CN113515703A - Information recommendation method and device, electronic equipment and readable storage medium - Google Patents
Information recommendation method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The invention relates to the field of intelligent decision making, and discloses an information recommendation method, which comprises the following steps: acquiring an information recommendation request and historical recommendation information of a user; training the pre-constructed information recommendation model according to the historical recommendation information to obtain an updated information recommendation model; acquiring an information data set according to the information recommendation request, and extracting information characteristics in each information data in the information data set; extracting user attributes in the user information recommendation request to obtain user characteristics; calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information characteristics and the user characteristics; and screening and sorting all the information data according to the recommendation coefficients to obtain a recommendation information data sequence and sending the recommendation information data sequence to preset terminal equipment. The invention also relates to a blockchain technique, the information data set can be stored in blockchain link points. The invention also provides an information recommendation device, equipment and a storage medium. The invention can improve the accuracy of information recommendation.
Description
Technical Field
The invention relates to the field of intelligent decision making, in particular to an information recommendation method and device, electronic equipment and a readable storage medium.
Background
Real-time personalized information data recommendation is a very challenging problem due to the timeliness of information data and the volatility of user preferences. Although some real-time recommendation models exist in the industry at present to solve the problem of information data recommendation, the information recommended by the recommendation models only considers click feedback of users, the model feedback evaluation dimensionality is small, the recommendation models cannot push the information really needed by the users, and the accuracy of information recommendation is low.
Disclosure of Invention
The invention provides an information recommendation method, an information recommendation device, electronic equipment and a computer readable storage medium, and mainly aims to improve the accuracy of information recommendation.
In order to achieve the above object, an information recommendation method provided by the present invention includes:
when an information recommendation request of a user is received, acquiring history recommendation information corresponding to all history information recommendation requests of the user;
performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute respectively;
extracting the user attribute in the user information recommendation request to obtain the user characteristics;
calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics;
and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
Optionally, the iteratively training the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model includes:
acquiring request time of the information recommendation requests, and extracting the request time in each historical information recommendation request;
combining all the extracted request times according to the time sequence to obtain a request time sequence;
judging whether the quantity of the request time in the request time sequence is greater than 1;
when the quantity of the request time in the request time sequence is more than 1, screening all the historical recommendation information according to the request time sequence to obtain target historical recommendation information, and training a current information recommendation model by using the target historical recommendation information to obtain an updated information recommendation model;
and when the number of the request time in the request time sequence is equal to 1, determining the current information recommendation model as an updated information recommendation model.
Optionally, the screening all the historical recommendation information according to the request time sequence to obtain target historical recommendation information, and training a current information recommendation model by using the target historical recommendation information to obtain an updated information recommendation model, including:
selecting the last request time and the last request time in the request time sequence to construct a target time interval;
selecting historical recommendation information corresponding to the penultimate request time in the request time sequence to obtain the target historical recommendation information, and acquiring user click information corresponding to each information data in the target historical recommendation information in the target time interval range;
calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm according to the user click information;
extracting the user click times in the user click information, and calculating according to the user click times and the activity probability to obtain a historical recommendation coefficient;
and performing iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain the updated information recommendation model.
Optionally, the iteratively training the information recommendation model according to the target historical recommendation information and the recommendation coefficient to obtain an updated information recommendation model includes:
step A: performing characteristic conversion on each information data in the target historical recommendation information to obtain an initial sample;
and B: marking the initial sample by using the historical recommendation coefficient to obtain a training sample;
and C: predicting the initial sample by using the information recommendation model to obtain a prediction coefficient;
step D: calculating a loss value of the prediction coefficient and a historical recommendation coefficient corresponding to the training sample by using a preset loss function, and when the loss value is greater than or equal to the preset loss threshold value, adjusting model parameters of the information recommendation model and returning to the step C; and stopping training the information recommendation model when the loss value is smaller than a preset loss threshold value to obtain an updated information recommendation model.
