CN114896501A - Cloud computing and block chain based service recommendation method and cloud computing system - Google Patents
Cloud computing and block chain based service recommendation method and cloud computing system Download PDFInfo
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Abstract
The invention relates to the technical field of cloud computing and block chain business, in particular to a cloud computing and block chain business recommendation method and a cloud computing system, wherein the method comprises the following steps: step S1, the central control module makes a preliminary judgment on whether the user is recommended according to whether the user sends an access demand; step S2, the central control module adjusts the system push frequency to a corresponding value; step S3, the central control module adjusts the preset standard value of the browsing time, the pushing period R of the service and the pushing speed; step S4, the central control module adjusts the recommended service type quantity to a corresponding value, and when the adjustment is completed, the service recommending module recommends the corresponding service for the user; the system comprises: the system comprises an information acquisition module, a service analysis module, a storage module, a network module, a service recommendation module and a central control module. The invention realizes more accurate adjustment of the service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of the recommendation system.
Description
Technical Field
The invention relates to the technical field of cloud computing and block chain business, in particular to a cloud computing and block chain business recommendation method and a cloud computing system.
Background
With the increasingly wide application of the block chain technology, the block chain and cloud computing are also increasingly widely applied, when a user browses a page, a recommendation system based on the cloud computing and the block chain technology is more important for recommending services, and when the user browses the page, the recommendation for a certain service may have the situations that the recommendation is not accurate and the recommendation speed is too slow to miss a potential user, because the update of a service recommendation method is particularly important.
Chinese patent publication No.: CN 111652639A. The method comprises the steps of acquiring a travel end point position in user travel information; then, processing the trip end point position by utilizing a pre-divided geo-fence, and determining a target business district to which the trip end point position belongs; and uploading the user travel information to a alliance chain of the target business district, so that a merchant management system in the target business district obtains the user travel information from the alliance chain of the target business district for service recommendation, and therefore it can be seen that the service recommendation method and system have the following problems: the accurate adjustment of parameters in the service recommendation is insufficient, and the accurate recommendation of users with different characteristics is insufficient, so that the working efficiency of the recommendation system is too low.
Disclosure of Invention
Therefore, the invention provides a service recommendation method and a cloud computing system based on cloud computing and block chains, which are used for solving the problems of low working efficiency caused by insufficient accurate adjustment of parameters in service recommendation and insufficient accurate recommendation of users with different characteristics in the prior art.
Step S1, the information acquisition module acquires the original information of the user using the Internet and the position scene information of the user using the mobile service, before recommending the service to the user, the central control module makes a preliminary judgment on whether to recommend the user according to whether the user sends an access requirement; step S2, when the central control module completes the preliminary judgment on whether the user is recommended, the central control module further judges whether the user is recommended according to the browsing time of the user on the page, and when the central control module completes the further judgment on whether the user is recommended, the central control module adjusts the system pushing frequency to a corresponding value according to the difference between the actual page browsing time and the preset page browsing time; step S3, when the central control module completes the adjustment of the system push frequency, the central control module adjusts the preset browse time standard value according to the number of the pages actually clicked by the user and judges whether to adjust the push period R of the service according to the service search frequency in the unit browse time when the adjustment of the preset browse time standard value is completed, and when the central control module completes the adjustment of the service push period, the central control module adjusts the push occurrence speed according to the read completion rate of the same service characteristics on the pages in the unit time; step S4, when the central control module completes the adjustment of the push occurrence speed, the central control module preliminarily determines whether to adjust the number of the service recommendation service types according to the click ratio after the service push in the unit period, adjusts the number of the recommendation service types to a corresponding value according to the difference between the actual click ratio and the preset click ratio when the preliminary determination of whether to adjust the number of the recommendation service types is completed, and controls the recommendation service module to perform corresponding service recommendation on the user when the central control module completes the adjustment of the recommendation service types.
Further, in the step S1, the central control module performs a preliminary determination on whether to recommend a service to the user according to whether the user has an access requirement before recommending the user,
if the user sends an access requirement, the central control module judges to recommend the service to the user;
if the user does not send an access demand, the central control module judges whether to calculate the browsing duration of the user and further judges whether to recommend services to the user according to the parameters.
