CN107832433B - Information recommendation method, device, server and storage medium based on conversation interaction - Google Patents
Information recommendation method, device, server and storage medium based on conversation interaction Download PDFInfo
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Abstract
The embodiment of the invention discloses an information recommendation method, device, server and storage medium based on conversation interaction. The method comprises the following steps: generating reading subject inquiry information according to the user behavior characteristics of the historical reading interface and/or a user model, wherein the user model comprises at least one of the following: user interests, user interest topics and user interest authors; carrying out dialogue interaction with a user according to the generated reading subject inquiry information; determining the reading subject of the user according to the conversation interactive content; and pushing the information matched with the reading subject to the user. According to the embodiment of the invention, through natural language understanding and artificial intelligence technologies based on conversation interaction and the like, different topic information streams are connected, so that a user is helped to explore potential interests, the time length and the viscosity of the user are improved, a user model is more accurately constructed and controlled, and the user experience is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of information interaction, in particular to an information recommendation method, device, server and storage medium based on conversation interaction.
Background
With the development of science and technology and the continuous increase of internet information, the requirement of users on internet recommendation information is higher and higher.
At present, information recommendation modes of the mobile internet are mainly divided into two types, one is to build a user model for recommendation by mining interested tags through user behaviors, and the other is to recommend by actively paying attention to interested people or topics through users. The two information recommendation modes have certain problems, the first type of user model is obtained by indirect user behavior mining, so the recommendation accuracy is not very good, and in addition, the user model is usually implicit, so that the user cannot actively set or directly modify the user model, new interesting tags of the user cannot be added in time, outdated or wrong interesting tags cannot be deleted in time, and the user experience is influenced; the second category is that the user needs to actively initiate attention, so that the potential (unfocused) interests of the user are difficult to mine, and the using duration and the viscosity of the user are not improved.
Disclosure of Invention
The embodiment of the invention provides an information recommendation method, device, server and storage medium based on conversation interaction, which can improve the user duration and stickiness, more accurately construct and control a user model and improve the user experience.
In a first aspect, an embodiment of the present invention provides an information recommendation method based on dialog interaction, including:
generating reading subject inquiry information according to the user behavior characteristics of the historical reading interface and/or a user model, wherein the user model comprises at least one of the following: user interests, user interest topics and user interest authors;
carrying out dialogue interaction with a user according to the generated reading subject inquiry information;
determining the reading subject of the user according to the conversation interactive content;
and pushing the information matched with the reading subject to the user.
In a second aspect, an embodiment of the present invention further provides an information recommendation apparatus based on dialog interaction, including:
the query information acquisition module is used for generating reading subject query information according to the user behavior characteristics and/or the user model of the historical reading interface, and the user model comprises at least one of the following: user interests, user interest topics and user interest authors;
the dialogue interaction module is used for carrying out dialogue interaction with the user according to the generated reading subject inquiry information;
the reading theme module is used for determining the reading theme of the user according to the conversation interactive content;
and the pushing module is used for pushing the information matched with the reading subject to the user.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a dialog interaction-based information recommendation method as described above.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the information recommendation method based on dialog interaction as described above.
According to the embodiment of the invention, reading subject inquiry information is generated according to the user behavior characteristics and/or the user model of the historical reading interface, conversation interaction is carried out with the user according to the generated reading subject inquiry information, the reading subject of the user is determined according to conversation interaction content, and information matched with the reading subject is pushed to the user. According to the embodiment of the invention, different topic information streams are connected through artificial intelligence technologies such as natural language understanding and dialogue interaction, so that the method helps a user to explore potential interests, improves the duration and viscosity of the user, more accurately constructs and controls a user model, and improves user experience.
Drawings
Fig. 1 is a flowchart of an information recommendation method based on dialog interaction according to a first embodiment of the present invention;
fig. 2 is a flowchart of an information recommendation method based on dialog interaction according to a second embodiment of the present invention;
fig. 3 is a strong interaction form interface diagram of an information recommendation method based on dialog interaction in the second embodiment of the present invention;
fig. 4 is a weak interaction form interface diagram of an information recommendation method based on dialog interaction in the second embodiment of the present invention;
fig. 5 is a schematic structural diagram of an information recommendation device based on dialogue interaction according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of a server in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of an information recommendation method based on dialog interaction in an embodiment of the present invention, where this embodiment is applicable to the case of information recommendation, and the method may be executed by an information recommendation method based on dialog interaction, and the apparatus may be implemented in a software and/or hardware manner, for example, the apparatus may be configured in a server. As shown in fig. 1, the method may specifically include:
In this embodiment, the user behavior feature of the history reading interface is a behavior habit and a law of a user access interface obtained by statistically analyzing behavior data of the user on the history reading interface, for example, the user behavior feature may be the number of interest tags included in a clicked article or the number of the first three interest tags included in the clicked article, which is the largest. The reading subject inquiry information is a suitable related inquiry statement generated according to the reading subjects which are accessed more or interested by the historical users and obtained according to the user behavior characteristics and/or the user model.
