CN113643073A - Automatic information delivery system based on big data - Google Patents
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Abstract
The invention discloses an automatic information delivery system based on big data, which relates to the technical field of information delivery and comprises a control center, a database, an information acquisition module, an information preprocessing module, a data analysis module and an information delivery module; respectively setting interested labels and uninteresting labels for the product feature marks in each version block; the method comprises the steps of marking the total interested quantity of the product characteristic points and the total uninteresting quantity of the product characteristic points, uploading preference data of a user to a database through a data collection port, and finally, releasing information of the user according to the preference data of the user by an information releasing module.
Description
Technical Field
The invention belongs to the technical field of information delivery, and particularly relates to an automatic information delivery system based on big data.
Background
The information is generally released through a certain form of media, so that the information is publicly and widely transmitted to the public, and the information is generally delivered in the forms of advertisements, microblogs, news and the like;
the existing information delivery is often delivered randomly, such delivery mode achieves the effect of information transmission through the mode of 'walking amount', but on the one hand, such information delivery mode can make the delivery benefit of information unable to be obtained to the maximum extent, cause the waste of resources, on the other hand, the information delivery can not be carried out according to the requirement of a user, thereby making the user experience poor, how to promote the information delivery efficiency, and simultaneously, the user experience can also be considered, which is a problem that we need to solve, therefore, an automatic information delivery system based on big data is provided.
Disclosure of Invention
The invention aims to provide an automatic information delivery system based on big data.
The purpose of the invention can be realized by the following technical scheme: an automatic information delivery system based on big data comprises a control center, a database, an information acquisition module, an information preprocessing module, a data analysis module and an information delivery module;
the database is used for establishing a data label sub-library;
the information acquisition module is used for acquiring operation information of a user;
the information preprocessing module is used for preprocessing the information acquired by the information acquisition module;
the data analysis module is used for analyzing the preference of the user according to the preprocessed data obtained by the information preprocessing module;
and the information delivery module is used for delivering information to the user according to the preference data of the user.
Further, the process of establishing the data tag sub-library comprises: building a data label sub-library, wherein the data label sub-library comprises a user preference data set and a product characteristic set; a user preference data collection port is arranged in the user preference data set, a plurality of sections are arranged in the product feature set, and a plurality of product feature marks are arranged in each section.
Further, the process of acquiring the operation information of the user includes: acquiring a registration ID of a user, and uploading the ID of the user to a database through a user preference data collection port; acquiring a layout where a user is located, and marking the layout where the user is located; acquiring the staying time of a user in the layout, and marking the staying time of the user in the layout as BT; marking the information which is browsed by the user, marking the theoretical time length which is needed by the information which is browsed by the user as LT, and marking the staying time of the information which is browsed by the user as TT; and acquiring the comment time spent by the user on the information being browsed, and recording the comment time spent by the user on the information being browsed as PT.
Further, the preprocessing process of the information acquired by the information acquisition module includes: obtaining the proportion ZB of user comment time and a user browsing efficiency coefficient ZX through a formula; when ZX is larger than or equal to Z0, determining that dead time exists in the information browsing process of the user; when ZX is less than Z0, judging that the dead time does not exist in the information browsing process of the user; wherein Z0 is a preset browsing efficiency threshold of the system; and removing the dead time of the user to acquire the actual effective browsing time ST of the user.
Further, the analyzing process of the user's preference includes: marking all the blocks where the user stays, marking the stay time BT of the user in each block, and marking the block with the longest stay time as a user preference block; when TT is larger than or equal to LT and ZX is smaller than Z0, acquiring a product characteristic mark corresponding to the information browsed by the user and establishing a recommended index sequence; when TT is larger than or equal to LT and ZX is larger than or equal to Z0, obtaining the actual browsing efficiency SX of the user through a formula SX-LT/ST, and when SX is smaller than Z0, obtaining a product feature mark corresponding to the information browsed by the user and establishing a recommended index sequence; and when TT is less than LT, judging that the user is not interested in the browsed information, acquiring a product feature mark corresponding to the browsed information of the user, and establishing a shielding index sequence.