Optionally, the obtaining an information data set according to the information recommendation request includes:
determining the request time of the information recommendation request as target request time;
constructing a recommendation time interval according to the target request time and a preset time period;
and screening all information data in a preset information recommendation database by using the recommendation time interval to obtain the information data set.
Optionally, the constructing a recommended time interval according to the target request time and a preset time period includes:
taking the target request time as an interval right end point;
taking the time period as an interval length;
and constructing an interval according to the interval right endpoint and the interval length to obtain the recommended time interval.
Optionally, the screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence includes:
screening the information data of which the recommendation coefficient value is greater than a preset threshold value in the information data set to obtain an initial information data sequence;
and sequencing all the information data in the initial information data sequence according to the magnitude sequence of the corresponding recommendation coefficients to obtain the recommendation information data sequence.
In order to solve the above problem, the present invention also provides an information recommendation apparatus, including:
the model updating module is used for acquiring historical recommendation information corresponding to all historical information recommendation requests of a user when the information recommendation requests of the user are received; performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
the characteristic extraction module is used for acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and respectively extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute; extracting the user attribute in the user information recommendation request to obtain the user characteristics;
the information recommendation module is used for calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics; and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and a processor executing the computer program stored in the memory to implement the information recommendation method.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the information recommendation method described above.
The embodiment of the invention obtains the historical recommendation information corresponding to all the historical information recommendation requests of a user when receiving the information recommendation requests of the user; performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model, and updating the model by using feedback of the user on information beneficial to recommendation, so that the performance of the model is better and more accurate in prediction; acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute respectively; extracting the user attribute in the user information recommendation request to obtain the user characteristics; according to the information interaction characteristics, the information content characteristics and the user characteristics, the updated information recommendation model is used for calculating the recommendation coefficient of each information data in the information data set, the model is trained by using the multidimensional characteristics, the robustness of the model is better, and the accuracy of information recommendation is improved; and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user. Therefore, the information recommendation method, the information recommendation device, the electronic equipment and the readable storage medium provided by the embodiment of the invention improve the accuracy of information recommendation.
Drawings
Fig. 1 is a schematic flowchart of an information recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of an information recommendation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device implementing an information recommendation method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides an information recommendation method. The execution subject of the information recommendation method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the information recommendation method may be executed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a schematic flow chart of an information recommendation method according to an embodiment of the present invention is shown, where in the embodiment of the present invention, the information recommendation method includes:
s1, acquiring historical recommendation information corresponding to all historical information recommendation requests of a user when receiving the information recommendation requests of the user;
in the embodiment of the present invention, the information recommendation request includes: the request time of the user, the user attribute and the personal information of the user.
Specifically, in the embodiment of the present invention, the history information recommendation request is an information recommendation request initiated by the user before the information recommendation request of this time is initiated, and therefore, the history information recommendation request also includes: the request time of the user, the user attribute and the personal information of the user. The historical recommendation information is information recommended in response to the corresponding historical information recommendation request.
S2, performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
optionally, in the embodiment of the present invention, the current information recommendation model is a deep Q network model.
In detail, in the embodiment of the present invention, the iteratively training a current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model includes: extracting the request time in each historical information recommendation request and the request time in the information recommendation request; combining all the extracted request times according to the time sequence to obtain a request time sequence, for example: the information recommendation request is the request time corresponding to the information recommendation request of the user A is t4, the user A initiates the user information recommendation request three times before initiating the user information recommendation request, three historical recommendation requests are available, the request time corresponding to the first historical recommendation request is t1, the request time corresponding to the second historical recommendation request is t2, the request time corresponding to the third historical recommendation request is t3, t1< t2< t3, t4> t3, and the request time sequence is [ t1, t2, t3, t4 ]; further, in the embodiment of the present invention, it is determined whether the number of request times in the request time sequence is greater than 1, where the number of request times in the request time sequence is the number of request times included in the request time sequence, for example: the request time sequence is [ t1, t2, t3, t4], including request times t1, t2, t3, t4, then the number of request times in the request time sequence is 4; when the quantity of the request time in the request time sequence is more than 1, screening all the historical recommendation information according to the request time sequence to obtain target historical recommendation information, and training the information recommendation model by using the target historical recommendation information to obtain the updated information recommendation model; and when the quantity of the request time in the request time sequence is equal to 1, determining the information recommendation model as the updated information recommendation model.