And step 120, carrying out dialogue interaction with the user according to the generated reading subject inquiry information.
Specifically, the dialogue interaction with the user is implemented by a natural language understanding technique, a query sentence is transmitted to the user, the user answers the query, and a new query sentence is proposed according to the answer of the user or the subsequent step 130 is performed, thereby completing the dialogue interaction. The method comprises the steps of scanning words from left to right for a sentence answered by a user, recognizing the word meaning and the usage of each word according to a dictionary, determining the combination of a phrase and the sentence according to a syntactic rule, acquiring the meaning of an input sentence according to a semantic rule and an inference rule, inquiring a knowledge base, and organizing response output according to subject knowledge and a sentence generation rule, wherein the words, the syntactic rule, the semantic rule, the inference rule and the subject knowledge are stored in advance.
Illustratively, if a query sentence "want to know what big news is today? "if the user answers" good ", a new query sentence" want to know news about the amusement park "is proposed according to the user's answer? ", the user continues to answer.
It should be noted that candidate information options can be generated according to the reading subject query message for the user to select, and the user can also answer the query by selecting candidate information, wherein the candidate information options are pictures or links of related information obtained according to the reading subject query message, such as "information 1", "information 2" and "information 3" in fig. 3.
And step 130, determining the reading subject of the user according to the conversation interactive content.
Specifically, through the answer content of the user in the dialogue interaction with the user, the information which the user wants to know currently can be determined, and the current reading subject of the user can be determined according to the information, wherein the reading subject refers to the main reading content, and the reading subject can be a conference, an event, a sport, a person and the like, such as an Olympic conference.
Specifically, various preset tags of the reading subject can be constructed in advance, corresponding tags of the reading subject are added to the information, the reading subject is matched with the tags of the information, and the information which is successfully matched is determined as the information matched with the reading subject. In addition, the matching degree of the reading subject and each piece of information can be determined according to the reading subject and the subject key words contained in each piece of information, and the information with the matching degree meeting the condition is determined as the information matched with the reading subject.
According to the embodiment of the invention, reading subject inquiry information is generated according to the user behavior characteristics and/or the user model of the historical reading interface, conversation interaction is carried out with the user according to the generated reading subject inquiry information, the reading subject of the user is determined according to conversation interaction content, and information matched with the reading subject is pushed to the user. According to the embodiment of the invention, different topic information streams are connected through natural language understanding and artificial intelligence technologies based on conversation interaction and the like, so that the method and the device can help the user to explore potential interests, improve the duration and viscosity of the user, more accurately construct and control a user model and improve user experience.
Illustratively, 110 may include: determining the topic currently concerned by the user according to the user behavior characteristics and/or the user model of the historical reading interface; and generating inquiry information for reminding reading of updated contents according to the updating condition of the topic currently concerned by the user. Specifically, in the process of a user browsing a history interface, some topics of content may be relatively concerned by the user, the topics concerned by the user are determined according to user behavior characteristics and/or a user model of the history reading interface, and if the concerned topics are updated, query information for reminding the user to read the updated content is generated, for example, "there is a new choice article on the topic concerned by you, and it is to see now? ".
Further, after the above-mentioned determining the topic currently focused on by the user, the method may further include: providing more information of the current interest topic of the user for the user to select according to the user behavior characteristics of the historical reading interface; and providing information of related topics of the current topics of interest of the user for the user to select according to the user behavior characteristics of the historical reading interface. Specifically, according to the behavior characteristics of the user history reading interface, more information of the current topic of interest and related topics is recommended from the direction of depth and breadth, and the generated query sentence is as follows, for example, "do you want to know deeply about which topics about B? "or" recommend B related more topics for you, "etc.