Further, the collecting process of the user preference data specifically includes the following steps: respectively setting interested labels and uninteresting labels for the product feature marks in each version block; after the information browsed by the user is analyzed by the data analysis module to generate a recommendation index sequence, sending the information and the corresponding product feature marks into interest labels, adding 1 to the interest quantity of the product feature marks, and marking the total interest quantity of the product feature points as s; after the information browsed by the user is analyzed by the data analysis module to generate a shielding index sequence, the information and the corresponding product feature marks are sent to the uninteresting labels, the uninteresting quantity of the product feature marks is added by 1, and the total uninteresting quantity of the product feature points is marked as m.
Further, the process of delivering information to the user includes: obtaining a preference coefficient PX of a user for a product feature mark through a formula PX ═ c x (s-m)/[ d x (s + m) ]; when PX is larger than or equal to A, judging that the user is interested in the product feature mark; when PX is less than A, judging that the user is not interested in the product feature mark; increasing the putting frequency of the information under the product feature mark which is interested by the user, and reducing the putting frequency of the information under the product feature mark which is not interested by the user, wherein A is a preset preference threshold value of the system.
Further characterized in that the data collection port is configured to collect user preference data.
The invention has the beneficial effects that: respectively setting interested labels and uninteresting labels for the product feature marks in each version block; after the information browsed by the user is analyzed by the data analysis module to generate a recommendation index sequence, sending the information and the corresponding product feature marks into the interested labels, adding 1 to the interested quantity of the product feature marks, and marking the total interested quantity of the product feature points; after the information browsed by the user is analyzed by the data analysis module to generate a shielding index sequence, the information and the corresponding product feature marks are sent to the uninteresting labels, the uninteresting quantity of the product feature marks is added by 1, the total uninteresting quantity of the product feature points is marked, the preference data of the user is uploaded to the database through the data collection port, and finally the information release module releases the information to the user according to the preference data of the user.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of an automated big data-based information delivery system.
Detailed Description
As shown in fig. 1, an automated information delivery system based on big data includes a control center, a database, an information collection module, an information preprocessing module, a data analysis module, and an information delivery module;
the database is used for establishing a data label sub-library, and the specific establishing process of the data label sub-library comprises the following steps:
step S1: building a data label sub-library, wherein the data label sub-library comprises a user preference data set and a product characteristic set;
step S2: a user preference data collection port is arranged in the user preference data set and used for collecting user preference data;
step S3: a plurality of sections are arranged in the product characteristic set, and a plurality of product characteristic marks are arranged in each section.
The information acquisition module is used for acquiring operation information of a user, and the specific acquisition process comprises the following steps:
step X1: acquiring a registration ID of a user, and uploading the ID of the user to a database through a user preference data collection port;
step X2: acquiring a layout where a user is located, and marking the layout where the user is located; acquiring the staying time of a user in the layout, and marking the staying time of the user in the layout as BT;
step X3: marking the information which is browsed by the user, marking the theoretical time length which is needed by the information which is browsed by the user as LT, and marking the staying time of the information which is browsed by the user as TT;
step X4: obtaining the comment time spent by the user on the information being browsed, and recording the comment time spent by the user on the information being browsed as PT;
the information preprocessing module is used for preprocessing the information acquired by the information acquisition module, and the specific process comprises the following steps:
step Y1: obtaining the proportion ZB of the comment time of the user through a formula ZB-PT/TT;
step Y2: then obtaining a user browsing efficiency coefficient ZX according to a formula ZX-LT/(TT-PT);
step Y3: when ZX is larger than or equal to Z0, determining that dead time exists in the information browsing process of the user; when ZX is less than Z0, judging that the dead time does not exist in the information browsing process of the user; wherein Z0 is a preset browsing efficiency threshold of the system;
step Y4: removing the dead time of the user, and acquiring the actual effective browsing time ST of the user, wherein ST is TT multiplied by ZB multiplied by a + (LT/TT) multiplied by ZX multiplied by b, wherein a and b are system factors, and a and b are both larger than 0;
step Y5: and sending the preprocessed data obtained in the steps Y1-Y4 to a data analysis module.
