CN116595249A - Personality recommendation method, system, electronic device and computer program product - Google Patents
Personality recommendation method, system, electronic device and computer program product Download PDFInfo
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Abstract
The invention provides a method, a system, electronic equipment and a computer program product for recommending individuality, which comprise the following steps: acquiring a label of target data in a database; the database stores at least one target data and a tag of the at least one target data; when a keyword input by a user is received, the tag of the target data is obtained from a database according to the keyword; classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data; acquiring target data corresponding to the first recommendation list from a database to obtain a second recommendation list; obtaining recommended content according to the first recommendation list and the second recommendation list; therefore, more hierarchical information channels are provided for the user, and the experience of the user is improved.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a method, a system, electronic equipment and a computer program product for individual recommendation.
Background
With the development of computer and network technologies, data recommendation is also widely applied, and more users are used to search for favorite contents in the home page. However, most of the current searches are fuzzy or accurate matching of single data, belong to basic retrieval functions, the recommended content obtained by searching through the method is final target data after fuzzy or accurate matching of the single data, and for users who want to widen information channels or have ambiguous targets, the method cannot provide more recommended content for the users, and has low experience.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a personalized recommendation method, a personalized recommendation system, electronic equipment and a computer program product, which are used for solving the problems in the prior art.
One embodiment of the invention provides a personalized recommendation method, which comprises the following steps:
acquiring a label of target data in a database; wherein the database stores at least one target data and a tag of the at least one target data; when a keyword input by a user is received, the tag of the target data is obtained from a database according to the keyword;
Classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data;
acquiring target data corresponding to the first recommendation list from a database to obtain a second recommendation list;
and obtaining recommended content according to the first recommendation list and the second recommendation list.
In one embodiment, the tag of the target data is obtained from the database according to the keyword, including:
processing the keywords, and determining tags of target data matched with the keywords in the database; the method comprises the steps that at least one field of a label is stored in a database, the field of the label is formed by splicing initial letters of each Chinese character pinyin of the label, and an index for recalling the label is arranged in the field;
and recalling the label of the target data matched with the keyword in the database according to the index of the field.
In one embodiment, processing the keywords includes:
Acquiring keywords input by a user; wherein the keywords include one or more of age groups, letters, and numbers;
when the keyword comprises letters, determining the field of the label matched with the letters in the keyword according to the matching result of the letters in the keyword and the fields of the labels in the database;
converting letters in the keywords into characters;
based on adjacent characters of the Chinese characters in the labels, word segmentation is carried out on the characters;
inquiring in a database according to the segmentation;
when the keyword comprises a number, inquiring in a database according to the number;
when the keywords comprise age groups, inquiring target data matched with the age groups in a database according to the age groups; and determining target data matched with the age bracket according to the age label, wherein the age label is formed by self-defining according to basic information of the target data.
In one embodiment, the method further comprises:
when the keyword input by the user is not received, the label of the target data is obtained from the database according to the order quantity and the order quantity of each target data in the database;
Or when the keyword input by the user is not received, the label of the target data is obtained from a database according to the history of the user, and the label is matched with the target data in the history;
when the keywords input by the user are not received, the first recommendation list comprises one or more of a search history list, a first name list of hot search target data, a second name list of a hot search target and a hot search target type list.
In one embodiment, the classifying outputting the tag of the target data includes:
classifying tags of the target data, wherein the tags of the target data comprise one or more of a first name tag recorded according to a first name of the target data, a second name tag recorded according to a second name of the target data and a target data type tag recorded according to a type of the target data;
sequentially sorting the labels of the classified targets according to the sorting order of the labels of the target data in the ranking table;
outputting the label of the ordered target data;
the ranking table is obtained by ranking the labels of the target data classified in the database according to the order quantity and the order quantity of the target data.
In one embodiment, classifying tags of target data includes:
obtaining a label of target data matched with the first name label in the labels of the target data, so as to obtain a first name list of guessed target data or a first name list of hot search target data after sorting;
obtaining a label of target data matched with the second name label in the labels of the target data, so as to obtain a second name list of guessed target data or a second name list of hot search target data after sorting;
obtaining tags matched with the types of the target type tags in the tags of the target data, and obtaining a guessed target data type list or a hot search target data type list after sorting;
and acquiring a label matched with the first name label in the labels of the target data based on the history record, so as to obtain a search history list after sorting according to the time stamp of the history record.
In one embodiment, the ranking table is obtained by sorting the tags of the target data classified in the database according to the on-demand quantity and the order quantity of the target data, and includes:
acquiring on-demand data and ordering data of target data to form a history record of a user so as to count the on-demand quantity and ordering quantity of each target data in a database; wherein the history record has a timestamp;
And executing different statistical strategies on the labels of the target data in the database based on the on-demand quantity and the ordering quantity of each target data in the database to obtain a ranking table.