Illustratively, step 120 may be followed by: determining the scale and/or the tone of the user according to the interactive content of the dialog of the user; and determining the matched information of the user according to the scale label and/or the tone label added in the information in advance. Specifically, the measure of the scale and/or the tone of the user is preset words capable of expressing the scale and/or the tone level, such as "horror", "low level", and the like, which can be set by the user according to needs. Classifying and extracting texts of user conversation interactive contents, comparing the extracted words with preset words, and determining the most relevant words as the scale and/or the style of the user. Then, the words representing the user scale and/or tone are compared with scale labels and/or tone labels added in advance in the information, and the most relevant information is determined as the information matched with the user.
Illustratively, after step 140, the method may further include: and if the user closes the previous reading interface and does not have reading action on the previous reading interface, generating the feedback inquiry information of the previous reading interface. Specifically, if the user closes without any action on the previous reading interface, a query sentence for feedback is generated, for example, "do you satisfy above recommended? "," is you unsatisfied with the recommendations above? "or" where you are not satisfied with the recommendation? "and the like. The behavior of the last reading interface comprises the number of clicked articles, the number of interest tags contained in the clicked articles and existing in the user model, the number of interest tags contained in the clicked articles and not existing in the user model, the number of the first three interest tags contained in the clicked articles and/or the proportion of the first three interest tags contained in the clicked articles and having the largest number to all tags and the like.
Example two
Fig. 2 is a flowchart of an information recommendation method based on dialog interaction in the second embodiment of the present invention. On the basis of the above embodiments, the present embodiment further optimizes the information recommendation method based on the dialog interaction. Correspondingly, as shown in fig. 2, the method of the embodiment specifically includes:
and step 211, determining the current scene type of the user according to the user behavior characteristics and/or the user model of the history reading interface.
In this embodiment, the scene type includes the current time and state of the user, and the scene type may be set by the user according to the need. The specific scene type of the user can be determined according to the user behavior characteristics of the historical reading interface and/or the user model. For example, the current time is 8 am, and from this time, it can be determined that the scene type of the user is "wake-up time" or "breakfast time".
And 212, taking the reading subject matched with the current scene type as a subject to be recommended according to the incidence relation between the preset scene type and the reading subject.
Specifically, reading subjects related to the scene type are searched according to the scene type of the user, and partial reading subjects are selected as subjects to be recommended according to the importance degree of the association relationship, wherein the importance degree of the association relationship can be set by the user.
The query sentence of the reading subject can be generated according to the subject to be recommended, for example, if the scene type of the user is "3 pm", the subject to be recommended determined according to step 212 is "make a fun", and the query sentence can be "make a trouble in the bar in the afternoon? Choose some fun segments carefully to want to look at the mani? ".
And step 220, carrying out dialogue interaction with the user according to the generated reading subject inquiry information.
231, determining the intention of the user according to the interactive content of the dialog of the user;
the intention of the user refers to the user's interest, interest topic or operation that the author wants to perform on the user extracted from the interactive contents of the conversation.
And step 232, adding and deleting the user interests, the user concerned topics or the user concerned authors according to the intentions of the user.
Specifically, according to the intention of the user, a new user interest, a user interest topic or a user interest author is added or a user interest, a user interest topic or a user interest author is deleted.
And step 240, determining the reading subject of the user according to the conversation interactive content.
And step 250, pushing the information matched with the reading subject to the user.
And 261, after the user closes the last reading interface, if the frequency of clicking any topic information by the user is detected to be greater than a preset threshold value, taking the topic as a recommended interest topic.
In this embodiment, a suitable threshold may be preset for the frequency of clicking on topic information by the user, and if the frequency of clicking on a certain topic information by the user is greater than the threshold, the topic is taken as a recommended topic of interest, where the frequency of clicking on the topic by the user on the same day includes whether the topic is firstly entered on the same day and the number of times the topic is entered on the same day.
And step 262, generating inquiry information added by the attention topic according to the recommended attention topic.
Specifically, a query sentence added with the follow-up topic may be generated according to the recommended follow-up topic, for example, if the recommended follow-up topic determined in step 261 is "topic a", the query sentence may be "do you want to follow topic a? A new pick article may be recommended to you! ".