Further, when the central control module completes the preliminary determination of whether to recommend the service to the user, whether to recommend the service to the user is further determined according to the actual browsing duration T of the page by the user, the central control module is provided with a preset first page browsing duration T1 and a preset second page browsing duration T2, wherein T1 is less than T2,
if T is less than or equal to T1, the central control module judges that the actual browsing duration of the page by the user is lower than the allowable range and does not recommend the user;
if T is more than T1 and less than or equal to T2, the central control module judges that the actual browsing time of the page by the user is within an allowable range, calculates the difference value delta T between the actual browsing time of the page by the user and the preset browsing time of the page, adjusts the pushing frequency of the system to a corresponding value according to the delta T, and sets delta T = T-T1;
and if T is greater than T2, the central control module judges that the actual browsing duration of the page by the user is within the allowable range and controls the system to directly recommend the user.
Further, the central control module adjusts the system push frequency to a corresponding value according to a difference between an actual page browsing time length and a preset page browsing time length when the central control module completes further determination of whether to recommend the user, the central control module is provided with a first page browsing time length difference Δ T1, a preset second page browsing time length difference Δ T2, a preset first system push frequency adjustment coefficient α 1, a preset second system push frequency adjustment coefficient α 2 and a preset system push frequency F0, wherein Δ T1 is less than Δ T2, α 1 is less than α 1 and α 2 is less than α 2,
if the delta T is less than or equal to the delta T1, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is less than an allowable range, adjusts the pushing frequency of the system by using alpha 1, records the adjusted system pushing frequency as F1, and sets F1= alpha 1 xF 0;
if delta T1 is smaller than delta T and smaller than or equal to delta T2, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is lower than an allowable range, adjusts the system pushing frequency by using alpha 2, records the adjusted system pushing frequency as F2, and sets F2= alpha 2 xF 2;
if delta T is larger than delta T2, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is in an allowable range and does not adjust the system pushing frequency.
Further, when the central control module completes adjustment of the system push frequency, the preset browsing duration standard value is adjusted to a corresponding value according to a comparison result between the number of pages actually clicked by the user and the number of pages clicked by a preset single user, the central control module is provided with a preset first single user page click number A1, a preset second single user page click number A2, a preset first preset browsing duration standard value adjustment coefficient S1, a preset second preset browsing duration standard value adjustment coefficient S2 and a preset browsing duration T0, wherein A1 is greater than A2, 1 is greater than S1 is greater than S2,
if A is not more than A1, the central control module judges that the number of the pages actually clicked by the user is within an allowable range and does not adjust a preset browsing time standard value;
if A is greater than A1 and less than or equal to A2, the central control module judges that the number of pages actually clicked by the user is not within an allowable range, and adjusts the preset browsing duration standard value by using S1, the adjusted preset browsing duration standard value is marked as T1, and T1= S1 × T0 is set;
if A is greater than A2, the central control module judges that the number of the pages actually clicked by the user is not within an allowable range, and adjusts the preset browsing duration standard value by using S2, the adjusted preset browsing duration standard value is recorded as T2, and T2= S2 × T0 is set.
Further, the central control module adjusts the service push period R according to the service search frequency in unit browsing time when completing the adjustment of the preset browsing time standard value, and the central control module is provided with a preset first service search frequency P1, a preset second service search frequency P2, a preset first service push period adjustment coefficient g1, a preset second service push period adjustment coefficient g2 and a preset service push period R0, wherein P1 is greater than P2, 0 is greater than g1 is greater than g2 is less than 1,
if P is less than or equal to P1, the central control module judges that the actual service searching frequency is within the allowable range and does not adjust the pushing period of the service;
if P is greater than P1 and less than or equal to P2, the central control module determines that the actual service search frequency exceeds the allowable range and adjusts the service push period by using g1, the adjusted service push period is marked as R1, and R1= g1 × R0 is set;
if P > P2, the central control module determines that the actual service search volume exceeds the allowable range and adjusts the service push period by using g2, the adjusted service push period is recorded as R2, and R2= g2 × R0 is set.
Further, the central control module adjusts the push occurrence speed according to the reading completion rate of the same service feature on the page in unit time when the central control module completes the adjustment of the service push cycle, and the central control module is provided with a preset first reading completion rate D1, a preset second reading completion rate D2, a preset first push occurrence speed adjustment coefficient γ 1, a preset second push occurrence speed adjustment coefficient γ 2 and a preset push occurrence speed V0, wherein D1 is greater than D2, 1 is greater than γ 1 and is less than γ 2,
if D is not more than D1, the central control module judges that the reading completion rate of the same service features on the page in unit time is within an allowable range and does not adjust the pushing speed;
if D1 is larger than D and is not larger than D2, the central control module judges that the reading completion rate of the same service features on the page in unit time exceeds an allowable range and adjusts the pushing occurrence speed by using gamma 1, the adjusted pushing occurrence speed is marked as V1, and V1= gamma 1 × V0 is set;
if D is larger than D2, the central control module judges that the reading completion rate of the same business features on the page in unit time exceeds an allowable range and adjusts the pushing occurrence speed by using gamma 2, the adjusted pushing occurrence speed is recorded as V2, and V2= gamma 2 xV 0 is set.