In one embodiment, performing different statistical policies on labels of target data in a database based on the on-demand and subscription amounts of each target data in the database includes:
determining the order quantity and the order quantity of each label in the database according to the order quantity and the order quantity of each target data;
classifying tags of target data in a database to obtain one or more of a first name list of the target data, a second name list of the target data and a type list of the target data;
the labels of the target data in the target data first name list, the target data second name list and the target data type list are respectively ordered according to the weight of the label index, and the ranking list is obtained;
wherein the tag index is configured to: adding the ordered quantity of each label in the database and the common logarithm of the on-demand quantity in a preset time period;
the preset time period includes one or more of a week, a half month, a half year, and a year.
In one embodiment, the obtaining, in the database, the target data corresponding to the first recommendation list includes:
when the user does not select the label of the target data in the first recommendation list, determining the target data corresponding to the first recommendation list;
acquiring target data matched with the label of the target data corresponding to the first recommendation list in a database to obtain first recommendation target data;
the first recommendation target data are ordered according to the ordering sequence of the labels of the target data in the ranking list, so that a second recommendation list is obtained;
when the user selects the tag of the target data in the first recommendation list,
acquiring target data matched with the label of the selected target data in a database;
and sorting the target data according to the sorting order of the labels of the target data in the ranking table to obtain a second recommendation list.
In one embodiment, the obtaining recommended content according to the first recommendation list and the second recommendation list includes:
displaying a request list for inputting keywords by a user;
different display strategies are executed on the request list, the first recommendation list and the second recommendation list according to the moving direction or the position of the cursor so as to obtain the recommended content;
Wherein the moving direction or position of the cursor is controlled by the user;
the recommended content includes one or more of the first, second, and third recommended content.
In one embodiment, executing different exhibition strategies on the request list, the first recommendation list and the second recommendation list according to the position of the cursor comprises the following steps:
displaying the request list, the first recommendation list and the second recommendation list to obtain first recommendation content; wherein the cursor is on a request list;
hiding or folding the request list when the cursor moves from the request list to the first recommendation list based on the first recommendation content, and displaying the first recommendation list and the second recommendation list to obtain second recommendation content;
based on the second recommended content, when the cursor moves from the first recommended list to the second recommended list, hiding or folding the request list and the first recommended list, and displaying the second recommended list to obtain the third recommended content.
In one embodiment, according to the moving direction of the cursor, different exhibition strategies are executed on the request list, the first recommendation list and the second recommendation list, including:
Based on the third recommended content, when the cursor moves to a preset distance or a preset area in the second recommended list in a preset direction, displaying the first recommended list and the second recommended list to obtain the second recommended content;
and based on the second recommended content, when the cursor moves to a preset distance or a preset area in the first recommended list in a preset direction, displaying the request list, the first recommended list and the second recommended list to obtain the first recommended content.
One embodiment of the present invention further provides a personalized recommendation system, which is characterized by comprising:
the first acquisition module is used for acquiring the tag of the target data in the database; wherein the database stores at least one target data and a tag of the at least one target data;
the receiving module is used for receiving keywords input by a user, and the labels of the target data are obtained from a database according to the keywords;
the processing module is used for classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data;
The second acquisition module is used for acquiring target data corresponding to the first recommendation list from the database to obtain a second recommendation list;
and the generation module is used for obtaining recommended contents according to the first recommendation list and the second recommendation list.
One embodiment of the present invention further provides an electronic device, including: a processor and a memory;
wherein the memory is used for storing computer execution instructions;
a processor for executing computer-executable instructions stored in a memory to perform the steps of the personalized recommendation method according to any one of the embodiments described above.
An embodiment of the invention also provides a computer program product, which is characterized in that the computer program product comprises a computer program, and is characterized in that the computer program, when being executed by a processor, realizes the steps of the personalized recommendation method according to any of the embodiments above.