In detail, in the embodiment of the present invention, the target time interval is constructed by selecting the penultimate request time and the penultimate request time in the request time sequence, for example: the request time sequence is [ t1, t2, t3, t4], and as can be seen from the above, the times in the request time sequence are arranged according to the chronological order, so that the time interval constructed by the last request time t4 and the last request time t3 in the request time sequence is [ t3, t4 ].
Specifically, in the embodiment of the present invention, the screening all the historical recommendation information according to the request time sequence to obtain the target historical recommendation information includes: and selecting historical recommendation information corresponding to the last but one request time in the request time sequence to obtain target historical recommendation information.
Further, user click information corresponding to each information data in the target historical recommendation information in the target time interval range is obtained; and calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm according to the user click information. The user click information comprises user click times, times and time for the user to return to a system where the browsing information data are located. For example: information data news data in a news APP, the times and time that the user returns to the system where the information data is located are the times and time that the user opens the APP.
Further, in the embodiment of the present invention, the number of user clicks in the user click information is extracted, and calculation is performed according to the number of user clicks and the activity probability to obtain a historical recommendation coefficient, for example: the number of clicks of the user corresponding to the information data A is 5, the activity probability is 0.6, and then the historical recommendation coefficient corresponding to the information data A is 5+ 0.6-5.6; and performing iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain an updated information recommendation model, wherein optionally, the information recommendation model in the embodiment of the invention is a deep Q learning model.
Optionally, the preset algorithm in the embodiment of the present invention may be performed by using the following formula:
wherein S (t) is the active probability of the user, λ is the set risk function, t2Is the penultimate time, t1Is the penultimate time, SaResetting the user activity probability to be a preset initial activity degree when the user returns to the system where the browsing information data is located each time in the target time interval
And h is the system time when the user returns the browsing information data last time in the target time interval.
Further, in the embodiment of the present invention, calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm includes: and when the active probability is greater than a preset value, determining the preset value as the active probability, wherein the preset value is 1.
In summary, in the embodiment of the present invention, the training the information recommendation model by using the target historical recommendation information to obtain an updated information recommendation model includes: selecting a last request time and a last request time in the request time sequence to construct an interval to obtain a target time interval; acquiring user click information corresponding to each information data in the target historical recommendation information within the target time interval range; calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm according to the user click information; extracting the user click times in the user click information, and calculating according to the user click times and the activity probability to obtain a historical recommendation coefficient; and performing iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain the updated information recommendation model.
In detail, in the embodiment of the present invention, the iteratively training the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain the updated information recommendation model includes:
step A: performing characteristic conversion on each information data in the target historical recommendation information to obtain a corresponding initial sample;
in detail, the embodiment of the present invention performs feature transformation on each information data in the target history recommendation information to obtain a corresponding initial sample, including: and extracting information content characteristics, information interaction characteristics and user characteristics corresponding to each information data in the target historical recommendation information to obtain corresponding initial samples.
And B: marking the corresponding initial sample by using the historical recommendation coefficient to obtain a training sample;
and C: predicting the initial sample by using the information recommendation model to obtain a prediction coefficient;
step D: calculating a loss value of the prediction coefficient and a historical recommendation coefficient corresponding to the training sample by using a preset loss function, and when the loss value is greater than or equal to the preset loss threshold value, adjusting model parameters of the information recommendation model and returning to the step C; and stopping training the information recommendation model when the loss value is smaller than a preset loss threshold value to obtain an updated information recommendation model.