Further, the central control module performs preliminary determination on whether the number of the service types recommended by the service is adjusted according to the click ratio after the service is pushed in a unit period when the adjustment of the push occurrence speed is completed, the central control module is provided with a preset first click ratio E1 and a preset second click ratio E2, wherein E1 is less than E2,
if E is less than or equal to E1, the central control module judges that the actual click ratio after the service push in the unit period is lower than the allowable range and sends the user access information to the storage module for later use;
if E is more than E1 and less than or equal to E2, the central control module judges that the actual click ratio after the service push in the unit period is in an allowable range, calculates the difference value delta E between the actual click ratio after the service push in the unit period and the preset click ratio, adjusts the number of the service types recommended by the service to a corresponding value according to the delta E, and sets delta E = E-E1;
and if E is larger than E2, the central control module judges that the actual click ratio of the pushed service in the unit period is within the allowable range, directly pushes the pushed service in the unit period, and sends the user access information to the storage module after pushing is finished.
Further, the central control module adjusts the number of the service types recommended by the service to a corresponding value according to a difference Δ E between an actual click ratio after the service push in a unit period and a preset click ratio when the primary determination of whether to adjust the number of the service types recommended by the service is completed, the central control module is provided with a preset first click ratio difference Δ E1, a preset second click ratio difference Δ E2, a preset first service recommended service type number adjustment coefficient K1, a preset second service recommended service type number adjustment coefficient K2 and a preset service recommended service type number M0, wherein Δ E1 is less than Δ E2, and 1 < K1 < K2,
if the delta E is less than or equal to the delta E1, the central control module judges that the difference value between the actual click ratio and the preset click ratio after the service is pushed in the unit period is in an allowable range and does not adjust the number of the service recommendation service types;
if delta E1 is less than delta E and less than or equal to delta E2, the central control module judges that the difference value between the actual click ratio and the preset click ratio after the service push in the unit period is not in an allowable range, and adjusts the number of the service recommendation service types by using K1, wherein the adjusted number of the service recommendation service types is recorded as M1, and M1= K1 × M0 is set;
if Δ E > - Δ E2, the central control module determines that the difference between the actual click ratio after the service push in the unit period and the preset click ratio is not within the allowable range, adjusts the number of the service recommendation service types by using K2, records the adjusted number of the service recommendation service types as M2, and sets M2= K2 × M0.
On the other hand, the invention also provides a cloud computing system based on cloud computing and block chain service recommendation, which is characterized by comprising:
the information acquisition module is used for extracting user information of the user and sending the user information to the service analysis module;
the service analysis module is connected with the information acquisition module and is used for analyzing, calculating and processing the received user information;
the storage module is connected with the service analysis module and is used for storing the processed information;
the network module, the business analysis module and the storage module are used for keeping communication support for the processing process;
the service recommendation module is connected with the network module and is used for recommending the service to be recommended;
and the central control module is connected with the modules and used for analyzing and calculating the received information transmitted by the modules and adjusting the operation parameters and the system preset parameters corresponding to the modules to corresponding values by using corresponding adjusting coefficients.
Compared with the prior art, the method has the advantages that by setting the browsing time length of the preset page, the browsing time length difference value of the preset page, the number of pages clicked by the preset single user, the business search frequency in the unit browsing time length, the reading completion rate of the same business characteristics on the page in the unit time, the click ratio after the business push in the unit period and the preset click ratio difference value, the preset browsing time standard value, the pushing period R of the service, the number of the service recommendation service types and the occurrence speed of pushing can be adjusted to corresponding values according to the comparison result of the actual detection value and the preset value, so that the real-time preset parameter adjusting capability of the service recommendation system and the accurate recommended parameter adjusting capability of the recommendation system are improved, and more accurate adjustment of the service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of the recommendation system are realized.
Furthermore, the method of the invention preliminarily judges whether the user is recommended according to whether the user sends the access requirement or not, improves the accuracy of user recommendation, and further realizes more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the work efficiency of a recommendation system.
Furthermore, the method of the invention can further judge whether the user is recommended according to the browsing duration of the user to the page by setting the preset first page browsing duration and the preset second page browsing duration, thereby improving the accurate recommendation capability of the user, further realizing more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommendation system.