The personality recommendation method, the personality recommendation system, the electronic equipment and the computer program product provided by the above embodiments have the following beneficial effects:
the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a list of guessed target data types, so that the user can acquire labels of target data with different hierarchies, hierarchical display of the target data is realized, relevant information of popular target data in a preset time period can be quickly known, an information channel of the user is further widened, the user can be provided with higher search accuracy through the second recommendation list, search results are accurately displayed, diversified and personalized recommendation effects of the target data are realized, the user can pay attention to historical records, popular target data or popular target data related to search content in time, and the relevant popular target data can be further recommended according to the labels of the target data of the user in the first recommendation list, so that the information channel of the user is further widened; the recommended content obtained through the first recommended list and the second recommended list is used for providing more hierarchical information channels for users, so that the experience of the users is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall workflow schematic diagram of a personality recommendation method according to an embodiment of the present invention;
FIG. 2 is a schematic workflow diagram of implementing various recommended content by the personalized recommendation method of FIG. 1;
FIG. 3 is a basic information diagram of target data of the personality recommendation method of FIG. 1;
FIG. 4 is a schematic label diagram of target data of the personality recommendation method of FIG. 1;
FIG. 5 is a schematic diagram of a first recommendation list of the personalized recommendation method of FIG. 1 when a user inputs keywords;
FIG. 6 is a schematic diagram of a first recommendation list of the personalized recommendation method of FIG. 1 when a user does not input keywords;
FIG. 7 is a ranking representation of the personality recommendation method of FIG. 1;
FIG. 8 is a schematic diagram of the overall operation of the personality recommendation method of FIG. 1;
FIG. 9 is a diagram showing recommended content when the user of the personalized recommendation method of FIG. 1 does not input keywords;
FIG. 10 is a diagram showing recommended content when a user of the personalized recommendation method of FIG. 1 inputs keywords;
FIG. 11 is a schematic illustration showing a first recommended content of the personalized recommendation method of FIG. 1;
FIG. 12 is a schematic diagram showing a second recommended content of the personalized recommendation method of FIG. 1;
fig. 13 is a schematic illustration showing a third recommended content of the personalized recommendation method in fig. 1.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, if a directional indication (such as up, down, left, right, front, and rear … …) is involved in the embodiment of the present invention, the directional indication is merely used to explain the relative positional relationship, movement condition, etc. between the components in a specific posture, and if the specific posture is changed, the directional indication is correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, if "and/or" and/or "are used throughout, the meaning includes three parallel schemes, for example," a and/or B "including a scheme, or B scheme, or a scheme where a and B are satisfied simultaneously. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Referring to fig. 1-13, one embodiment of the present invention provides a method for recommending personalities, which includes the following steps:
s100, acquiring a tag of target data in a database; wherein the database stores at least one target data and a tag of the at least one target data; s110, when a keyword input by a user is received, obtaining a label of the target data from a database according to the keyword;
S200, classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data;
s300, acquiring target data corresponding to the first recommendation list from a database to obtain a second recommendation list;
s400, obtaining recommended content according to the first recommendation list and the second recommendation list.
In this embodiment, since the first recommendation list includes one or more of a first name list of guessed target data, a second name list of guessed target data and a list of guessed target data types, it is ensured that a user can acquire tags of target data with different hierarchies, hierarchical display of the target data is achieved, so that relevant information of popular target data within a preset time period can be quickly known, information channels of the user can be further widened, higher search accuracy can be provided for the user through the second recommendation list, accurate display of search results is achieved, diversified and personalized recommendation effects of the target data are achieved, and it is ensured that the user can pay attention to historical records, popular target data or popular target data related to search content in time, and the relevant popular target data can be further recommended according to the tags of target data of the user prone to the first recommendation list, so that the information channels of the user can be further widened; the recommended content obtained through the first recommended list and the second recommended list is used for providing more hierarchical information channels for users, so that the experience of the users is improved.
The label of the target data is formed according to the basic information of the target data; the base information includes one or more of a first name of the target data, a second name of the target data, and a type of the target data.
In one embodiment, the tag of the target data is obtained from the database according to the keyword, including:
processing the keywords, and determining tags of target data matched with the keywords in the database; the method comprises the steps that at least one field of a label is stored in a database, the field of the label is formed by splicing initial letters of each Chinese character pinyin of the label, and an index for recalling the label is arranged in the field;
and recalling the label of the target data matched with the keyword in the database according to the index of the field.
In this embodiment, when the target data is input into the database, the user-defined label is performed according to the basic information of the target data, so as to form a first recommendation list, obtain related target data, and form a second recommendation list, so that the user can quickly know the condition of the target data related to the label of the target data; the tag of the target data is formed according to basic information of the target data; the base information includes one or more of a first name of the target data, a second name of the target data, and a type of the target data. According to the first name definition of the target data, a first name label corresponding to the target data is obtained; obtaining a second name label corresponding to the target data according to the second name definition of the target data; obtaining a target data type label corresponding to the target data according to the type definition of the target data; for example, when the target data is a course, the labels of the course are formed by self-defining according to the basic information of the course, for example, the first name label is formed according to the name setting of the course, and the second name label is formed according to the IP setting of the course, for example: the Duola A dream is a second name tag, a season of the Duola A dream is a first name tag, and a course type (target data) tag is formed according to a type definition of a course (target data), such as one or more of a cartoon, a life and a study.
In this embodiment, because the pinyin letters have a natural fuzzy matching effect, when the target data is input into the database, unified pinyin conversion and initial extraction processing are performed on each Chinese character of each label in the first name label, the second name label or the target data type label of the target data, and indexes are added to form hidden fields of the first name label, the second name label or the target data type label corresponding to the target data; specifically, each Chinese character in the first name label of the target data is subjected to unified pinyin conversion and initial extraction, and an index is added to form a field of the first name label corresponding to the target data; performing unified pinyin conversion and initial extraction processing on each Chinese character of the second name label of the target data, and adding an index to form a field of the second name label corresponding to the target data; and carrying out unified pinyin conversion and initial extraction processing on each Chinese character in the target data type label of the target data, and adding an index to form a field of the target data type label corresponding to the target data.
In one embodiment, processing the keywords includes:
Acquiring keywords input by a user; wherein the keywords include one or more of age groups, letters, and numbers;
when the keyword comprises letters, determining the field of the label matched with the letters in the keyword according to the matching result of the letters in the keyword and the fields of the labels in the database;
converting letters in the keywords into characters;
based on adjacent characters of the Chinese characters in the labels, word segmentation is carried out on the characters;
inquiring in a database according to the segmentation;
when the keyword comprises a number, inquiring in a database according to the number;
when the keywords comprise age groups, inquiring target data matched with the age groups in a database according to the age groups; and determining target data matched with the age bracket according to the age label, wherein the age label is formed by self-defining according to basic information of the target data.