S3, acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and respectively extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute;
in the embodiment of the present invention, the information data set includes a plurality of information data sets. In one application scenario of the present invention, the information data is news.
In detail, the acquiring an information data set according to the information recommendation request in the embodiment of the present invention includes: determining the request time of the information recommendation request as target request time; constructing a recommendation time interval according to the target request time and a preset time period; specifically, the embodiment of the present invention takes the target request time as an interval right endpoint; taking the time period as an interval length; constructing an interval according to the interval right endpoint and the interval length to obtain the recommended time interval, for example: the target request time is 12:00 and the time period is 1 hour, then the constructed recommended time interval is 11:00, 12: 00. . Further, in the embodiment of the present invention, all information data in a preset information database are screened by using the recommended time interval, so as to obtain the information data set. The information database is updated continuously along with time, and further, in order to ensure timeliness of information recommendation, the embodiment of the invention does not need old data in the information database, so that the embodiment of the invention screens all information data in a preset information database by using the recommendation time interval, screens the information data in the information database with data writing time within the recommendation time interval, and obtains the information data set.
Specifically, the information content attributes are characteristics of some attributes of the corresponding information data, including title, provider, rank, entity name, category, topic category, and number of clicks of the last 1 hour, 6 hours, 24 hours, 1 week, and 1 year, respectively. In the embodiment of the invention, the information content attribute of each information data in the information data set is converted into a vector to obtain the corresponding information content characteristic.
Further, in this embodiment of the present invention, the information interaction attribute is interaction between a user and corresponding information data, and includes a frequency of occurrence of a corresponding entity attribute of the information data in a reading history of the user, where the entity attribute includes: a category of information data, a subject category of information data, and a provider of information data. And converting the corresponding information interaction attributes in the information data set into vectors to obtain the corresponding information interaction characteristics.
In another embodiment of the invention, the information data set is stored in the block link points by utilizing the characteristic of high throughput of the block chain, so that the data access efficiency is improved.
S4, extracting the user attribute in the user information recommendation request to obtain the user characteristics;
in the embodiment of the present invention, the user attribute is a characteristic of information browsed by the user within a preset time period, such as a characteristic of news (i.e., title, provider, rank, entity name, category, and topic category) clicked by the user within 1 hour, 6 hours, 24 hours, 1 week, and 1 year, respectively.
Further, the embodiment of the present invention converts the user attribute into a vector, so as to obtain the user characteristic.
Optionally, in the embodiment of the present invention, a Word2vec model formed by transfer learning training may be used for vector conversion based on a preset text (e.g., teaching materials, training materials) based on professional domain knowledge.
S5, calculating a recommendation coefficient of each information data in the information data set by using the information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics;
in detail, in the embodiment of the present invention, information interaction characteristics, information content characteristics, and user characteristics corresponding to each information data in the information data set are input to the information recommendation model to obtain a corresponding recommendation coefficient.
S6, screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the terminal equipment corresponding to the user information recommendation request.
In detail, in the embodiment of the present invention, information data corresponding to the recommended coefficient value greater than a preset threshold value in the information data set are screened to obtain an initial information data sequence; sorting all information data in the initial information data sequence according to the magnitude sequence of the corresponding recommendation coefficients to obtain the recommendation information data sequence, for example: the information data set has five information data of A, B, C, D and E in total, the recommendation coefficient corresponding to A is 0.9, the recommendation coefficient corresponding to B is 0.91, the recommendation coefficient corresponding to C is 0.8, the recommendation coefficient corresponding to D is 0.92, the recommendation coefficient corresponding to E is 0.7, and the preset threshold value is 0.8, so that the initial information sequence comprises A, B and D; and sequencing all the information data in the initial information data sequence according to the size sequence of the corresponding recommendation coefficients to obtain the recommendation information data sequence [ D, B, A ].
Further, in this embodiment of the present invention, the recommendation information data sequence is sent to a terminal device corresponding to the user information recommendation request, where the terminal device includes: intelligent terminals such as mobile phones, tablets and computers.
