CN111723231A - Topic prediction method and device - Google Patents
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Abstract
A title prediction method and device, obtain the vector or set of the search title of the present user; generating a target topic set according to the vector or the set of the topics; the questions in the target question set are sequenced to obtain a recommendation sequence, the problem that the problem searching process in the prior art depends on strong OCR results is solved, a user does not need to take pictures, repair pictures and wait for each question, and the question searching time of the user is greatly saved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a topic prediction method and device.
Background
With the continuous integration of image processing and data analysis, products for shooting and searching questions are online as early as 2013. The shooting and searching in the related technology is mainly realized based on the camera module and the text recognition and searching module, and particularly, in the shooting process of the camera module, a user needs to continuously adjust the angle to shoot the title as full as possible so as to be convenient for the camera module to shoot, so that the difficulty is increased for the user in the title searching process.
Disclosure of Invention
The application provides a topic prediction method and a topic prediction device, which are used for solving the problem that in the prior art, the dependence on OCR results in a topic searching process is strong.
The application provides the following scheme:
in a first aspect, the present application provides a topic prediction method, including: obtaining a vector or a set of search topics of a current user; generating a target topic set according to the vector or the set of the topics; and sequencing the topics in the target topic set to obtain a recommendation sequence.
In some embodiments, the generating a target topic set according to the vector or set of topics specifically includes: obtaining search question data, wherein the search question data comprises question contents and a search question sequence in at least one user historical search question process; in the search question data, according to the inverted index and similarity search, a search question sequence similar to the vector or set of the questions is obtained, the questions behind the current questions in the search question sequence are extracted to serve as target questions, and a target question set is generated.
In some embodiments, the obtaining subject data further includes: reducing the dimension of the searched data by using a data mining method and a network representation learning method based on a graph to obtain a searched vector; and the nodes of the search question vectors are question information, and the directions of the search question vectors are search question sequences.
In some embodiments, the sorting the titles in the target title set to obtain a recommendation sequence further includes: training the search question data to generate a click rate prediction model; predicting the click rate of the target title according to the title and the click rate prediction model; and generating the recommendation sequence according to a preset click rate sequence.
In some embodiments, the title prediction method further includes: and displaying a preset number of target titles to the current user according to the recommendation sequence.
In some embodiments, when the preset number is greater than one, the method further includes: simultaneously displaying the preset number of titles to a user; or show the user one title at a time.
In some embodiments, each time a title is presented to the user, further comprising: acquiring a control instruction of the current user; when the control instruction is a first control instruction, providing answers and analysis of the current display question for the current user; and when the control instruction is a second control instruction, replacing the current display title for the current user. In some embodiments, before displaying a preset number of target titles to the current user according to the recommendation sequence, the method further includes: and acquiring a control instruction of the target title which needs to be provided by the current user.
In some embodiments, after replacing a preset number of display titles to the current user and/or upon obtaining a user exit instruction, exiting the display of the predicted titles.
In a second aspect, the present application provides a topic prediction apparatus, including: the acquisition module is used for acquiring a vector or a set of search topics of a current user; the generating module is used for generating a target topic set according to the vector or the set of the topics; and the recommendation module is used for sequencing the topics in the target topic set to obtain a recommendation sequence.
According to the specific embodiments provided herein, the present application discloses the following technical effects:
in the embodiment of the application, a large amount of search question data can be matched with questions searched by a current user, a target question to be searched by the user can be predicted by data mining and other modes, a recommendation sequence is generated, the predicted questions can be recommended to the user by combining a recommendation function, when the recommended question is consistent with a question required by the user, the user can obtain an answer and analyze by only a first control instruction without photographing, repairing and waiting for each question by the user, a large amount of search question time of the user is saved, the recommendation sequence is generated by descending order of click rate, 1-5 questions can be recommended for the user, the problem that the user cannot obtain the questions in time when using different tutoring materials due to single recommendation content is avoided, meanwhile, the problem that the user cannot obtain the questions in time due to the fact that the user does not need to obtain the target questions and only needs a page turning function is avoided, and the problem that the user cannot photograph the user, The influence of the search questions caused by the interference such as picture completion and the like further improves the experience of the search questions of the user.
