Recommendation systems, in turn, have become the basis for helping users alleviate information overload problems and select information of interest in many Web applications, such as search, e-commerce, and streaming media sites. Conversational recommendation is a special field in recommendation systems, most existing recommendation systems assume constant recording of user profiles and past activities. However, in many services, the user identity may be unknown and only a history of user behavior during an ongoing session is available. Therefore, it is important to model the limited behavior in one session and generate recommendations accordingly. In contrast, in this case, conventional recommendation methods that rely on sufficient user-item interactions are problematic in producing accurate conversational recommendation results. However, by introducing a deep learning technology, a large number of user behaviors are analyzed and modeled, and a recommendation result meeting the requirements of a user and a platform can be given according to a plurality of interactions of the user under the condition that user information is anonymous.
In the existing conversation recommendation technology, serialized problems are converted into graph problems, all conversation sequences are modeled through a directed graph, then a hidden vector representation of each commodity is learned through a graph neural network, and further a short-term interest of a user is captured through an attention network architecture model, so that the purpose of capturing the vector representation of the coexistence of long-term and short-term interests is achieved. The technical characteristics include: (1) preprocessing the session data, and modeling the session data into data with a graph structure; (2) modeling the session data by using a GNN model to obtain a session vector representation; (3) and performing similarity calculation by using the final conversation expression vector and the commodity vector to obtain a recommendation result. The disadvantages of this technique are: (1) because the conversation data is built into a graph, the time sequence information of the sequence is lost to a certain extent; (2) the sequences are directly connected into a graph structure to represent, and modeling of high-order interaction information in the sequences is too simple.
In another existing session recommendation technology, a recurrent neural network is used to model session data in a time sequence manner, and then an attention mechanism is used to model information in the sequence, so as to obtain a vector representation of a session. The technical characteristics include: (1) and a cyclic neural network is used for modeling the commodity sequence in time sequence, so that more complete conversation vector representation can be obtained. (2) The importance of the last item in the sequence to the user's intent is heavily considered. The disadvantages of this technique are: (1) the high level of user intent is not considered; (2) the extraction of the user intention is too simple, and the diversity of the user intention is not considered.
Disclosure of Invention
In order to solve at least one of the technical problems in the prior art to a certain extent, the invention aims to provide a conversation recommendation method, a conversation recommendation device and a conversation recommendation medium based on user personalized modeling.
The technical scheme adopted by the invention is as follows:
a conversation recommendation method based on user personalized modeling comprises the following steps:
acquiring a session data set of a user on an e-commerce platform, wherein the session data set comprises article information, a user purchase timestamp and a user purchase behavior;
performing data enhancement on the session data set;
modeling the session data set after data enhancement into a sequence structure, and inputting the sequence structure into an RNN (radio network) to obtain a preliminary session vectorization representation data set;
storing the sessions in the preliminary session vectorization representation dataset in a historical session vectorization representation memory;
extracting user intention by using a multi-intention network to obtain a session vectorization representation h expressing the multi-intention of the user interest ；
Using Transformer netsNetwork-to-session vectorized representation h interest Carrying out global information modeling to obtain session vectorization representation h for representing global information global ；
Vectorizing a session using a Convolition network to represent h interest Modeling local information to obtain session vectorization representation h for representing local information local ；
Representing a global session vector as h global And local session vector representation h local Connecting the conversation vectorization representation through a fusion layer to obtain a final conversation vectorization representation;
and calculating the final session vectorization representation, calculating cosine similarity with all the articles in the session data set, and taking the article with the highest similarity as a recommendation result.
Further, after obtaining the session data set, the method further includes a step of preprocessing the session data set, including:
sequencing the conversation data according to the time stamp to obtain a conversation sequence which is sequenced according to time, removing the conversation which is clicked once, and deleting the click item with too low frequency of occurrence;
wherein, each item of the session data comprises the serial number item _ id of the commodity and the time stamp of the commodity being clicked.