Furthermore, the method of the invention can adjust the system push frequency to a corresponding value according to the difference between the actual page browsing duration and the preset page browsing duration by setting the preset first page browsing duration difference, the preset second page browsing duration difference, the preset first system push frequency adjustment coefficient, the preset second system push frequency adjustment coefficient and the preset system push frequency, thereby improving the adjustment capability of the push frequency for service recommendation, and further realizing more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of the recommendation system.
Furthermore, the method of the invention can adjust whether the preset browsing time standard value is adjusted according to the comparison result of the number of the pages actually clicked by the user and the number of the pages clicked by the preset single user by setting the preset first single-user page click number, the preset second single-user page click number, the preset first preset browsing time standard value adjustment coefficient, the preset second preset browsing time standard value adjustment coefficient and the preset browsing time standard value, thereby realizing the timely adjustment of the preset parameters of the system, further realizing the more accurate adjustment of the service recommendation parameters, the accurate recommendation of users with different characteristics and the improvement of the working efficiency of the recommendation system.
Furthermore, the method of the present invention can adjust whether the service push period R is to be performed according to the service search frequency within a unit browsing time by setting the preset first service search frequency, the preset second service search frequency, the preset first service push period adjustment coefficient, the preset second service push period, and the preset service push period, thereby improving the real-time adjustment capability of the service push period, and further realizing more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics, and improvement of the work efficiency of the recommendation system.
Furthermore, the method of the invention can adjust the occurrence speed of the push according to the reading completion rate of the same service characteristic on the page in unit time by setting the preset first reading completion rate, the preset second reading completion rate, the preset first push occurrence speed adjustment coefficient, the preset second push occurrence speed adjustment coefficient and the preset push occurrence speed, thereby improving the accurate adjustment of the service recommendation speed, further realizing the more accurate adjustment of the service recommendation parameter, the accurate recommendation of users with different characteristics and the improvement of the working efficiency of the recommendation system.
Furthermore, the method of the invention can preliminarily judge whether the number of the service recommendation service types is adjusted according to the click ratio after the service is pushed in the unit period by setting the preset first click ratio and the preset second click ratio, thereby realizing the accurate judgment of the number of the service recommendation types, further realizing the more accurate adjustment of the service recommendation parameters, the accurate recommendation of users with different characteristics and the improvement of the working efficiency of the recommendation system.
Furthermore, the method of the present invention, by setting a preset first click ratio difference, a preset second click ratio difference, a preset first service recommendation service type number adjustment coefficient, a preset second service recommendation service type number adjustment coefficient, and a preset service recommendation service type number, can adjust the service recommendation service type number to a corresponding value according to a comparison result between a difference between an actual click ratio and a preset click ratio difference, thereby improving the accurate adjustment of the service recommendation type number and the adjustment capability of the service recommendation range, and further realizing more accurate adjustment of the service recommendation parameters, accurate recommendation of users with different characteristics, and improvement of the work efficiency of the recommendation system.
Furthermore, the system provided by the invention can provide strong cloud computing data support for the service recommendation process, perform corresponding data processing on the page browsing and clicking parameters of the user and timely adjust the preset parameters and the operation parameters related to the service recommendation by using the corresponding adjusting parameters through setting the information obtaining module, the service analysis module, the storage module, the network module, the service recommendation module and the central control module, so that the processing work efficiency of the service recommendation is improved, and more accurate adjustment of the service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the work efficiency of the recommendation system are further realized.
Drawings
Fig. 1 is a schematic flow chart of a cloud computing and block chain based service recommendation method according to the present invention;
fig. 2 is a schematic structural diagram of a cloud computing system based on a cloud computing and block chain service recommendation method according to the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a recommendation method based on cloud computing and block chain service includes:
step S1, the information acquisition module acquires the original information of the user using the Internet and the position scene information of the user using the mobile service, and before service recommendation is performed on the user, the central control module performs preliminary judgment on whether the user is recommended or not according to whether the user sends an access requirement or not;
step S2, when the central control module completes the preliminary judgment on whether the user is recommended, the central control module further judges whether the user is recommended according to the browsing time of the user on the page, and when the central control module completes the further judgment on whether the user is recommended, the central control module adjusts the system pushing frequency to a corresponding value according to the difference between the actual page browsing time and the preset page browsing time;
step S3, when the central control module completes the adjustment of the system push frequency, the central control module adjusts the preset browse time standard value according to the number of the pages actually clicked by the user and judges whether to adjust the push period R of the service according to the service search frequency in the unit browse time when the adjustment of the preset browse time standard value is completed, and when the central control module completes the adjustment of the service push period, the central control module adjusts the push occurrence speed according to the read completion rate of the same service characteristics on the pages in the unit time;
step S4, when the central control module completes the adjustment of the push occurrence speed, the central control module preliminarily determines whether to adjust the number of the service recommendation service types according to the click ratio after the service push in the unit period, adjusts the number of the recommendation service types to a corresponding value according to the difference between the actual click ratio and the preset click ratio when the preliminary determination of whether to adjust the number of the recommendation service types is completed, and controls the recommendation service module to perform corresponding service recommendation on the user when the central control module completes the adjustment of the recommendation service types.