Of course, it is not necessary for any product to achieve all of the above-described advantages at the same time for the practice of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a diagram illustrating topic information selection by a user in the related art;
FIG. 2 is a diagram illustrating providing answers and resolutions to a user in the related art;
FIG. 3 is a flowchart of a topic prediction method according to an embodiment of the present application;
FIG. 4 is a flow chart of a topic prediction method according to one embodiment of the present application;
FIG. 5 is a schematic diagram of a search topic vector of the present application;
FIG. 6 is a flow chart of a topic prediction method according to another embodiment of the present application;
FIG. 7 is a flow chart of a topic prediction method according to yet another embodiment of the present application;
FIG. 8 is a schematic illustration of the present subject matter in a specific embodiment of the present application;
FIG. 9 is a schematic illustration of a target topic in one embodiment of the present application;
FIG. 10 is a schematic illustration of a recommendation interface in an exemplary embodiment of the present application;
FIG. 11 is a schematic illustration of a recommendation interface in another embodiment of the present application;
fig. 12 is a block diagram schematically illustrating a title prediction apparatus according to the present embodiment.
Detailed Description
It should be noted that the photographing search problem in the related art is generally implemented based on a camera module, a text recognition module and a search module. Specifically, when searching for a question by photographing, a user is first required to photograph a question to be searched, and at this time, the camera module provides a photographing service such as focusing for the user, and the user is also required to select a frame in a photographing region and a picture region to select a part only including the question information, as shown in fig. 1. Then, the text recognition module extracts characters in the picture obtained by photographing, so that the search module can search in the database according to the characters extracted by the text recognition module, and a return interface shown in fig. 2 is obtained.
However, in the related art, there are disadvantages as follows: 1) the query time is longer: because the user is required to photograph the target question and then search the target question, each question is subjected to photographing, text recognition and searching processes once; 2) the operation cost is high: in the shooting process, a user needs to shoot the photo, and frame selection operation needs to be carried out on the shot photo, and the operations increase the complexity of the user; 3) strongly dependent OCR results: the recognition result of OCR (Optical character recognition) usually results in the returned result not matching the original question due to the error of user operation, such as incomplete shooting, unclear shooting, meaningless information, etc.
Therefore, these problems seriously affect the user's experience using the photo-taking problem-searching function, not only waste time, but also easily return to an erroneous result, and thus, improvement is required.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived from the embodiments of the present application by a person of ordinary skill in the art are intended to be within the scope of the present application.
FIG. 3 is a flowchart of a topic prediction method according to an embodiment of the present application. As shown in fig. 1, the title prediction method includes:
s101: and acquiring a vector or a set of search topics of the current user.
It should be noted that the present application is provided based on the existing topic search method, and when the topics of the current user's topic search are obtained, the user can use the camera module, the text recognition module, and the search module in the related art to implement the method.
Further, the vector or set of the current user search topics may be a vector or set of the current user search topic process, that is, the content of the current user search topics may be nodes in the vector or points in the set, and the order of the current user search topics may be the direction of the vector or the order of the points in the set.
S102: and generating a target topic set according to the vector or the set of the topics.
In this embodiment, as shown in fig. 4, generating a target topic set according to a vector or a set of topics may further include:
s201: and acquiring the data of the search questions.
The title searching data comprises title contents and a title searching sequence in at least one user historical title searching process.
It should be noted that, when each user searches for questions through the question searching application program, the current question of shooting search and the sequence of consecutive search questions of the user are stored, and the question searching data is generated by the question searching question contents and the question searching sequence of a large number of users, so that the volume of the question searching data is huge with the increase of the number of the question searching users and the increase of the operation time of the question searching application program.
It should be noted that, the data of the search questions may also be pre-stored in advance by the developer. In this embodiment, acquiring the search question data further includes: and reducing the dimension of the search question data by using a data mining method and a network representation method based on a graph to obtain a search question vector.
Wherein, the node of the search question vector can be the question information, and the direction of the search question vector can be the search question sequence.
For example, as shown in fig. 5, the search question vector can be used to express the sequence of the user doing questions in forward order or reverse order according to the sequence number, respectively. It should be understood that, in the prior art, the topic contents in many tutoring materials are the same, and are distinguished only by changing the topic order, so that the question-making order of the user can be determined more simply by adopting the way of searching for the topic vector, and the content of the tutoring materials of the user does not need to be known.
S202: in the search question data, a search question sequence similar to a vector or a set of questions is obtained according to the inverted index and similarity search, the questions behind the current questions in the search question sequence are extracted to serve as target questions, and a target question set is generated.
That is to say, after the search question data is obtained, according to the historical learning behavior of at least one user and the result of the latest photographed search question, by using an inverted index and a similarity search mode, a search question sequence similar to a vector or a set of search questions of the user is obtained first, then the questions behind the current question in the sequence are extracted, the problem that the current user may need to photograph and answer next in the search question data is recalled, and the set of the questions is used as a target question set.