For example, fig. 3 is a strong interaction form interface diagram of an information recommendation method based on dialog interaction in the second embodiment of the present invention, as shown in fig. 3, the strong interaction form is embodied in a special interaction interface, topic information and related information that a user currently wants to know are recommended in the interaction interface according to dialog interaction content with the user, for example, the user wants to "recommend some machine learning articles", the user sends related information 1, 2 and 3 "after" recommending relevant machine learning information for you "is replied for selection, and a new query sentence" helps you recommend some other machine learning related topics is generated, and information is continuously recommended or a new query sentence is generated according to the user reply. In addition, inquiry sentences generated according to the historical behaviors of the user can be used for the user to quickly select, such as 'seeing foreign affairs' or 'sports news'. After the user selects the information to be read, the user enters a reading interface, the information selected by the user can be automatically called in the reading interface for the user to read, and operations such as paying attention, interested topics, disliked topics or closing the reading interface can be selected.
Fig. 4 is a weak interaction form interface diagram of an information recommendation method based on dialog interaction in the second embodiment of the present invention, where the weak interaction form is embodied in that there is no special interaction interface, and dialog interaction with a user is implemented through a pop-up interactive dialog box. As shown in fig. 4, in addition to the information recommended according to the user's historical behavior, a new query sentence may be generated in the interactive dialog, for example, "do you need to pay your attention to topic a", and there may be a simple option in the interactive dialog for the user to select to answer the query.
The embodiment determines the current scene type or the current concerned topic of the user according to the user behavior characteristics and/or the user model of the historical reading interface, generates inquiry information to carry out conversation interaction with the user according to the current scene type or the current concerned topic, determines the intention and the style of the user according to the content of the conversation interaction, selects information matched with the reading topic and the intention and the style of the user to recommend the user, and can generate the inquiry information added with the concerned topic according to the frequency of the topic clicking by the user or generate feedback inquiry information when the user does not have the reading behavior after the user closes the previous reading interface. The embodiment has the active attention function, and can guide the user to explore and discover the potential interest of the user through conversation, so that the use duration and the viscosity of the user are improved, the content which the user does not want to see can be shielded through a simple natural language instruction, and the user experience is better.
EXAMPLE III
Fig. 5 is a schematic structural diagram of an information recommendation device based on dialogue interaction according to a third embodiment of the present invention. As shown in fig. 5, the apparatus may include:
the query information obtaining module 310 is configured to generate reading subject query information according to the user behavior characteristics of the historical reading interface and/or a user model, where the user model includes at least one of: user interests, user interest topics and user interest authors;
the conversation interaction module 320 is used for carrying out conversation interaction with the user according to the generated reading subject inquiry information;
a reading subject module 330, configured to determine a reading subject of the user according to the dialog interaction content;
the pushing module 340 is configured to push information matched with the reading subject to the user.
For example, the query information obtaining module 310 may include:
a scene type unit, specifically configured to: determining the current scene type of the user according to the user behavior characteristics and/or the user model of the historical reading interface; according to the incidence relation between the preset scene type and the reading theme, taking the reading theme matched with the current scene type as a theme to be recommended; and generating the reading subject inquiry information according to the subject to be recommended.
For example, the query information obtaining module 310 may include:
the topic unit is specifically used for: determining the topic currently concerned by the user according to the user behavior characteristics and/or the user model of the historical reading interface; and generating inquiry information for reminding reading of updated contents according to the updating condition of the topic currently concerned by the user.
Further, the topic unit may be specifically configured to:
after the topic currently concerned by the user is determined, providing more information of the topic currently concerned by the user for the user to select according to the user behavior characteristics of a historical reading interface; and providing information of related topics of the current topics of interest of the user for the user to select according to the user behavior characteristics of the historical reading interface.
For example, the query information obtaining module 310 may further include:
and the information option unit is used for generating candidate information options for the user to select according to the reading subject inquiry information after generating the reading subject inquiry information according to the user behavior characteristics and/or the user model of the historical reading interface.
Exemplarily, the apparatus may further include a frequency determining unit, specifically configured to:
after a user closes a reading interface, if the frequency of clicking any topic information by the user is detected to be greater than a preset threshold value, taking the topic as a recommended interest topic; and generating inquiry information added by the interest topic according to the recommended interest topic.
Illustratively, the apparatus may further include:
and the feedback unit is used for generating feedback inquiry information of the previous reading interface if the user closes the previous reading interface and does not have reading action on the previous reading interface.
Illustratively, the dialog interaction module 320 may include:
an intent unit, particularly for: determining the intention of the user according to the interactive contents of the dialog of the user; and performing addition and deletion processing on user interests, user concerned topics or user concerned authors according to the intentions of the users.