In the embodiment, through inputting keywords in the request list by the user, label recommendation and diversified and hierarchical classification recommendation of accurate target data can be performed according to the keywords input by the user; according to the matching result of the keywords and the fields, data labels with order quantity or ordering quantity in a week are extracted from a database, and sequencing is carried out according to the sequencing order of the labels of the target data in each sorting order in the ranking table, so that a first recommendation list comprising one or more of a first name list of the guessed target data, a second name list of the guessed target data and a type list of the guessed target data is obtained, information channels of users are expanded, accurate and efficient output of labels of popular target data related to the keywords is achieved according to the keywords, the situation of displaying the popular target data in a layering mode is achieved, users can quickly know different layering information, names of the target data are displayed through the first name list of the target data, information acquisition efficiency is improved, the effects of data diversified display and information channels of the users are achieved, and experience of the users is improved.
The method comprises the steps of acquiring age groups and letters which are transmitted into by a user through a request list, and forming keywords. Obtaining a first name label of target data with order quantity or ordering quantity in the last week by filtering keywords and matching fields in a database, and obtaining a first name list of guessed target data according to a corresponding ranking order in a ranking list; obtaining a second name label of target data with order quantity or ordering quantity in the last week by filtering keywords and matching fields in a database, and obtaining a second name list of guessed target data according to the corresponding ranking order ranking in a ranking list; and obtaining a target data type label of target data with the order quantity or the order quantity in the last week by filtering the keywords and matching fields in the database, and obtaining a guessed target data type list according to the corresponding ranking order in the ranking list to form a first recommendation list.
In one embodiment, the method further comprises:
s120, when the keyword input by the user is not received, the label of the target data is obtained from the database according to the order quantity and the order quantity of each target data in the database;
or S120, when the keyword input by the user is not received, the label of the target data is obtained from a database according to the history of the user, and the label is matched with the target data in the history;
When the keywords input by the user are not received, the first recommendation list comprises one or more of a search history list, a first name list of hot search target data, a second name list of a hot search target and a hot search target type list.
In this embodiment, when the user does not input a keyword, the tag of the target data acquired in the database is determined by the on-demand amount and the order amount of each target data in the database; and/or determining the label of the target data acquired in the database through the history record of the user; thereby quickly generating a first recommendation list and recommendation content, wherein the first recommendation list comprises one or more of a search history list, a first name list of hot search target data, a second name list of a hot search target and a hot search target type list;
in one embodiment, the classifying outputting the tag of the target data includes:
classifying tags of the target data, wherein the tags of the target data comprise one or more of a first name tag recorded according to a first name of the target data, a second name tag recorded according to a second name of the target data and a target data type tag recorded according to a type of the target data;
Sequentially sorting the labels of the classified targets according to the sorting order of the labels of the target data in the ranking table;
outputting the label of the ordered target data;
the ranking table is obtained by ranking the labels of the target data classified in the database according to the order quantity and the order quantity of the target data.
In this embodiment, the requirement of the user on demand or ordering can be further determined through the second recommendation list, so that the target data in the database can be accurately obtained for on demand, and the tags for obtaining the target data selected from the first recommendation list or automatically selected from the first recommendation list are sequentially ordered according to the ordering sequence of the ranking list in a preset time period; therefore, the second recommendation list is obtained, the hot target data in the first recommendation list is accurately and efficiently output, the information acquisition efficiency is improved, and the effects of diversifying and layering the target data and widening the information channels of users are achieved.
Wherein, the labels of the target data are determined from the first recommendation list according to the user, the labels of the target data comprise one or more of a first name label recorded according to the first name of the target data, a second name label recorded according to the second name of the target data and a target data type label recorded according to the type of the target data, the labels of the target data are determined according to the labels (wherein, in an initial state, when a keyword is not input by the user and the label of the target data is not selected in the first recommendation list, the labels of the target data can be output according to one of a first name list, a second name list and a target data type list of the target data in the first recommendation list to obtain a second recommendation list, when the keyword is input by the user but the label of the target data in the first recommendation list is not selected, the labels of the target data in the first recommendation list can be output according to the first name list, the second name list and one of the target data in the target list is selected in the first recommendation list is displayed, the second list is obtained by outputting according to one of the first name list and the target data in the first list is selected when the keyword is selected by the user is matched with the first name list, and sorting the target data corresponding to the second name (label) of the target data or the target data corresponding to the third name (label) of the target data according to a sorting order of the ranking table in a preset time period to obtain a second recommendation list, wherein the preset time comprises one or more of one week, one half month, one half year and one year. When the user does not input a keyword, the second recommendation list can be automatically output and displayed according to one of a week, a half month, a half year and a year, when the user selects a first name label in the first recommendation list, the first name of target data corresponding to the first name label is matched in a database, the target data corresponding to the first name label is obtained, and the second recommendation list is obtained by sorting according to a sorting order corresponding to a ranking list; when the user selects the second name tag and the target data type tag in the first recommendation list, matching the second name tag and the second name of the target data corresponding to the target data type tag and the type of the target data in the database, acquiring one or more target data corresponding to the second name tag and the target data type tag, and sorting according to the sorting order corresponding to the ranking list to obtain the second recommendation list.