Specifically, firstly, due to the large amount of the search question data and the irregularity of the search questions of the user, the search question data needs to be subjected to dimensionality reduction to obtain a search question vector, then inverted indexing is performed on the search question data to obtain a first candidate similar target question set, and after the obtained first candidate similar target question set is obtained, the first candidate similar question set is screened to obtain a second candidate similar question set. And further, combining similarity calculation to obtain a final target topic set.
S103: and sequencing the topics in the target topic set to obtain a recommendation sequence.
In this embodiment, as shown in fig. 6, sorting the titles in the target title set to obtain a recommendation sequence, may further include:
s301: and training the data of the search questions to generate a click rate prediction model.
It should be noted that a deep learning model can be constructed according to the above-mentioned search question data, that is, a click rate prediction model is generated according to the search question data, wherein the question content and the search question sequence are used as input, and each question is used as the click rate of the target question as output.
S302: and predicting the click rate of the target topic according to the topic and click rate prediction model.
S303: and generating a recommendation sequence according to a preset click rate sequence.
Specifically, the titles searched by the current user are input to the click rate prediction model, the click rates of a plurality of target titles which may appear behind the title can be obtained, and then the recommendation sequence is generated according to the sequence of the preset click rates.
In this embodiment of the present application, the click rate sequence may be a descending order, that is, the recommendation sequence is generated according to the click rate descending order, in other words, the topic at the first position in the recommendation sequence is the topic with the highest click rate.
In the embodiment of the application, after the recommendation sequence is obtained, a preset number of target titles can be displayed to the current user according to the recommendation sequence.
For example, one topic can be displayed in the interface at a time, the user can change the topic by turning pages (selecting the next topic or changing the next topic), or one to five topics can be displayed in the interface at a time. It should be understood that in order to display the title content in the interface that can be fully presented to the user, preferably, the titles are recommended one at a time in the interface.
In this embodiment, when displaying one title at a time in the interface, as shown in fig. 7, the method further includes:
s401: and acquiring a control instruction of the current user.
S402: and when the control instruction is a first control instruction, providing the answer and the analysis of the current display title for the current user.
S403: and when the control instruction is a second control instruction, replacing the current display title for the current user.
Specifically, referring to fig. 8, when the interface recommends a question for the user, a first control instruction button and a second control instruction button may be simultaneously displayed below the interface, where the first control instruction may be a view button, and may specifically be represented by characters such as "see", "determine", and "analyze", and the second control button may be a page turning button, and may specifically be represented by characters such as "change one question", "turn a page", and "next question". When the user clicks the first control instruction button, the currently recommended topic is selected and analyzed by the user, at the moment, the answer and the analysis of the currently recommended topic are displayed for the user, when the user clicks the second control instruction button, the currently recommended topic is not the topic selected by the user, and the next recommended topic needs to be continuously provided for the user according to the recommendation sequence.
It should be understood that the presentation of the predicted titles is exited after a preset number of presentation titles are replaced to the current user and/or upon obtaining a user exit instruction.
That is, when a recommended title is displayed to a current user, if the user obtains that none of the titles in the recommended sequence are the titles that the user wants to search through a page turning mode, the user can automatically quit the display interface and return to the photo-taking and title-searching interface, or if the user inputs a quit instruction, the user can quit the display interface and return to the photo-taking and title-searching interface.
Further, before a preset number of target titles are presented to the current user according to the recommendation sequence, the method further includes: and acquiring a control instruction of a target topic which needs to be provided by the current user.
That is, while the answer and the analysis of the current title are performed, a control button of text content such as "shoot again" may be further provided, so that when the user triggers the control button, a control instruction that the user needs to provide a target title is obtained, and at this time, the user may be directly recommended to the user without taking a picture, as shown in fig. 8 to 10.
Therefore, according to the title prediction method, a target title to be searched by a user can be predicted in a data processing mode through a large amount of search data and the title searched by the current user, a recommendation sequence is generated, the predicted title can be recommended to the user in combination with a recommendation function, when the recommended title is consistent with the title required by the user, the user can obtain an answer and analyze through a first control instruction, the user does not need to photograph, repair and wait for each title, the title search time of the user is greatly saved, in addition, 1-5 titles can be recommended to the user through the recommendation sequence arranged in a descending order, the problem that the user cannot obtain the title in time when using different tutoring materials due to single recommendation content is avoided, meanwhile, the problem that the user cannot obtain the title in time due to the fact that the user does not need to obtain the target title and only needs a page turning function is avoided, and the problem that the user cannot photograph is not clear is, The influence of the search questions caused by the interference such as picture completion and the like further improves the experience of the search questions of the user.