Illustratively, the dialog interaction module 320 may further include:
a scale unit, specifically configured to: determining the scale and/or the tone of the user according to the interactive content of the dialog of the user; and determining the matched information of the user according to the scale label and/or the tone label added in the information in advance.
The information recommendation device based on the dialogue interaction provided by the embodiment of the invention can execute the information recommendation method based on the dialogue interaction provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 6 is a schematic structural diagram of a server in the fourth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary server 412 suitable for use in implementing embodiments of the present invention. The server 412 shown in fig. 6 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 6, the server 412 is in the form of a general purpose computing device. Components of server 412 may include, but are not limited to: one or more processors 416, a system memory 428, and a bus 418 that couples the various system components (including the system memory 428 and the processors 416).
The system memory 428 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)430 and/or cache memory 432. The server 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Memory 428 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The server 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 424, etc.), with one or more devices that enable a user to interact with the server 412, and/or with any devices (e.g., network card, modem, etc.) that enable the server 412 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 422. Also, server 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) through network adapter 420. As shown, network adapter 420 communicates with the other modules of server 412 over bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the server 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor 416 executes various functional applications and data processing by executing programs stored in the system memory 428, for example, implementing a method for recommending information based on dialog interaction according to an embodiment of the present invention, the method includes:
generating reading subject inquiry information according to the user behavior characteristics of the historical reading interface and/or a user model, wherein the user model comprises at least one of the following: user interests, user interest topics and user interest authors;
carrying out dialogue interaction with a user according to the generated reading subject inquiry information;
determining the reading subject of the user according to the conversation interactive content;
and pushing the information matched with the reading subject to the user.
EXAMPLE five
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for recommending information based on dialog interaction, where the method includes:
generating reading subject inquiry information according to the user behavior characteristics of the historical reading interface and/or a user model, wherein the user model comprises at least one of the following: user interests, user interest topics and user interest authors;
carrying out dialogue interaction with a user according to the generated reading subject inquiry information;
determining the reading subject of the user according to the conversation interactive content;
and pushing the information matched with the reading subject to the user.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (15)
1. The information recommendation method based on the dialogue interaction is characterized by comprising the following steps:
determining a current scene type or a current concerned topic of the user according to user behavior characteristics of a historical reading interface and/or a user model, and generating reading subject inquiry information according to the current scene type or the current concerned topic, wherein the user model comprises at least one of the following: user interests, user interest topics and user interest authors;
carrying out dialogue interaction with a user according to the generated reading subject inquiry information;
determining the reading subject of the user according to the conversation interactive content;
and pushing the information matched with the reading subject to the user.
2. The method of claim 1, wherein generating reading topic query information based on the current scene type comprises:
according to the incidence relation between the preset scene type and the reading theme, taking the reading theme matched with the current scene type as a theme to be recommended;
and generating the reading subject inquiry information according to the subject to be recommended.
3. The method of claim 1, wherein generating reading topic query information from the currently focused topic comprises:
and generating inquiry information for reminding reading of updated contents according to the updating condition of the topic currently concerned by the user.
4. The method of claim 1, wherein the determining the topic currently of interest to the user further comprises:
providing more information of the current interest topic of the user for the user to select according to the user behavior characteristics of the historical reading interface;
and providing information of related topics of the current topics of interest of the user for the user to select according to the user behavior characteristics of the historical reading interface.
5. The method of claim 1, wherein after generating the reading topic query information according to the current scene type or the current topic of interest, further comprising:
and generating candidate information options for the user to select according to the reading subject inquiry information.
6. The method of claim 1, further comprising:
after a user closes a reading interface, if the frequency of clicking any topic information by the user is detected to be greater than a preset threshold value, taking the topic as a recommended interest topic;
and generating inquiry information added by the interest topic according to the recommended interest topic.
7. The method of claim 1, further comprising:
and if the user closes the previous reading interface and does not have reading action on the previous reading interface, generating the feedback inquiry information of the previous reading interface.
8. The method of claim 1, wherein after the dialog interaction with the user according to the generated reading subject query message, further comprising:
determining the intention of the user according to the interactive contents of the dialog of the user;
and performing addition and deletion processing on user interests, user concerned topics or user concerned authors according to the intentions of the users.
9. The method of claim 1, wherein after the dialog interaction with the user according to the generated reading subject query message, further comprising:
determining the scale and/or the tone of the user according to the interactive content of the dialog of the user;
and determining the matched information of the user according to the scale label and/or the tone label added in the information in advance.