In one embodiment, classifying tags of target data includes:
obtaining a label of target data matched with the first name label in the labels of the target data, so as to obtain a first name list of guessed target data or a first name list of hot search target data after sorting;
obtaining a label of target data matched with the second name label in the labels of the target data, so as to obtain a second name list of guessed target data or a second name list of hot search target data after sorting;
obtaining tags matched with the types of the target type tags in the tags of the target data, and obtaining a guessed target data type list or a hot search target data type list after sorting;
and acquiring a label matched with the first name label in the labels of the target data based on the history record, so as to obtain a search history list after sorting according to the time stamp of the history record.
In one embodiment, the ranking table is obtained by sorting the tags of the target data classified in the database according to the on-demand quantity and the order quantity of the target data, and includes:
acquiring on-demand data and ordering data of target data to form a history record of a user so as to count the on-demand quantity and ordering quantity of each target data in a database; wherein the history record has a timestamp;
And executing different statistical strategies on the labels of the target data in the database based on the on-demand quantity and the ordering quantity of each target data in the database to obtain a ranking table.
In one embodiment, performing different statistical policies on labels of target data in a database based on the on-demand and subscription amounts of each target data in the database includes:
determining the order quantity and the order quantity of each label in the database according to the order quantity and the order quantity of each target data;
classifying tags of target data in a database to obtain one or more of a first name list of the target data, a second name list of the target data and a type list of the target data;
the labels of the target data in the target data first name list, the target data second name list and the target data type list are respectively ordered according to the weight of the label index, and the ranking list is obtained;
wherein the tag index is configured to: adding the ordered quantity of each label in the database and the common logarithm of the on-demand quantity in a preset time period;
the preset time period includes one or more of a week, a half month, a half year, and a year.
In this embodiment, the on-demand amount and the order amount of the courses (the target data may be the courses) are counted according to the days in real time to form a ranking table; specifically, counting the on-demand amount of courses in real time according to days and counting the ordering amount of courses in real time according to days, and respectively ordering labels in a first name list of target data, a second name list of target data or a type list of target data according to label indexes in a preset time period to generate a ranking table; specific:
calculating first name labels of courses (target data) in a first name list of target data according to label indexes (order quantity of each course (target data) and common logarithm of order quantity of each course (target data)) of the last week (in the past seven days), and sorting according to weights of labels in the first name list to obtain a first name label sequence in a week; if the label index of the first part of the transformers (the target data, the first name of the target data or the first name label of the target data) in the last week is counted, the label index is obtained by calculating the ordered quantity of the first part of the transformers and the common logarithm of the order quantity;
calculating second name labels of courses (target data) in a second name list of target data according to label indexes (second name ordering amount of the same courses and common logarithm of second name ordering amount of the same courses in a database) of the last week (in the past seven days), and sorting according to weights of labels in the second name list to obtain a second label sequence in a week; if the label index of the transformers (the second name of the target data or the second name label of the target data) in the last week is counted, obtaining all ordered amounts and common logarithms of all order amounts of the first part of the transformers to the latest part of the transformers;
Sorting the labels of the target data according to the label index (the common logarithm of the ordering quantity of the courses (target data) of the same type and the ordering quantity of the courses of the same type) of the last week (in the past seven days), so as to obtain a third label sequence in the week; such as counting the label index of science fiction (type of target data or target data type label) in the last week, and obtaining by calculating all ordered amounts and common logarithms of all on-demand amounts of all courses (target data) with science fiction labels in a database;
sorting the labels of the target data according to the label index (order quantity per course+taking the common logarithm of order quantity per course) of the last month (in the past 30 days), so as to obtain a first label sequence in one month;
sorting the labels of the target data according to the IP index (same IP course ordering amount+taking the common logarithm of same IP course ordering amount) of the course in the last month (in the past 30 days), and obtaining a second label sequence in one month;
sorting the labels of the target data according to the class type index (class ordering quantity of the same type+taking the common logarithm of the order quantity of the same type) of the class in the last month (in the past 30 days), and obtaining a third label sequence in one month;
Sorting the labels of the target data according to the label index (order quantity per course+take the common logarithm of order quantity per course) of the last half year (in the past 180 days), so as to obtain a first label sequence in half year;
sorting the labels of the target data according to the label index (order quantity per course+taking the common logarithm of order quantity per course) of the last half year (in the past 180 days), so as to obtain a second label sequence in half year;
sorting the labels of the target data according to the label index (order quantity per course+taking the common logarithm of order quantity per course) of the last half year (in the past 180 days), so as to obtain a third label sequence in half year;
and respectively acquiring the first label sequence, the second label sequence and the third label sequence in one week, the first label sequence, the second label sequence and the third label sequence in one month and the first label sequence, the second label sequence and the third label sequence in half year to form the ranking table.