Further, the sorting the session data according to the time stamp to obtain a time-sorted session sequence includes:
acquiring N session data from the session data set, wherein s represents a session, v represents a clicked commodity item, all commodity items v in the session are sorted according to the time stamp _ stamp of the clicked commodity, and a session sequence can be represented as s ═ s<v 1 ,v 2 ,v 3 …v c >Wherein the subscript c of v is used for distinguishing the order of clicking the commodities.
Further, the vectorizing of the session using the Transformer network represents h interest Carrying out global information modeling to obtain session vectorization representation h for representing global information global The method comprises the following steps:
will extract the user intentionModeling the session vectorization representation after the graph into a sequence structure, inputting the sequence structure into a transform network, and obtaining the vectorization representation of each commodity in the session sequence after training; vectorization representation of each node in the conversation sequence represents vector representation X of one commodity after the characteristics of other commodities in the sequence are integrated<x 1 ,x 2 ,x 3 …x n >；
The vectorization representation of all commodity nodes in the conversation sequence is averaged to obtain the vectorization representation of each conversation, and the conversation s is equal to the conversation s<v 1 ,v 2 ,v 3 …v n >Wherein n is the number of commodities contained in the conversation s, and the global vectorization of the conversation is represented as h global 。
Further, the vectorizing of the session using the Convolition network represents h interest Modeling local information to obtain session vectorization expression h for expressing local information local The method comprises the following steps:
modeling the session vectorization representation after the user intention is extracted into a sequence structure, inputting the sequence structure into a Convolition network, and obtaining the vectorization representation of each commodity in the session sequence after training; vectorization representation of each node in the conversation sequence represents vector representation Y of one commodity after the characteristics of other commodities in the sequence are integrated<y 1 ,y 2 ,y 3 …y n >；
Vectorizing all commodity nodes in the conversation sequence, taking the vector of the last commodity as the vectorizing representation of the conversation, and regarding the conversation sequence s as the vectorizing representation of the conversation<v 1 ,v 2 ,v 3 …v n >Wherein n is the number of commodities contained in the conversation s, and the local representation of the conversation is represented by vectorization h local 。
Further, the global session vector is represented as h global And local session vector representation h local Connecting together through a fusion layer to obtain a final session vectorization representation, comprising:
representing a global session vector as h global And local session vector representation h local The sessions in the set are stored in a historical session vectorization representation memory, and a final session vectorization representation set is obtained and recorded as M;
wherein the content of the first and second substances,
p represents the number of sessions in the user's purchase session data set, t represents the session length,
indicating a session vectorized representation of length j after the above-described data enhancement at the ith session.
Further, the expression of the final session vectorization representation z is as follows:
z＝αh local +(1-a)h global
wherein the parameter α ═ σ (W) 1 h local +W 2 h global ) σ stands for sigmoid function, W 1 ,W 2 Are model parameters that can be trained.
Further, the cosine similarity is calculated as follows:
Value＝softmax(z T x)
wherein z is final session vectorization representation, x is article vectorization representation, and T represents vector transposition; the K items with the highest scores form the final recommended item list.
The invention adopts another technical scheme that:
a conversation recommendation device based on user personalized modeling comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a computer readable storage medium, in which a program executable by a processor is stored, the program executable by the processor being for performing the method as described above when executed by the processor.
The invention has the beneficial effects that: the method and the device can effectively capture the consumption modes of similar users by representing and storing the historical sessions of the historical purchasing behaviors of the users and using the historical purchasing sessions as the collaborative information, and simultaneously, when the current users are recommended, the users are respectively modeled by using different networks by considering various intentions of the users in the session sequences and the locality and the globality of the behavior modes in the current session sequences, so that the new recommendation algorithm can more effectively predict the real requirements of the users, and more accurate recommendation results are provided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of a method for recommending a session based on user-customized modeling according to an embodiment of the present invention;
fig. 2 is a general flowchart of a session recommendation method based on user personalized modeling according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
As shown in fig. 1 and fig. 2, the present embodiment provides a session recommendation method, which uses historical purchase information as collaborative information, and can effectively model consumption behaviors of a user, and when recommending is performed for the user, the user behaviors having the same preference as that of the user in a historical session are considered, so that a recommendation algorithm can more effectively predict a real consumption intention of the user, and a more accurate recommendation result is provided. The method specifically comprises the following steps:
and S1, acquiring a conversation data set of the user on the E-commerce platform, wherein the conversation data set comprises item information, a user purchase time stamp and user purchase behaviors.