It should be understood that after the title of the current user search is obtained, the following is also included: the answer and resolution of the title is provided to the current user.
That is, after the user uses the photographing function to search for the question each time, the answer and the analysis of the current search question are provided for the user, and the function of searching for the question is realized.
According to another embodiment of the present application, as shown in fig. 11, the method comprises the following steps:
s501: and the user takes a picture to search for questions.
S502: and displaying the answers and the analysis of the questions.
S503: and judging whether the user clicks to shoot a question again.
If yes, step S504 is executed, and if no, the process is ended.
S504: and displaying the recommended target title.
S505: and judging whether the user clicks to see.
If yes, returning to the step S502; if not, step S506 is performed.
S506: and displaying the next target topic.
S507: and judging whether the user clicks to see.
If yes, returning to the step S502; if not, step S501 is executed.
Specifically, after the user enters the application program, the user first takes a picture to search for questions, and shows answers and analysis of the questions to the user. Then, whether the user clicks a rephotograph question or not is judged, when the user selects the rephotograph question, the fact that the user needs to continue searching the question is indicated, at the moment, the recommended target question can be displayed for the user, the shooting question searching time of the user is saved, the problems of shooting blurring, picture completion and the like are avoided, then, whether the user clicks to see is judged, if yes, the recommended question is indicated to be in line with the selection of the user, the answer and the analysis of the recommended question continue to be displayed for the user, if not, the next target question is displayed for the user, a plurality of selected spaces are provided for the user, if the questions in the recommendation list are not the questions required by the user, the shooting question searching is returned, and the user can still search the question through the shooting question searching function.
Fig. 12 is a block diagram schematically illustrating a title prediction apparatus according to the present embodiment. As shown in fig. 11, the title prediction apparatus 100 includes: an acquisition module 10, a generation module 20 and a recommendation module 30.
The obtaining module 10 is configured to obtain a vector or a set of search topics of a current user; the generating module 20 is configured to generate a target topic set according to the vector or set of topics; the recommending module 30 is configured to sort the titles in the target title set to obtain a recommended sequence.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are described in a relatively simple manner since they are similar to the method embodiments, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application. In view of the above, the description should not be taken as limiting the application.
Claims (10)
1. A topic prediction method, comprising:
obtaining a vector or a set of search topics of a current user;
generating a target topic set according to the vector or the set of the topics;
and sequencing the topics in the target topic set to obtain a recommendation sequence.
2. The topic prediction method according to claim 1, wherein generating a target topic set according to the vector or set of topics specifically comprises:
obtaining search question data, wherein the search question data comprises question contents and a search question sequence in at least one user history search question process;
in the search question data, according to the inverted index and similarity search, a search question sequence similar to the vector or set of the questions is obtained, the questions behind the current questions in the search question sequence are extracted to serve as target questions, and a target question set is generated.
3. The title prediction method of claim 2, wherein the obtaining of the search title data further comprises:
reducing the dimension of the searched data by using a data mining method and a network representation learning method based on a graph to obtain a searched vector;
and the nodes of the search question vectors are question information, and the directions of the search question vectors are search question sequences.
4. The topic prediction method of claim 2, wherein the ranking of the topics in the target topic set to obtain a recommendation sequence further comprises:
training the search question data to generate a click rate prediction model;
predicting the click rate of the target title according to the title and the click rate prediction model;
and generating the recommendation sequence according to a preset click rate sequence.
5. The topic prediction method according to claim 1, further comprising:
and displaying a preset number of target titles to the current user according to the recommendation sequence.
6. The topic prediction method of claim 5, wherein when the predetermined number is greater than one, further comprising:
simultaneously displaying the preset number of titles to a user; or
The user is presented with a title at a time.
7. The title prediction method of claim 5, further comprising, when each title is presented to the user:
acquiring a control instruction of the current user;
when the control instruction is a first control instruction, providing answers and analysis of the current display question for the current user;
and when the control instruction is a second control instruction, replacing the current display title for the current user.
8. The topic prediction method of claim 5, further comprising, before presenting a preset number of target topics to the current user according to the recommendation sequence:
and acquiring a control instruction of the target title which needs to be provided by the current user.
9. The topic prediction method according to claim 5, further comprising:
and exiting the display of the prediction titles after replacing a preset number of display titles for the current user and/or acquiring a user exit instruction.
10. A topic prediction device, comprising:
the acquisition module is used for acquiring a vector or a set of search topics of a current user;
the generating module is used for generating a target topic set according to the vector or the set of the topics;
and the recommendation module is used for sequencing the topics in the target topic set to obtain a recommendation sequence.
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