In one embodiment, the obtaining, in the database, the target data corresponding to the first recommendation list includes:
s310, when a user does not select a label of target data in the first recommendation list, determining the target data corresponding to the first recommendation list;
Acquiring target data matched with the label of the target data corresponding to the first recommendation list in a database to obtain first recommendation target data;
the first recommendation target data are ordered according to the ordering sequence of the labels of the target data in the ranking list, so that a second recommendation list is obtained;
s320 of, when the user selects a tag of the target data in the first recommendation list,
acquiring target data matched with the label of the selected target data in a database;
and sorting the target data according to the sorting order of the labels of the target data in the ranking table to obtain a second recommendation list.
In one embodiment, the obtaining recommended content according to the first recommendation list and the second recommendation list includes:
displaying a request list for inputting keywords by a user;
different display strategies are executed on the request list, the first recommendation list and the second recommendation list according to the moving direction or the position of the cursor so as to obtain the recommended content;
wherein the moving direction or position of the cursor is controlled by the user;
the recommended content includes one or more of the first, second, and third recommended content.
In this embodiment, keywords including one or more of age group, letters, and numbers are entered in the request list by the user; and stay on the request list through the user operation cursor, send the request to the database (back end server interface) in real time through the [ determining ] button trigger, transmit the keyword content, so that the keyword is matched with the field, the database (back end server interface) extracts the label of the target data associated with the keyword from the database according to the matching result, and sorts the label of the target data associated with the keyword according to the sorting order of the ranking list to form a first recommendation list.
The cursor moves to the first recommendation list, automatically triggers a request to a database (a back-end server interface), selects the label of the target data in the first recommendation list through the cursor, and the database (the back-end server interface) inputs the target data associated with the label of the target data in the first recommendation list from the database and sorts the target data according to the corresponding sorting order in the ranking list to form a second recommendation list.
In one embodiment, executing different exhibition strategies on the request list, the first recommendation list and the second recommendation list according to the position of the cursor comprises the following steps:
Displaying the request list, the first recommendation list and the second recommendation list to obtain first recommendation content; wherein the cursor is on a request list;
hiding or folding the request list when the cursor moves from the request list to the first recommendation list based on the first recommendation content, and displaying the first recommendation list and the second recommendation list to obtain second recommendation content;
based on the second recommended content, when the cursor moves from the first recommended list to the second recommended list, hiding or folding the request list and the first recommended list, and displaying the second recommended list to obtain the third recommended content.
In one embodiment, according to the moving direction of the cursor, different exhibition strategies are executed on the request list, the first recommendation list and the second recommendation list, including:
based on the third recommended content, when the cursor moves to a preset distance or a preset area in the second recommended list in a preset direction, displaying the first recommended list and the second recommended list to obtain the second recommended content;
and based on the second recommended content, when the cursor moves to a preset distance or a preset area in the first recommended list in a preset direction, displaying the request list, the first recommended list and the second recommended list to obtain the first recommended content.
One embodiment of the present invention further provides a personalized recommendation system, which is characterized by comprising:
the first acquisition module is used for acquiring the tag of the target data in the database; wherein the database stores at least one target data and a tag of the at least one target data;
the receiving module is used for receiving keywords input by a user, and the labels of the target data are obtained from a database according to the keywords;
the processing module is used for classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data;
the second acquisition module is used for acquiring target data corresponding to the first recommendation list from the database to obtain a second recommendation list;
and the generation module is used for obtaining recommended contents according to the first recommendation list and the second recommendation list.
In one embodiment, the generating module includes a display module, configured to display recommended content; the display module comprises an input area, a selection area and a result area, and is used for executing different display strategies on the request list, the first recommendation list and the second recommendation list according to the moving direction or the position of the cursor so as to display recommended contents; the method comprises the steps that a request list is arranged in an input area, a first recommendation list is arranged in a selection area, a label of target data in a database is obtained according to the working state of the input area or the working state of the request list, the label is displayed back to the selection area to obtain a first recommendation list, a second recommendation list is arranged in a result area, the label of the target data in the database is obtained according to the working state of the selection area or the working state of the first recommendation list, the label is displayed back to the selection area to obtain a second recommendation list, and different display strategies are executed on the request list, the first recommendation list and the second recommendation list through a display module to display recommendation contents; the working state of the input area or the working state of the request list comprises that a user does not input keywords in the input area or the request list and the user inputs keywords in the input area or the request list; the working state of the selection area or the working state of the first recommendation list comprises a label of which the user does not select target data in the selection area or the first recommendation list and a label of which the user selects target data in the selection area or the first recommendation list; when the first recommended content is displayed, the display module displays the input area, the selection area and the result area, when the second recommended content is displayed, the display module displays the selection area and the result area, and when the third recommended content is displayed, the display module displays the result area in a parallel, side-by-side or other mode layout arrangement so as to display on the display module, thereby further enhancing diversified and personalized display effects; the input area and the selection area can be gradually called out, and the calling out, hiding and folding modes of the input area, the selection area and the result area can be left and right flat pushing or up and down flat pushing, etc., so that diversified and personalized display effects are further enhanced.