The data analysis module is used for analyzing the preference of the user according to the preprocessed data obtained by the information preprocessing module, and the specific analysis process comprises the following steps:
step F1: marking all the blocks where the user stays, marking the stay time BT of the user in each block, and marking the block with the longest stay time as a user preference block;
step F2: when TT is larger than or equal to LT and ZX is smaller than Z0, acquiring a product characteristic mark corresponding to the information browsed by the user and establishing a recommended index sequence;
step F3: when TT is larger than or equal to LT and ZX is larger than or equal to Z0, obtaining the actual browsing efficiency SX of the user through a formula SX-LT/ST, and when SX is smaller than Z0, obtaining a product feature mark corresponding to the information browsed by the user and establishing a recommended index sequence;
step F4: when TT is less than LT, judging that the user is not interested in the browsed information, acquiring a product feature mark corresponding to the browsed information of the user, and establishing a shielding index sequence;
the collecting process of the user preference data specifically comprises the following steps:
step P1: respectively setting interested labels and uninteresting labels for the product feature marks in each version block;
step P2: after the information browsed by the user is analyzed by the data analysis module to generate a recommendation index sequence, sending the information and the corresponding product feature marks into interest labels, adding 1 to the interest quantity of the product feature marks, and marking the total interest quantity of the product feature points as s;
step P3: after the information browsed by the user is analyzed by the data analysis module to generate a shielding index sequence, sending the information and the corresponding product feature marks into uninteresting labels, adding 1 to the uninteresting quantity of the product feature marks, and marking the total uninteresting quantity of the product feature points as m;
step P4: the receipts obtained in steps P1-P3 are uploaded into the database through a data collection port.
It should be further noted that, in a specific using process, when a user browses information in a version block, the information being browsed by the user is quickly analyzed by obtaining operation information of the user, and then the information browsed by the user is determined according to the analysis data, so that preference data of the user is collected, and information delivery is performed on the user according to the preference data of the user.
The information delivery module is used for delivering information to the user according to preference data of the user, and the specific process comprises the following steps:
step T1: obtaining a preference coefficient PX of a user for a product feature mark through a formula PX ═ c x (s-m)/[ d x (s + m) ];
step T2: when PX is larger than or equal to A, judging that the user is interested in the product feature mark; when PX is less than A, judging that the user is not interested in the product feature mark; wherein A is a system preset preference threshold;
step T3: the method and the device increase the putting frequency of the information under the product feature mark which is interesting to the user and reduce the putting frequency of the information under the product feature mark which is not interesting to the user.
It should be further noted that, in the specific use process, the stay time BT of the user in a certain block is marked, then the block with the longest stay time among all blocks is set as the default block, and when the user enters the system again, the user preferentially enters the default block.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The foregoing is illustrative and explanatory of the structure of the invention, and various modifications, additions or substitutions in a similar manner to the specific embodiments described may be made by those skilled in the art without departing from the structure or scope of the invention as defined in the claims. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. In the embodiments provided by 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 there may be other divisions when the actual implementation is performed; 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 method of the embodiment.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (8)
1. An automatic information delivery system based on big data is characterized by comprising a control center, a database, an information acquisition module, an information preprocessing module, a data analysis module and an information delivery module;
the database is used for establishing a data label sub-library;
the information acquisition module is used for acquiring operation information of a user;
the information preprocessing module is used for preprocessing the information acquired by the information acquisition module so as to remove dead time of a user in the information browsing process;
the data analysis module is used for analyzing the preference of the user according to the preprocessed data obtained by the information preprocessing module;
and the information delivery module is used for delivering information to the user according to the preference data of the user.