Specifically, step S1 is as follows: and obtaining a session data set containing item category information, user purchasing time and user purchasing behavior from the public data platform, wherein each item in the session data further comprises a serial number item _ id of the commodity and a time stamp time _ stamp of the commodity when the commodity is clicked.
And S2, preprocessing the session data set. The method comprises the following steps: and sequencing the session data on a timestamp to obtain a session sequence which is sequenced according to time, removing sessions which are clicked once and deleting click items with too low occurrence frequency.
Wherein the step S2 specifically includes steps S21-S22:
s21, obtaining N session data from the session data set, S representing the session, v representing the purchased commodity item, and sorting all commodity items v in the session according to the time stamp time _ stamp of the purchased commodity, wherein a session sequence is represented as S ═ S<v 1 ,v 2 ,v 3 ,…,v t >Wherein the subscript of v represents the click sequence, and t represents the current time;
s22, further pre-processing the sessions obtained after the pre-processing in step S21, in this embodiment, sessions appearing less than 5 times in the whole session database are removed, and then sessions with a session length less than 2 are removed.
And S3, dividing the preprocessed session data set into a training data set and a testing data set according to the time stamp, and respectively using the training data set and the testing data set as model training and testing.
As an optional manner, step S3 is specifically as follows: in the preprocessed session data set, the training set and the test set are divided according to the time stamp, for example, for the session s ═<v 1 ,v 2 ,v 3 ,v 4 ,v 5 >After division, the training set is<v 1 ,v 2 ,v 3 >The label is<v 2 ,v 3 ,v 4 >(ii) a The test set is<v 1 ,v 2 ,v 3 ,v 4 >The label is< 5 >。
And S4, performing data enhancement on the session data set by performing fragment type interception on each data.
As an optional manner, step S4 is specifically as follows: performing data enhancement on the training set obtained in the step S3 for the purpose ofIs to extend the data set in such a way that the sequence prefix is intercepted, for example for a session s 1 ＝<v 1 ,v 2 ,v 3 ,v 4 ,v 5 >After data enhancement, 4 conversation sequences are obtained<v 1 ,v 2 >、<v 1 ,v 2 ,v 3 >、<v 1 ,v 2 ,v 3 ,v 4 >、<v 1 ,v 2 ,v 3 ,v 4 ,v 5 >。
And S5, modeling the purchase data set after the data enhancement into a sequence structure, and then inputting the sequence structure into the RNN to obtain a preliminary session vectorization representation data set.
And S6, storing the sessions in the preliminary session vectorization representation data set in a historical session vectorization representation memory.
S7, for the conversation to be recommended, extracting multi-level intentions of the user by using the multi-intention attention layer, and generating h interest 。
S8, extracting local information by using Convolition network to obtain vectorization representation h representing sequence local intention local 。
Modeling the session data set after the user intention is extracted into a sequence structure, inputting the sequence structure into a Convolation network, extracting local information, and obtaining a session vectorization representation data set Y, wherein the method specifically comprises the following steps:
modeling the session vectorization representation after the user intention is extracted into a sequence structure, inputting the sequence structure into a Convolition network, and obtaining the vectorization representation of each commodity in the session sequence after training; vectorization representation of each node in the conversation sequence represents vector representation Y of one commodity after the characteristics of other commodities in the sequence are integrated<y 1 ,y 2 ,y 3 …y n >；
Vectorizing all commodity nodes in the conversation sequence, taking the vector of the last commodity as the vectorizing representation of the conversation, and regarding the conversation sequence s as the vectorizing representation of the conversation<v 1 ,v 2 ,v 3 …v n >Wherein n is the number of commodities contained in the conversation s, and a local list of the conversationVectorized representation h local 。
S9, extracting global information by using a Transformer network to obtain a vectorization representation h representing the global intention of the sequence global 。
Modeling the session data set with the user intention extracted as a sequence structure, inputting the sequence structure into a transform network, extracting global information, and obtaining a session vectorization representation data set X, wherein the method specifically comprises the following steps:
modeling the session vectorization representation after the user intention is extracted into a sequence structure, inputting the sequence structure into a Transformer network, and obtaining the vectorization representation of each commodity in the session sequence after training; vectorization representation of each node in the conversation sequence represents vector representation X of one commodity after the characteristics of other commodities in the sequence are integrated<x 1 ,x 2 ,x 3 …x n >；
The vectorization representation of all commodity nodes in the conversation sequence is averaged to obtain the vectorization representation of each conversation, and the conversation s is equal to the conversation s<v 1 ,v 2 ,v 3 …v n >Wherein n is the number of commodities contained in the conversation s, and the global vectorization of the conversation is represented as h global 。
S10, representing the local session vector as h local And global session vector representation h global And connecting the two layers together through a fusion layer to obtain the final session vectorization representation.