Fig. 2 is a functional block diagram of the information recommendation apparatus according to the present invention.
The information recommendation device 100 of the present invention may be installed in an electronic device. According to the implemented functions, the information recommendation device may include a model update module 101, a feature extraction module 102, and an information recommendation module 103, which may also be referred to as a unit, and refer to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the model updating module 101 is configured to, when receiving an information recommendation request of a user, obtain history recommendation information corresponding to all history information recommendation requests of the user; performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
in the embodiment of the present invention, the information recommendation request includes: the request time of the user, the user attribute and the personal information of the user.
Specifically, in the embodiment of the present invention, the history information recommendation request is an information recommendation request initiated by the user before the information recommendation request of this time is initiated, and therefore, the history information recommendation request also includes: the request time of the user, the user attribute and the personal information of the user. The historical recommendation information is information recommended in response to the corresponding historical information recommendation request.
Optionally, in the embodiment of the present invention, the current information recommendation model is a deep Q network model.
In detail, in the embodiment of the present invention, the iteratively training the current information recommendation model by the model updating module 101 according to the historical recommendation information to obtain an updated information recommendation model, where the iteratively training includes: extracting the request time in each historical information recommendation request and the request time in the information recommendation request; combining all the extracted request times according to the time sequence to obtain a request time sequence, for example: the information recommendation request is the request time corresponding to the information recommendation request of the user A is t4, the user A initiates the user information recommendation request three times before initiating the user information recommendation request, three historical recommendation requests are available, the request time corresponding to the first historical recommendation request is t1, the request time corresponding to the second historical recommendation request is t2, the request time corresponding to the third historical recommendation request is t3, t1< t2< t3, t4> t3, and the request time sequence is [ t1, t2, t3, t4 ]; further, in the embodiment of the present invention, it is determined whether the number of request times in the request time sequence is greater than 1, where the number of request times in the request time sequence is the number of request times included in the request time sequence, for example: the request time sequence is [ t1, t2, t3, t4], including request times t1, t2, t3, t4, then the number of request times in the request time sequence is 4; when the number of request time in the request time sequence is greater than 1, the model updating module 101 screens all the historical recommendation information according to the request time sequence to obtain target historical recommendation information, and trains the information recommendation model by using the target historical recommendation information to obtain the updated information recommendation model; when the number of request times in the request time sequence is equal to 1, the model updating module 101 determines the information recommendation model as the updated information recommendation model.
Specifically, in the embodiment of the present invention, the screening, by the model updating module 101, all the historical recommendation information according to the request time sequence to obtain the target historical recommendation information includes: and selecting historical recommendation information corresponding to the second request time of the derivative in the request time sequence to obtain target historical recommendation information.
In detail, in the embodiment of the present invention, the model updating module 101 selects a last request time and a last request time construction interval in the request time sequence to obtain a target time interval, for example: the request time sequence is [ t1, t2, t3, t4], and as can be seen from the above, the times in the request time sequence are arranged according to the chronological order, so that the time interval constructed by the last request time t4 and the last request time t3 in the request time sequence is [ t3, t4 ].
Further, the model updating module 101 obtains user click information corresponding to each information data in the target historical recommendation information within the target time interval range; and calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm according to the user click information. The user click information comprises user click times, times and time for the user to return to a system where the browsing information data are located. For example: information data news data in a news APP, the times and time that the user returns to the system where the information data is located are the times and time that the user opens the APP.
Further, in the embodiment of the present invention, the model updating module 101 extracts the user click times in the user click information, and calculates according to the user click times and the active probability to obtain a historical recommendation coefficient, for example: the number of clicks of the user corresponding to the information data A is 5, the activity probability is 0.6, and then the historical recommendation coefficient corresponding to the information data A is 5+ 0.6-5.6; and performing iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain an updated information recommendation model, wherein optionally, the information recommendation model in the embodiment of the invention is a deep Q learning model.