The invention sets browsing time length of the preset page, the difference value of the browsing time lengths of the preset page, the number of pages clicked by the preset single user, service searching frequency in unit browsing time length, reading completion rate of the same service characteristics on the page in unit time, click ratio after service push in unit period and the difference value of the preset click ratio, the preset browsing time standard value, the pushing period R of the service, the number of the service recommendation service types and the occurrence speed of pushing can be adjusted to corresponding values according to the comparison result of the actual detection value and the preset value, so that the real-time preset parameter adjusting capability of the service recommendation system and the accurate recommended parameter adjusting capability of the recommendation system are improved, and more accurate adjustment of the service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of the recommendation system are realized.
2. The cloud computing and block chain based service recommendation method and system according to claim 1, wherein in step S1, the central control module performs a preliminary determination on whether to make a service recommendation to the user according to whether the user requests access before making a recommendation to the user,
if the user sends an access requirement, the central control module judges to recommend the service to the user;
if the user does not send an access demand, the central control module judges whether to calculate the browsing duration of the user and further judges whether to recommend services to the user according to the parameters.
The method of the invention preliminarily judges whether the user is recommended or not according to whether the user sends the access requirement or not, improves the accuracy of user recommendation, and further realizes more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommendation system.
3. The cloud computing and block chain based service recommendation method according to claim 2, wherein the central control module further determines whether to recommend the service to the user according to an actual browsing duration T of the user to the page when completing the preliminary determination of whether to recommend the service to the user, and the central control module is provided with a preset first page browsing duration T1 and a preset second page browsing duration T2, wherein T1 < T2,
if T is less than or equal to T1, the central control module judges that the actual browsing duration of the page by the user is lower than the allowable range and does not recommend the user;
if T is more than T1 and less than or equal to T2, the central control module judges that the actual browsing time of the page by the user is within an allowable range, calculates the difference value delta T between the actual browsing time of the page by the user and the preset browsing time of the page, adjusts the pushing frequency of the system to a corresponding value according to the delta T, and sets delta T = T-T1;
and if T is greater than T2, the central control module judges that the actual browsing duration of the page by the user is within the allowable range and controls the system to directly recommend the user.
According to the method, the preset first page browsing time length and the preset second page browsing time length are set, whether the user is recommended or not can be further judged according to the browsing time length of the page of the user, the accurate recommending capability of the user is improved, and more accurate adjustment of service recommending parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommending system are further achieved.
4. The cloud computing and block chain service recommendation method according to claim 3, wherein the central control module adjusts the system push frequency to a corresponding value according to a difference between an actual page browsing time length and a preset page browsing time length when further determination is made as to whether to recommend the user, and the central control module is provided with a first page browsing time length difference Δ T1, a preset second page browsing time length difference Δ T2, a preset first system push frequency adjustment coefficient α 1, a preset second system push frequency adjustment coefficient α 2, and a preset system push frequency F0, wherein Δ T1 is less than Δ T2, 1 < α 2,
if the delta T is less than or equal to the delta T1, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is less than an allowable range, adjusts the pushing frequency of the system by using alpha 1, records the adjusted system pushing frequency as F1, and sets F1= alpha 1 xF 0;
if delta T1 is smaller than delta T and smaller than or equal to delta T2, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is lower than an allowable range, adjusts the system pushing frequency by using alpha 2, records the adjusted system pushing frequency as F2, and sets F2= alpha 2 xF 2;
if delta T is larger than delta T2, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is in an allowable range and does not adjust the system pushing frequency.
According to the method, the preset first page browsing time difference, the preset second page browsing time difference, the preset first system push frequency adjusting coefficient, the preset second system push frequency adjusting coefficient and the preset system push frequency are set, the system push frequency can be adjusted to the corresponding value according to the difference between the actual page browsing time and the preset page browsing time, the adjusting capacity of the push frequency for service recommendation is improved, and more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommendation system are further achieved.