10. Information recommendation device based on dialogue interaction, characterized by comprising:
the query information acquisition module is used for determining the current scene type or the current concerned topic of the user according to the user behavior characteristics of the historical reading interface and/or a user model, and generating reading subject query information according to the current scene type or the current concerned topic, wherein the user model comprises at least one of the following: user interests, user interest topics and user interest authors;
the dialogue interaction module is used for carrying out dialogue interaction with the user according to the generated reading subject inquiry information;
the reading theme module is used for determining the reading theme of the user according to the conversation interactive content;
and the pushing module is used for pushing the information matched with the reading subject to the user.
11. The apparatus according to claim 10, wherein the query information obtaining module includes a scene type unit, and the scene type unit is specifically configured to:
according to the incidence relation between the preset scene type and the reading theme, taking the reading theme matched with the current scene type as a theme to be recommended;
and generating the reading subject inquiry information according to the subject to be recommended.
12. The apparatus of claim 10, wherein the dialog interaction module further comprises:
an intent unit, particularly for:
determining the intention of the user according to the interactive contents of the dialog of the user;
and performing addition and deletion processing on user interests, user concerned topics or user concerned authors according to the intentions of the users.
13. The apparatus of claim 10, wherein the dialog interaction module further comprises:
a scale unit, specifically configured to:
determining the scale and/or the tone of the user according to the interactive content of the dialog of the user;
and determining the matched information of the user according to the scale label and/or the tone label added in the information in advance.
14. A server, characterized in that the server comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the dialog interaction based information recommendation method of any of claims 1-9.
15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for interactive information recommendation according to any one of claims 1 to 9.
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Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108521366A (en) * | 2018-03-27 | 2018-09-11 | 联想(北京)有限公司 | Expression method for pushing and electronic equipment |
| CN108377273B (en) * | 2018-05-15 | 2021-03-05 | 北京奇虎科技有限公司 | Information pushing method, server and device |
| CN108920649B (en) * | 2018-06-29 | 2021-09-17 | 北京百度网讯科技有限公司 | Information recommendation method, device, equipment and medium |
| CN108920651A (en) * | 2018-06-29 | 2018-11-30 | 百度在线网络技术（北京）有限公司 | Information-pushing method, device, server and storage medium |
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| CN111144708A (en) * | 2019-12-06 | 2020-05-12 | 星络智能科技有限公司 | Information interaction method and system and readable storage medium |
| CN111026932B (en) * | 2019-12-20 | 2023-05-26 | 北京百度网讯科技有限公司 | Man-machine dialogue interaction method and device, electronic equipment and storage medium |
| CN112100491A (en) * | 2020-09-08 | 2020-12-18 | 中国平安人寿保险股份有限公司 | Information recommendation method, device and equipment based on user data and storage medium |
| CN112328892B (en) * | 2020-11-24 | 2024-06-18 | 北京百度网讯科技有限公司 | Information recommendation method, device, equipment and computer storage medium |
| CN113158031B (en) * | 2021-03-15 | 2024-04-23 | 北京水滴科技集团有限公司 | Method and device for determining user resource information, computer storage medium and terminal |
| CN113568943A (en) * | 2021-06-18 | 2021-10-29 | 武汉理工数字传播工程有限公司 | Book resource processing method, device, equipment and storage medium |
| CN114650268B (en) * | 2022-04-14 | 2024-01-30 | 北京字跳网络技术有限公司 | Content pushing method, device, electronic equipment, medium and program product |