Optionally, the preset algorithm in the embodiment of the present invention may be performed by using the following formula:
wherein S (t) is the active probability of the user, λ is the set risk function, t2Is the penultimate time, t1Is the penultimate time, SaResetting the user activity probability to be a preset initial activity degree when the user returns to the system where the browsing information data is located each time in the target time interval
And h is the system time when the user returns the browsing information data last time in the target time interval.
Further, in the embodiment of the present invention, the calculating, by the model updating module 101, the activity probability of each information data in the target historical recommendation information by using a preset algorithm includes: and when the active probability is greater than a preset value, determining the preset value as the active probability, wherein the preset value is 1.
In summary, in the embodiment of the present invention, the training of the information recommendation model by the model updating module 101 using the target historical recommendation information to obtain an updated information recommendation model includes: selecting a last request time and a last request time in the request time sequence to construct an interval to obtain a target time interval; acquiring user click information corresponding to each information data in the target historical recommendation information within the target time interval range; calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm according to the user click information; extracting the user click times in the user click information, and calculating according to the user click times and the activity probability to obtain a historical recommendation coefficient; and performing iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain the updated information recommendation model.
In detail, in the embodiment of the present invention, the model updating module 101 performs iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain the updated information recommendation model, including:
step A: performing characteristic conversion on each information data in the target historical recommendation information to obtain a corresponding initial sample;
in detail, the embodiment of the present invention performs feature transformation on each information data in the target history recommendation information to obtain a corresponding initial sample, including: and extracting information content characteristics, information interaction characteristics and user characteristics corresponding to each information data in the target historical recommendation information to obtain corresponding initial samples.
And B: marking the corresponding initial sample by using the historical recommendation coefficient to obtain a training sample;
and C: predicting the initial sample by using the information recommendation model to obtain a prediction coefficient;
step D: calculating a loss value of the prediction coefficient and a historical recommendation coefficient corresponding to the training sample by using a preset loss function, and when the loss value is greater than or equal to the preset loss threshold value, adjusting model parameters of the information recommendation model and returning to the step C; and stopping training the information recommendation model when the loss value is smaller than a preset loss threshold value to obtain an updated information recommendation model.
The feature extraction module 102 is configured to obtain an information data set according to the information recommendation request, extract an information content attribute and an information interaction attribute of each information data in the information data set, and extract a corresponding information content feature and an information interaction feature from the information content attribute and the information interaction attribute, respectively; extracting the user attribute in the user information recommendation request to obtain the user characteristics;
in the embodiment of the present invention, the information data set includes a plurality of information data sets. In one application scenario of the present invention, the information data is news.
In detail, in the embodiment of the present invention, the obtaining, by the feature extraction module 102, an information data set according to the information recommendation request includes: determining the request time corresponding to the information recommendation request as target request time; constructing a recommendation time interval according to the target request time and a preset time period; specifically, the embodiment of the present invention takes the target request time as an interval right endpoint; taking the time period as an interval length; constructing an interval according to the interval right endpoint and the interval length to obtain the recommended time interval, for example: the target request time is 12:00 and the time period is 1 hour, then the constructed recommended time interval is 11:00, 12: 00. . Further, in the embodiment of the present invention, the feature extraction module 102 utilizes the recommended time interval to filter all information data in a preset information database, so as to obtain the information data set. Further, in order to ensure timeliness of information recommendation, the embodiment of the present invention does not require old data in the information database, and therefore, the feature extraction module 102 in the embodiment of the present invention screens all information data in a preset information database by using the recommendation time interval, and screens information data in the information database whose data writing time is within the range of the recommendation time interval, so as to obtain the information data set.
Specifically, the information content attributes are characteristics of some attributes of the corresponding information data, including title, provider, rank, entity name, category, topic category, and number of clicks of the last 1 hour, 6 hours, 24 hours, 1 week, and 1 year, respectively. In the embodiment of the invention, the information content attribute of each information data in the information data set is converted into a vector to obtain the corresponding information content characteristic.