5. The cloud computing and block chain service recommendation method according to claim 4, wherein the central control module adjusts the preset standard browsing duration value to a corresponding value according to a comparison result between the number of pages actually clicked by the user and the number of pages clicked by a preset single user when the adjustment of the system push frequency is completed, and the central control module is provided with a preset first single user page click number A1, a preset second single user page click number A2, a preset first preset browsing duration value standard value adjustment coefficient S1, a preset second preset browsing duration value standard value adjustment coefficient S2, and a preset browsing duration value T0, where A1 is greater than A2, and 1 is greater than S1 is less than S2,
if A is not more than A1, the central control module judges that the number of the pages actually clicked by the user is within an allowable range and does not adjust a preset browsing time standard value;
if A is greater than A1 and less than or equal to A2, the central control module judges that the number of pages actually clicked by the user is not within an allowable range, and adjusts the preset browsing duration standard value by using S1, the adjusted preset browsing duration standard value is marked as T1, and T1= S1 × T0 is set;
if A is greater than A2, the central control module judges that the number of the pages actually clicked by the user is not within an allowable range, and adjusts the preset browsing duration standard value by using S2, the adjusted preset browsing duration standard value is recorded as T2, and T2= S2 × T0 is set.
According to the method, the preset number of clicks of the first single-user page, the preset number of clicks of the second single-user page, the preset first preset browsing time standard value adjusting coefficient, the preset second preset browsing time standard value adjusting coefficient and the preset browsing time standard value are set, whether the preset browsing time standard value is adjusted or not can be determined according to the comparison result of the number of the pages actually clicked by the user and the number of the pages clicked by the preset single-user, the preset parameters of the system can be adjusted in time, more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of the recommendation system are further achieved.
6. The cloud computing and block chain service recommendation method according to claim 5, wherein the central control module adjusts the service push period R according to the service search frequency in unit browsing time when completing the adjustment of the preset standard value of browsing time, and the central control module is provided with a preset first service search frequency P1, a preset second service search frequency P2, a preset first service push period adjustment coefficient g1, a preset second service push period adjustment coefficient g2, and a preset service push period R0, wherein P1 < P2, 0 < g1 < g2 < 1,
if P is less than or equal to P1, the central control module judges that the actual service searching frequency is within the allowable range and does not adjust the pushing period of the service;
if P is greater than P1 and less than or equal to P2, the central control module determines that the actual service search frequency exceeds the allowable range and adjusts the service push period by using g1, the adjusted service push period is marked as R1, and R1= g1 × R0 is set;
if P > P2, the central control module determines that the actual service search volume exceeds the allowable range and adjusts the service push period by using g2, the adjusted service push period is recorded as R2, and R2= g2 × R0 is set.
The method of the invention can adjust the service push period R according to the service search frequency in unit browsing time by setting the preset first service search frequency, the preset second service search frequency, the preset first service push period adjustment coefficient, the preset second service push period and the preset service push period, thereby improving the real-time adjustment capability of the service push period, further realizing more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommendation system.
7. The cloud computing and block chain based service recommendation method according to claim 6, wherein the central control module adjusts the occurrence speed of pushing according to the reading completion rate of the same service feature on the page in unit time when completing the adjustment of the service pushing period, and the central control module is provided with a preset first reading completion rate D1, a preset second reading completion rate D2, a preset first pushing occurrence speed adjustment coefficient γ 1, a preset second pushing occurrence speed adjustment coefficient γ 2, and a preset pushing occurrence speed V0, wherein D1 < D2, 1 < γ 2,
if D is not more than D1, the central control module judges that the reading completion rate of the same service features on the page in unit time is within an allowable range and does not adjust the pushing speed;
if D1 is larger than D and is not larger than D2, the central control module judges that the reading completion rate of the same service features on the page in unit time exceeds an allowable range and adjusts the pushing occurrence speed by using gamma 1, the adjusted pushing occurrence speed is marked as V1, and V1= gamma 1 × V0 is set;
if D is larger than D2, the central control module judges that the reading completion rate of the same business features on the page in unit time exceeds an allowable range and adjusts the pushing occurrence speed by using gamma 2, the adjusted pushing occurrence speed is recorded as V2, and V2= gamma 2 xV 0 is set.
According to the method, the occurrence speed of pushing can be adjusted according to the reading completion rate of the same service characteristic on the page in unit time by setting the preset first reading completion rate, the preset second reading completion rate, the preset first pushing occurrence speed adjusting coefficient, the preset second pushing occurrence speed adjusting coefficient and the preset pushing occurrence speed, so that the accurate adjustment of the service recommendation speed is improved, and further the more accurate adjustment of the service recommendation parameter, the accurate recommendation of users with different characteristics and the improvement of the working efficiency of a recommendation system are realized.