| CN117436614A (en) * | 2023-11-02 | 2024-01-23 | 国农(重庆)生猪大数据产业发展有限公司 | Comprehensive service platform for pig breeding |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1894713A (en) * | 2003-12-15 | 2007-01-10 | 皇家飞利浦电子股份有限公司 | Information recommendation system and method |
| CN101276375A (en) * | 2007-03-31 | 2008-10-01 | 索尼德国有限责任公司 | Method for content recommendation |
| CN102208086A (en) * | 2010-03-31 | 2011-10-05 | 北京邮电大学 | Field-oriented personalized intelligent recommendation system and implementation method |
| CN102713908A (en) * | 2009-11-19 | 2012-10-03 | 厦姆技术有限公司 | Apparatus and method of adaptive questioning and recommending |
| CN103348369A (en) * | 2011-01-06 | 2013-10-09 | 电子湾有限公司 | Interestingness recommendations in a computing advice facility |
| CN103793537A (en) * | 2014-03-04 | 2014-05-14 | 南京大学 | System for recommending individual music based on multi-dimensional time series analysis and achieving method of system |
| CN103927354A (en) * | 2014-04-11 | 2014-07-16 | 百度在线网络技术（北京）有限公司 | Interactive searching and recommending method and device |
| CN104166707A (en) * | 2014-08-08 | 2014-11-26 | 百度在线网络技术（北京）有限公司 | Search recommendation method and device |
| CN104572797A (en) * | 2014-05-12 | 2015-04-29 | 深圳市智搜信息技术有限公司 | Individual service recommendation system and method based on topic model |
| CN104809154A (en) * | 2015-03-19 | 2015-07-29 | 百度在线网络技术（北京）有限公司 | Method and device for recommending information |
| CN106372195A (en) * | 2016-08-31 | 2017-02-01 | 北京光年无限科技有限公司 | Human-computer interaction method and device for intelligent robot |
| CN106802951A (en) * | 2017-01-17 | 2017-06-06 | 厦门快商通科技股份有限公司 | A kind of topic abstracting method and system for Intelligent dialogue |
| CN106815217A (en) * | 2015-11-30 | 2017-06-09 | 北京云莱坞文化传媒有限公司 | Story recommends method and story recommendation apparatus |
| CN106844769A (en) * | 2017-02-27 | 2017-06-13 | 百度在线网络技术（北京）有限公司 | With reference to the pattern of passing through and in limited time reading model information flow recommend method and apparatus |
| CN106897284A (en) * | 2015-12-17 | 2017-06-27 | 腾讯科技（深圳）有限公司 | The recommendation method and device of e-book |
-
2017
- 2017-11-15 CN CN201711129842.7A patent/CN107832433B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1894713A (en) * | 2003-12-15 | 2007-01-10 | 皇家飞利浦电子股份有限公司 | Information recommendation system and method |
| CN101276375A (en) * | 2007-03-31 | 2008-10-01 | 索尼德国有限责任公司 | Method for content recommendation |
| CN102713908A (en) * | 2009-11-19 | 2012-10-03 | 厦姆技术有限公司 | Apparatus and method of adaptive questioning and recommending |
| CN102208086A (en) * | 2010-03-31 | 2011-10-05 | 北京邮电大学 | Field-oriented personalized intelligent recommendation system and implementation method |
| CN103348369A (en) * | 2011-01-06 | 2013-10-09 | 电子湾有限公司 | Interestingness recommendations in a computing advice facility |
| CN103793537A (en) * | 2014-03-04 | 2014-05-14 | 南京大学 | System for recommending individual music based on multi-dimensional time series analysis and achieving method of system |
| CN103927354A (en) * | 2014-04-11 | 2014-07-16 | 百度在线网络技术（北京）有限公司 | Interactive searching and recommending method and device |
| CN104572797A (en) * | 2014-05-12 | 2015-04-29 | 深圳市智搜信息技术有限公司 | Individual service recommendation system and method based on topic model |
| CN104166707A (en) * | 2014-08-08 | 2014-11-26 | 百度在线网络技术（北京）有限公司 | Search recommendation method and device |
| CN104809154A (en) * | 2015-03-19 | 2015-07-29 | 百度在线网络技术（北京）有限公司 | Method and device for recommending information |
| CN104809154B (en) * | 2015-03-19 | 2019-03-08 | 百度在线网络技术（北京）有限公司 | The method and device recommended for information |
| CN106815217A (en) * | 2015-11-30 | 2017-06-09 | 北京云莱坞文化传媒有限公司 | Story recommends method and story recommendation apparatus |
| CN106897284A (en) * | 2015-12-17 | 2017-06-27 | 腾讯科技（深圳）有限公司 | The recommendation method and device of e-book |
| CN106372195A (en) * | 2016-08-31 | 2017-02-01 | 北京光年无限科技有限公司 | Human-computer interaction method and device for intelligent robot |
| CN106802951A (en) * | 2017-01-17 | 2017-06-06 | 厦门快商通科技股份有限公司 | A kind of topic abstracting method and system for Intelligent dialogue |
| CN106844769A (en) * | 2017-02-27 | 2017-06-13 | 百度在线网络技术（北京）有限公司 | With reference to the pattern of passing through and in limited time reading model information flow recommend method and apparatus |
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