One embodiment of the present invention further provides an electronic device, including: a processor and a memory;
wherein the memory is used for storing computer execution instructions;
a processor for executing computer-executable instructions stored in a memory to perform the steps of the personalized recommendation method according to any one of the embodiments described above. Reference may be made in particular to the relevant description of the embodiments of the method described above.
In the alternative, the memory may be separate or integrated with the processor.
When the memory is provided separately, the electronic device further comprises a bus for connecting the memory and the processor.
An embodiment of the present invention further provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the steps of the personalized recommendation method according to any one of the embodiments above
An embodiment of the invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the personalized recommendation method according to any of the embodiments described above.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit. The units formed by the modules can be realized in a form of hardware or a form of hardware and software functional units.
The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform some of the steps of the methods according to the embodiments of the application.
It should be understood that the above processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, and may also be a U-disk, a removable hard disk, a read-only memory, a magnetic disk or optical disk, etc.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an Extended industry standard architecture (Extended 15Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.
The storage medium may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). It is also possible that the processor and the storage medium reside as discrete components in an electronic device or a master device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (15)
1. A personalized recommendation method, comprising the steps of:
acquiring a label of target data in a database; wherein the database stores at least one target data and a tag of the at least one target data; when a keyword input by a user is received, the tag of the target data is obtained from a database according to the keyword;
Classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data;
acquiring target data corresponding to the first recommendation list from a database to obtain a second recommendation list;
and obtaining recommended content according to the first recommendation list and the second recommendation list.
2. The personalized recommendation method of claim 1, wherein the tag of the target data is obtained from the database according to the keyword, comprising:
processing the keywords, and determining tags of target data matched with the keywords in the database; the method comprises the steps that at least one field of a label is stored in a database, the field of the label is formed by splicing initial letters of each Chinese character pinyin of the label, and an index for recalling the label is arranged in the field;
and recalling the label of the target data matched with the keyword in the database according to the index of the field.
3. The personalized recommendation method of claim 2, wherein processing the keywords comprises:
acquiring keywords input by a user; wherein the keywords include one or more of age groups, letters, and numbers;
when the keyword comprises letters, determining the field of the label matched with the letters in the keyword according to the matching result of the letters in the keyword and the fields of the labels in the database;
converting letters in the keywords into characters;
based on adjacent characters of the Chinese characters in the labels, word segmentation is carried out on the characters;
inquiring in a database according to the segmentation;
when the keyword comprises a number, inquiring in a database according to the number;
when the keywords comprise age groups, inquiring target data matched with the age groups in a database according to the age groups; and determining target data matched with the age bracket according to the age label, wherein the age label is formed by self-defining according to basic information of the target data.
4. The personalized recommendation method of claim 1, further comprising:
When the keyword input by the user is not received, the label of the target data is obtained from the database according to the order quantity and the order quantity of each target data in the database;
or when the keyword input by the user is not received, the label of the target data is obtained from a database according to the history of the user, and the label is matched with the target data in the history;
when the keywords input by the user are not received, the first recommendation list comprises one or more of a search history list, a first name list of hot search target data, a second name list of a hot search target and a hot search target type list.
5. The personalized recommendation method of claim 4, wherein the classifying outputting the tag of the target data comprises:
classifying tags of the target data, wherein the tags of the target data comprise one or more of a first name tag recorded according to a first name of the target data, a second name tag recorded according to a second name of the target data and a target data type tag recorded according to a type of the target data;
sequentially sorting the labels of the classified targets according to the sorting order of the labels of the target data in the ranking table;
Outputting the label of the ordered target data;
the ranking table is obtained by ranking the labels of the target data classified in the database according to the order quantity and the order quantity of the target data.
6. The personalized recommendation method of claim 5, wherein classifying the tag of the target data comprises:
obtaining a label of target data matched with the first name label in the labels of the target data, so as to obtain a first name list of guessed target data or a first name list of hot search target data after sorting;
obtaining a label of target data matched with the second name label in the labels of the target data, so as to obtain a second name list of guessed target data or a second name list of hot search target data after sorting;
obtaining tags matched with the types of the target type tags in the tags of the target data, and obtaining a guessed target data type list or a hot search target data type list after sorting;
and acquiring a label matched with the first name label in the labels of the target data based on the history record, so as to obtain a search history list after sorting according to the time stamp of the history record.
7. The personalized recommendation method of claim 5, wherein the ranking table is obtained by sorting tags of the target data classified in the database according to the on-demand amount and the order amount of the target data, comprising:
Acquiring on-demand data and ordering data of target data to form a history record of a user so as to count the on-demand quantity and ordering quantity of each target data in a database; wherein the history record has a timestamp;
and executing different statistical strategies on the labels of the target data in the database based on the on-demand quantity and the ordering quantity of each target data in the database to obtain a ranking table.