Further, in this embodiment of the present invention, the information interaction attribute is interaction between a user and corresponding information data, and includes a frequency of occurrence of a corresponding entity attribute of the information data in a reading history of the user, where the entity attribute includes: a category of information data, a subject category of information data, and a provider of information data. And converting the corresponding information interaction attributes in the information data set into vectors to obtain the corresponding information interaction characteristics.
In another embodiment of the invention, the information data set is stored in the block link points by utilizing the characteristic of high throughput of the block chain, so that the data access efficiency is improved.
In the embodiment of the present invention, the user attribute is a characteristic of information browsed by the user within a preset time period, such as a characteristic of news (i.e., title, provider, rank, entity name, category, and topic category) clicked by the user within 1 hour, 6 hours, 24 hours, 1 week, and 1 year, respectively.
Further, the feature extraction module 102 in the embodiment of the present invention converts the user attribute into a vector, so as to obtain the user feature.
Optionally, in the embodiment of the present invention, a Word2vec model formed by transfer learning training may be used for vector conversion based on a preset text (e.g., teaching materials, training materials) based on professional domain knowledge.
The information recommendation module 103 is configured to calculate a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction feature, the information content feature, and the user feature; and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
In detail, in the embodiment of the present invention, the information recommendation module 103 inputs the information interaction characteristics, the information content characteristics, and the user characteristics corresponding to each information data in the information data set to the information recommendation model to obtain a corresponding recommendation coefficient.
In detail, in the embodiment of the present invention, the information recommendation module 103 filters information data, in the information data set, of which the recommendation coefficient value is greater than a preset threshold value, to obtain an initial information data sequence; sorting all information data in the initial information data sequence according to the magnitude sequence of the corresponding recommendation coefficients to obtain the recommendation information data sequence, for example: the information data set has five information data of A, B, C, D and E in total, the recommendation coefficient corresponding to A is 0.9, the recommendation coefficient corresponding to B is 0.91, the recommendation coefficient corresponding to C is 0.8, the recommendation coefficient corresponding to D is 0.92, the recommendation coefficient corresponding to E is 0.7, and the preset threshold value is 0.8, so that the initial information sequence comprises A, B and D; and sequencing all the information data in the initial information data sequence according to the size sequence of the corresponding recommendation coefficients to obtain the recommendation information data sequence [ D, B, A ].
Further, in this embodiment of the present invention, the recommendation information data sequence is sent to a terminal device corresponding to the user information recommendation request, where the terminal device includes: intelligent terminals such as mobile phones, tablets and computers.
Fig. 3 is a schematic structural diagram of an electronic device implementing the information recommendation method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as an information recommendation program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of an information recommendation program, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., information recommendation programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication bus 12 may be a PerIPheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The information recommendation program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which when executed in the processor 10, can realize:
when an information recommendation request of a user is received, acquiring history recommendation information corresponding to all history information recommendation requests of the user;
performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute respectively;
extracting the user attribute in the user information recommendation request to obtain the user characteristics;
calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics;
and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
when an information recommendation request of a user is received, acquiring history recommendation information corresponding to all history information recommendation requests of the user;
performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute respectively;
extracting the user attribute in the user information recommendation request to obtain the user characteristics;
calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics;
and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. An information recommendation method, characterized in that the method comprises:
when an information recommendation request of a user is received, acquiring history recommendation information corresponding to all history information recommendation requests of the user;
performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute respectively;
extracting the user attribute in the user information recommendation request to obtain the user characteristics;
calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics;
and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
2. The information recommendation method of claim 1, wherein the iteratively training a current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model comprises:
acquiring request time of the information recommendation requests, and extracting the request time in each historical information recommendation request;
combining all the extracted request times according to the time sequence to obtain a request time sequence;
judging whether the quantity of the request time in the request time sequence is greater than 1;
when the quantity of the request time in the request time sequence is more than 1, screening all the historical recommendation information according to the request time sequence to obtain target historical recommendation information, and training a current information recommendation model by using the target historical recommendation information to obtain an updated information recommendation model;
and when the number of the request time in the request time sequence is equal to 1, determining the current information recommendation model as an updated information recommendation model.