Storing the sessions in the two session vectorization representation data sets in step S8 and step S9 in the history session vectorization representation memory, including:
storing a session vectorized representation of user consumption behaviour in said preliminary session vectorized representation dataset, denoted M,
where p represents the number of sessions a user purchases in the session data set, t represents the length of the session,
indicating a session vectorized representation of length j after the above-described data enhancement at the ith session.
Connecting the current session vectorization representation and the neighborhood session vectorization representation to obtain a final session vectorization representation z:
z＝αh local +(1-a)h global
wherein the parameter α ═ σ (W) 1 h local +W 2 h global ) σ stands for sigmoid function, W 1 ,W 2 Are model parameters that can be trained.
And S11, calculating cosine similarity between the final session vectorization representation and all the items in the session data set, and taking the item with the highest similarity as a recommendation result.
After the final session vectorization representation is obtained, calculating a similarity score value of the final session vectorization representation and the article vectorization representation X as a recommendation basis, wherein the similarity score value is calculated as follows:
Value＝softmax(z T x)
wherein T represents a vector transpose; the K items with the highest scores form the final recommended item list.
In summary, compared with the prior art, the method of the present embodiment has the following advantages and beneficial effects:
the method comprises the steps that historical conversation of historical purchasing behavior of a user is represented and stored, the intention which is possibly expressed when the user purchases commodities is considered, and a multi-intention extraction module is designed to carry out multi-level extraction on the intention of the user; local characteristics and global characteristics in the commodity sequence are considered, corresponding networks are designed for modeling respectively, so that the real requirements of the user can be predicted more effectively by the new recommendation algorithm, and a more accurate recommendation result is provided.
The embodiment also provides a session recommendation device based on user personalized modeling, which includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 1.
The session recommendation device based on the user personalized modeling according to the embodiment of the invention can execute the session recommendation method based on the user personalized modeling provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
The embodiment also provides a storage medium, which stores an instruction or a program capable of executing the session recommendation method based on the user personalized modeling provided by the embodiment of the method of the present invention, and when the instruction or the program is executed, the steps can be implemented by any combination of the embodiments of the method, and the method has corresponding functions and advantages.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A conversation recommendation method based on user personalized modeling is characterized by comprising the following steps:
acquiring a session data set of a user on an e-commerce platform, wherein the session data set comprises article information, a user purchase timestamp and a user purchase behavior;
performing data enhancement on the session data set;
modeling the session data set after data enhancement into a sequence structure, and inputting the sequence structure into an RNN (radio network) to obtain a preliminary session vectorization representation data set;
storing the sessions in the preliminary session vectorization representation dataset in a historical session vectorization representation memory;
extracting user intention by using a multi-intention network to obtain a session vectorization representation h expressing the multi-intention of the user interest ；
Vectorized representation of a session using a Transformer network h interest Carrying out global information modeling to obtain session vectorization representation h for representing global information global ；
Vectorizing a session using a Convolition network to represent h interest Modeling local information to obtain session vectorization representation h for representing local information local ；
Representing a global session vector by h global And local session vector representation h local Connecting the conversation vectorization representation through a fusion layer to obtain a final conversation vectorization representation;
and calculating the final session vectorization representation, calculating cosine similarity with all the articles in the session data set, and taking the article with the highest similarity as a recommendation result.