8. The cloud computing and block chain based service recommendation method according to claim 7, wherein the central control module initially determines whether to adjust the number of service types recommended by the service according to the click ratio after the service is pushed in a unit period when the adjustment of the push occurrence speed is completed, and the central control module is provided with a preset first click ratio E1 and a preset second click ratio E2, where E1 < E2,
if E is less than or equal to E1, the central control module judges that the actual click ratio after the service push in the unit period is lower than the allowable range and sends the user access information to the storage module for later use;
if E is more than E1 and less than or equal to E2, the central control module judges that the actual click ratio after the service push in the unit period is in an allowable range, calculates the difference value delta E between the actual click ratio after the service push in the unit period and the preset click ratio, adjusts the number of the service types recommended by the service to a corresponding value according to the delta E, and sets delta E = E-E1;
and if E is larger than E2, the central control module judges that the actual click ratio of the pushed service in the unit period is within the allowable range, directly pushes the pushed service in the unit period, and sends the user access information to the storage module after pushing is finished.
According to the method, whether the number of the service recommendation service types is adjusted or not can be preliminarily judged according to the click ratio after the service is pushed in the unit period by setting the preset first click ratio and the preset second click ratio, so that the accurate judgment of the number of the service recommendation types is realized, and more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommendation system are further realized.
9. The method of claim 8, wherein the central control module adjusts the number of the recommended service types to corresponding values according to a difference Δ E between an actual click ratio and a preset click ratio after the service push in a unit cycle when performing the preliminary determination of whether to adjust the number of the recommended service types, and the central control module is provided with a preset first click ratio difference Δ E1, a preset second click ratio difference Δ E2, a preset first recommended service type number adjustment coefficient K1, a preset second recommended service type number adjustment coefficient K2, and a preset recommended service type number M0, where Δ E1 is less than Δ E2, and 1 < K1 < K2,
if the delta E is less than or equal to the delta E1, the central control module judges that the difference value between the actual click ratio and the preset click ratio after the service is pushed in the unit period is in an allowable range and does not adjust the number of the service recommendation service types;
if delta E1 is less than delta E and less than or equal to delta E2, the central control module judges that the difference value between the actual click ratio and the preset click ratio after the service push in the unit period is not in an allowable range, and adjusts the number of the service recommendation service types by using K1, wherein the adjusted number of the service recommendation service types is recorded as M1, and M1= K1 × M0 is set;
if Δ E > - Δ E2, the central control module determines that the difference between the actual click ratio after the service push in the unit period and the preset click ratio is not within the allowable range, adjusts the number of the service recommendation service types by using K2, records the adjusted number of the service recommendation service types as M2, and sets M2= K2 × M0.
According to the method, the preset first click ratio difference value, the preset second click ratio difference value, the preset first business recommendation business type quantity adjusting coefficient, the preset second business recommendation business type quantity adjusting coefficient and the preset business recommendation business type quantity are set, the business recommendation business type quantity can be adjusted to the corresponding value according to the comparison result of the difference value of the actual click ratio and the preset click ratio difference value, the accurate adjustment on the business recommendation type quantity and the adjustment capability on the business recommendation range are improved, and the more accurate adjustment on the business recommendation parameters, the accurate recommendation on users with different characteristics and the improvement on the working efficiency of a recommendation system are further realized.
10. A cloud computing system based on cloud computing and blockchain business recommendation using the method of any of claims 1 to 9, comprising:
the information acquisition module is used for extracting user information of the user and sending the user information to the service analysis module;
the service analysis module is connected with the information acquisition module and is used for analyzing, calculating and processing the received user information;
the storage module is connected with the service analysis module and is used for storing the processed information;
the network module, the business analysis module and the storage module are used for keeping communication support for the processing process;
the service recommendation module is connected with the network module and is used for recommending the service to be recommended;
and the central control module is connected with the modules and used for analyzing and calculating the received information transmitted by the modules and adjusting the operation parameters and the system preset parameters corresponding to the modules to corresponding values by using corresponding adjusting coefficients.