3. The information recommendation method according to claim 2, wherein the step of screening all the historical recommendation information according to the request time series to obtain target historical recommendation information, and training a current information recommendation model by using the target historical recommendation information to obtain an updated information recommendation model comprises:
selecting the last request time and the last request time in the request time sequence to construct a target time interval;
selecting historical recommendation information corresponding to the penultimate request time in the request time sequence to obtain the target historical recommendation information, and acquiring user click information corresponding to each information data in the target historical recommendation information in the target time interval range;
calculating the activity probability of each information data in the target historical recommendation information by using a preset algorithm according to the user click information;
extracting the user click times in the user click information, and calculating according to the user click times and the activity probability to obtain a historical recommendation coefficient;
and performing iterative training on the information recommendation model according to the target historical recommendation information and the historical recommendation coefficient to obtain the updated information recommendation model.
4. The information recommendation method of claim 3, wherein the iteratively training the information recommendation model according to the target historical recommendation information and the recommendation coefficient to obtain the updated information recommendation model comprises:
step A: performing characteristic conversion on each information data in the target historical recommendation information to obtain an initial sample;
and B: marking the initial sample by using the historical recommendation coefficient to obtain a training sample;
and C: predicting the initial sample by using the information recommendation model to obtain a prediction coefficient;
step D: calculating a loss value of the prediction coefficient and a historical recommendation coefficient corresponding to the training sample by using a preset loss function, and when the loss value is greater than or equal to the preset loss threshold value, adjusting model parameters of the information recommendation model and returning to the step C; and stopping training the information recommendation model when the loss value is smaller than a preset loss threshold value to obtain an updated information recommendation model.
5. The information recommendation method of claim 2, wherein said obtaining an information data set according to the information recommendation request comprises:
determining the request time of the information recommendation request as target request time;
constructing a recommendation time interval according to the target request time and a preset time period;
and screening all information data in a preset information recommendation database by using the recommendation time interval to obtain the information data set.
6. The information recommendation method of claim 5, wherein the constructing a recommendation time interval according to the target request time and a preset time period comprises:
taking the target request time as an interval right end point;
taking the time period as an interval length;
and constructing an interval according to the interval right endpoint and the interval length to obtain the recommended time interval.
7. The information recommendation method according to any one of claims 1 to 6, wherein the filtering and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommended information data sequence comprises:
screening the information data of which the recommendation coefficient value is greater than a preset threshold value in the information data set to obtain an initial information data sequence;
and sequencing all the information data in the initial information data sequence according to the magnitude sequence of the corresponding recommendation coefficients to obtain the recommendation information data sequence.
8. An information recommendation method, comprising:
the model updating module is used for acquiring historical recommendation information corresponding to all historical information recommendation requests of a user when the information recommendation requests of the user are received; performing iterative training on the current information recommendation model according to the historical recommendation information to obtain an updated information recommendation model;
the characteristic extraction module is used for acquiring an information data set according to the information recommendation request, extracting the information content attribute and the information interaction attribute of each information data in the information data set, and respectively extracting corresponding information content characteristics and information interaction characteristics from the information content attribute and the information interaction attribute; extracting the user attribute in the user information recommendation request to obtain the user characteristics;
the information recommendation module is used for calculating a recommendation coefficient of each information data in the information data set by using the updated information recommendation model according to the information interaction characteristics, the information content characteristics and the user characteristics; and screening and sorting the information data in the information data set according to the recommendation coefficient to obtain a recommendation information data sequence, and sending the recommendation information data sequence to the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the information recommendation method of any one of claims 1-7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the information recommendation method according to any one of claims 1 to 7.
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