2. The conversation recommendation method based on user personalized modeling according to claim 1, further comprising a step of preprocessing the conversation data set after obtaining the conversation data set, comprising:
sequencing the conversation data according to the time stamp to obtain a conversation sequence which is sequenced according to time, removing the conversation which is clicked once, and deleting the click item with too low frequency of occurrence;
wherein, each item of the session data comprises the serial number item _ id of the commodity and the time stamp of the commodity being clicked.
3. The conversation recommendation method based on the user personalized modeling according to claim 2, wherein the sorting the conversation data according to the time stamp to obtain a time-sorted conversation sequence comprises:
obtaining N conversation data from the conversation data set, using s to represent the conversation, using v to represent the clicked commodity item, sorting all commodity items v in the conversation according to the time stamp of the clicked commodity, and then a conversation sequence can be represented as s ═ v ≦ v 1 ，v 2 ，v 3 ...v c Where the subscript c of v is used to distinguish the order of the click of the item.
4. User-based personalized modeling according to claim 1The session recommendation method of (1), wherein the vectorization representation of the session using a Transformer network is h interest Carrying out global information modeling to obtain session vectorization representation h for representing global information global The method comprises the following steps:
modeling the session vectorization representation after the user intention is extracted into a sequence structure, inputting the sequence structure into a Transformer network, and obtaining the vectorization representation of each commodity in the session sequence after training; the vectorized representation of each node in the conversation sequence represents the vector representation of one commodity after integrating the characteristics of other commodities in the sequence
X＝＜x 1 ，x 2 ，x 3 ...x n ＞；
The vectorization representation of all commodity nodes in the conversation sequence is averaged to obtain the vectorization representation of each conversation, and the conversation s is equal to and less than v 1 ，v 2 ，v 3 ...v n >. where n is the number of commodities contained in a session s, the global vectorization of the session is represented as h global 。
5. The method according to claim 1, wherein the session vectorization representation h using the Convolition network is represented by a conversation vectorization network interest Modeling local information to obtain session vectorization representation h for representing local information local The method comprises the following steps:
modeling the session vectorization representation after the user intention is extracted into a sequence structure, inputting the sequence structure into a Convolition network, and obtaining the vectorization representation of each commodity in the session sequence after training; the vectorized representation of each node in the conversation sequence represents the vector representation of one commodity after integrating the characteristics of other commodities in the sequence
Y＝＜y 1 ，y 2 ，y 3 ...y n ＞；
Vectorizing all commodity nodes in the conversation sequence, taking the vector of the last commodity as the vectorizing representation of the conversation, and regarding the conversation sequence s ═ v 1 ，v 2 ，v 3 ...v n >. n is the number of commodities contained in a session s, and the local representation of the session is vectorized to be represented by h local 。
6. The method of claim 1, wherein the global session vector is represented as h global And local session vector representation h local Connecting together through a fusion layer to obtain a final session vectorization representation, comprising:
representing a global session vector as h global And local session vector representation h local The sessions in the set are stored in a historical session vectorization representation memory, and a final session vectorization representation set is obtained and recorded as M;
wherein the content of the first and second substances,
p represents the number of sessions in the user's purchase session data set, t represents the session length,
indicating a session vectorized representation of length j after the above-described data enhancement at the ith session.
7. The method of claim 1, wherein the final session vectorization representation z comprises the following expression:
z＝αh local +(1-a)h global
wherein the parameter α ═ σ (W) 1 h local +W 2 h aglobal ) σ denotes sigmoid function, W 1 ，W 2 Are model parameters that can be trained.
8. The conversation recommendation method based on the user personalized modeling according to claim 1, wherein the cosine similarity is calculated as follows:
Value＝softmax(z T x)
wherein z is final session vectorization representation, x is article vectorization representation, and T represents vector transposition; the K items with the highest scores form the final recommended item list.
9. A conversation recommendation device based on user personalized modeling is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-8.
10. A computer-readable storage medium, in which a program executable by a processor is stored, wherein the program executable by the processor is adapted to perform the method according to any one of claims 1 to 8 when executed by the processor.
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