8. The personalized recommendation method of claim 7, wherein performing different statistical policies on labels of target data in the database based on-demand and subscription amounts of each target data in the database comprises:
determining the order quantity and the order quantity of each label in the database according to the order quantity and the order quantity of each target data;
classifying tags of target data in a database to obtain one or more of a first name list of the target data, a second name list of the target data and a type list of the target data;
the labels of the target data in the target data first name list, the target data second name list and the target data type list are respectively ordered according to the weight of the label index, and the ranking list is obtained;
Wherein the tag index is configured to: adding the ordered quantity of each label in the database and the common logarithm of the on-demand quantity in a preset time period;
the preset time period includes one or more of a week, a half month, a half year, and a year.
9. The personalized recommendation method of claim 1, wherein the obtaining, in a database, target data corresponding to the first recommendation list comprises:
when the user does not select the label of the target data in the first recommendation list, determining the target data corresponding to the first recommendation list;
acquiring target data matched with the label of the target data corresponding to the first recommendation list in a database to obtain first recommendation target data;
the first recommendation target data are ordered according to the ordering sequence of the labels of the target data in the ranking list, so that a second recommendation list is obtained;
when the user selects the tag of the target data in the first recommendation list,
acquiring target data matched with the label of the selected target data in a database;
and sorting the target data according to the sorting order of the labels of the target data in the ranking table to obtain a second recommendation list.
10. The personalized recommendation method according to any one of claims 1 to 9, wherein obtaining recommended content according to the first recommendation list and the second recommendation list comprises:
displaying a request list for inputting keywords by a user;
different display strategies are executed on the request list, the first recommendation list and the second recommendation list according to the moving direction or the position of the cursor so as to obtain the recommended content;
wherein the moving direction or position of the cursor is controlled by the user;
the recommended content includes one or more of the first, second, and third recommended content.
11. The personalized recommendation method of claim 10, wherein performing different presentation policies on the request list, the first recommendation list, and the second recommendation list based on a position of a cursor comprises:
displaying the request list, the first recommendation list and the second recommendation list to obtain first recommendation content; wherein the cursor is on a request list;
hiding or folding the request list when the cursor moves from the request list to the first recommendation list based on the first recommendation content, and displaying the first recommendation list and the second recommendation list to obtain second recommendation content;
Based on the second recommended content, when the cursor moves from the first recommended list to the second recommended list, hiding or folding the request list and the first recommended list, and displaying the second recommended list to obtain the third recommended content.
12. The personalized recommendation method of claim 10, wherein performing different presentation policies on the request list, the first recommendation list, and the second recommendation list according to a moving direction of a cursor comprises:
based on the third recommended content, when the cursor moves to a preset distance or a preset area in the second recommended list in a preset direction, displaying the first recommended list and the second recommended list to obtain the second recommended content;
and based on the second recommended content, when the cursor moves to a preset distance or a preset area in the first recommended list in a preset direction, displaying the request list, the first recommended list and the second recommended list to obtain the first recommended content.
13. A personalized recommendation system, comprising:
the first acquisition module is used for acquiring the tag of the target data in the database; wherein the database stores at least one target data and a tag of the at least one target data;
The receiving module is used for receiving keywords input by a user, and the labels of the target data are obtained from a database according to the keywords;
the processing module is used for classifying and outputting the labels of the target data to obtain a first recommendation list; when receiving keywords input by a user, the first recommendation list comprises one or more of a first name list of guessed target data, a second name list of guessed target data and a type list of guessed target data;
the second acquisition module is used for acquiring target data corresponding to the first recommendation list from the database to obtain a second recommendation list;
and the generation module is used for obtaining recommended contents according to the first recommendation list and the second recommendation list.
14. An electronic device, comprising: a processor and a memory;
wherein the memory is used for storing computer execution instructions;
a processor for executing computer-executable instructions stored in memory to perform the steps of the personalized recommendation method according to any one of claims 1 to 12.
15. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the individual steps of the personalized recommendation method according to any one of claims 1 to 12.
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| CN109299399A (en) * | 2018-12-17 | 2019-02-01 | 广东小天才科技有限公司 | Learning content recommendation method and terminal equipment |
| CN111199428A (en) * | 2020-01-03 | 2020-05-26 | 江苏苏宁物流有限公司 | Commodity recommendation method and device, storage medium and computer equipment |
| CN111414375A (en) * | 2020-02-29 | 2020-07-14 | 深圳壹账通智能科技有限公司 | Input recommendation method based on database query, electronic device and storage medium |
| US20210209109A1 (en) * | 2020-06-29 | 2021-07-08 | Beijing Baidu Netcom Science Technology Co., Ltd. | Method, apparatus, device, and storage medium for intention recommendation |
| CN114528486A (en) * | 2022-02-08 | 2022-05-24 | 掌阅科技股份有限公司 | Book recommendation method, server, system and storage medium |
| CN115587244A (en) * | 2022-09-28 | 2023-01-10 | 云南腾云信息产业有限公司 | User interest dimension acquisition and recommendation method, device, equipment and storage medium |
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