2. The big-data-based automated information delivery system according to claim 1, wherein the building process of the data tag sub-library comprises: building a data label sub-library, wherein the data label sub-library comprises a user preference data set and a product characteristic set; a user preference data collection port is arranged in the user preference data set, a plurality of sections are arranged in the product feature set, and a plurality of product feature marks are arranged in each section.
3. The big data-based automatic information delivery system according to claim 1, wherein the obtaining of the operation information of the user comprises: acquiring a registration ID of a user, and uploading the ID of the user to a database through a user preference data collection port; acquiring a layout where a user is located, and marking the layout where the user is located; acquiring the staying time of a user in the layout, and marking the staying time of the user in the layout as BT; marking the information which is browsed by the user, marking the theoretical time length which is needed by the information which is browsed by the user as LT, and marking the staying time of the information which is browsed by the user as TT; and acquiring the comment time spent by the user on the information being browsed, and recording the comment time spent by the user on the information being browsed as PT.
4. The big data-based automatic information delivery system according to claim 3, wherein the preprocessing process of the information acquired by the information acquisition module comprises: obtaining the proportion ZB of user comment time and a user browsing efficiency coefficient ZX through a formula; when ZX is larger than or equal to Z0, determining that dead time exists in the information browsing process of the user; when ZX is less than Z0, judging that the dead time does not exist in the information browsing process of the user; wherein Z0 is a preset browsing efficiency threshold of the system; and removing the dead time of the user to acquire the actual effective browsing time ST of the user.
5. The big data-based automated information delivery system according to claim 4, wherein the analysis of the user's preferences comprises: marking all the blocks where the user stays, marking the stay time BT of the user in each block, and marking the block with the longest stay time as a user preference block; when TT is larger than or equal to LT and ZX is smaller than Z0, acquiring a product characteristic mark corresponding to the information browsed by the user and establishing a recommended index sequence; when TT is larger than or equal to LT and ZX is larger than or equal to Z0, obtaining the actual browsing efficiency SX of the user through a formula SX-LT/ST, and when SX is smaller than Z0, obtaining a product feature mark corresponding to the information browsed by the user and establishing a recommended index sequence; and when TT is less than LT, judging that the user is not interested in the browsed information, acquiring a product feature mark corresponding to the browsed information of the user, and establishing a shielding index sequence.
6. The automated big-data-based information delivery system according to claim 2, wherein the collecting process of the user preference data specifically comprises the following steps: respectively setting interested labels and uninteresting labels for the product feature marks in each version block; after the information browsed by the user is analyzed by the data analysis module to generate a recommendation index sequence, sending the information and the corresponding product feature marks into interest labels, adding 1 to the interest quantity of the product feature marks, and marking the total interest quantity of the product feature points as s; after the information browsed by the user is analyzed by the data analysis module to generate a shielding index sequence, the information and the corresponding product feature marks are sent to the uninteresting labels, the uninteresting quantity of the product feature marks is added by 1, and the total uninteresting quantity of the product feature points is marked as m.
7. The big data-based automated information delivery system according to claim 6, wherein delivering information to the user comprises: obtaining a preference coefficient PX of a user for a product feature mark through a formula PX ═ c x (s-m)/[ d x (s + m) ]; when PX is larger than or equal to A, judging that the user is interested in the product feature mark; when PX is less than A, judging that the user is not interested in the product feature mark; increasing the putting frequency of the information under the product feature mark which is interested by the user, and reducing the putting frequency of the information under the product feature mark which is not interested by the user, wherein A is a preset preference threshold value of the system.
8. The automated big-data-based information delivery system according to claim 2, wherein the data collection port is configured to collect user preference data.
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Application publication date: 20211112 |