According to the system, the information acquisition module, the service analysis module, the storage module, the network module, the service recommendation module and the central control module are arranged, so that strong cloud computing data support can be provided for a service recommendation process, corresponding data processing can be carried out on page browsing and clicking parameters of a user, and preset parameters and operation parameters related to service recommendation can be timely adjusted by using corresponding adjustment parameters, the processing working efficiency of service recommendation is improved, more accurate adjustment of service recommendation parameters, accurate recommendation of users with different characteristics and improvement of the working efficiency of a recommendation system are further realized.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The recommendation method based on cloud computing and block chain service is characterized by comprising the following steps:
step S1, the information acquisition module acquires the original information of the user using the Internet and the position scene information of the user using the mobile service, before the service recommendation is carried out to the user, the central control module carries out preliminary judgment to whether the user is recommended or not according to whether the user sends out the access requirement or not;
step S2, when the central control module completes the preliminary judgment on whether the user is recommended, the central control module further judges whether the user is recommended according to the browsing time of the user on the page, and when the central control module completes the further judgment on whether the user is recommended, the central control module adjusts the system pushing frequency to a corresponding value according to the difference between the actual page browsing time and the preset page browsing time;
step S3, when the central control module completes the adjustment of the system push frequency, the central control module adjusts the preset browse time standard value according to the number of the pages actually clicked by the user and judges whether to adjust the push period R of the service according to the service search frequency in the unit browse time when the adjustment of the preset browse time standard value is completed, and when the central control module completes the adjustment of the service push period, the central control module adjusts the push occurrence speed according to the read completion rate of the same service characteristics on the pages in the unit time;
step S4, when the central control module completes the adjustment of the push occurrence speed, the central control module preliminarily determines whether to adjust the number of the service recommendation service types according to the click ratio after the service push in the unit period, adjusts the number of the recommendation service types to a corresponding value according to the difference between the actual click ratio and the preset click ratio when the preliminary determination of whether to adjust the number of the recommendation service types is completed, and controls the recommendation service module to perform corresponding service recommendation on the user when the central control module completes the adjustment of the recommendation service types.
2. The cloud computing and block chain based service recommendation method and the cloud and cloud computing system according to claim 1, wherein in the step S1, the central control module performs a preliminary determination on whether to recommend a service to the user according to whether the user has an access requirement before recommending the user,
if the user sends an access requirement, the central control module judges to recommend the service to the user;
if the user does not send an access demand, the central control module judges whether to calculate the browsing duration of the user and further judges whether to recommend services to the user according to the parameters.
3. The cloud computing and block chain based service recommendation method according to claim 2, wherein the central control module further determines whether to recommend the service to the user according to an actual browsing duration T of the user to the page when completing the preliminary determination of whether to recommend the service to the user, and the central control module is provided with a preset first page browsing duration T1 and a preset second page browsing duration T2, wherein T1 < T2,
if T is less than or equal to T1, the central control module judges that the actual browsing duration of the page by the user is lower than the allowable range and does not recommend the user;
if T is more than T1 and less than or equal to T2, the central control module judges that the actual browsing time of the page by the user is within an allowable range, calculates the difference value delta T between the actual browsing time of the page by the user and the preset browsing time of the page, adjusts the pushing frequency of the system to a corresponding value according to the delta T, and sets delta T = T-T1;
and if T is greater than T2, the central control module judges that the actual browsing duration of the page by the user is within the allowable range and controls the system to directly recommend the user.
4. The cloud computing and block chain service recommendation method according to claim 3, wherein the central control module adjusts the system push frequency to a corresponding value according to a difference between an actual page browsing time length and a preset page browsing time length when further determination is made as to whether to recommend the user, and the central control module is provided with a first page browsing time length difference Δ T1, a preset second page browsing time length difference Δ T2, a preset first system push frequency adjustment coefficient α 1, a preset second system push frequency adjustment coefficient α 2, and a preset system push frequency F0, wherein Δ T1 is less than Δ T2, 1 < α 2,
if the delta T is less than or equal to the delta T1, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is less than an allowable range, adjusts the pushing frequency of the system by using alpha 1, records the adjusted system pushing frequency as F1, and sets F1= alpha 1 xF 0;
if delta T1 is smaller than delta T and smaller than or equal to delta T2, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is lower than an allowable range, adjusts the system pushing frequency by using alpha 2, records the adjusted system pushing frequency as F2, and sets F2= alpha 2 xF 2;
if delta T is larger than delta T2, the central control module judges that the difference value between the actual page browsing time length and the preset page browsing time length is in an allowable range and does not adjust the system pushing frequency.
5. The cloud computing and block chain service recommendation method according to claim 4, wherein the central control module adjusts the preset standard browsing duration value to a corresponding value according to a comparison result between the number of pages actually clicked by the user and the number of pages clicked by a preset single user when the adjustment of the system push frequency is completed, and the central control module is provided with a preset first single user page click number A1, a preset second single user page click number A2, a preset first preset browsing duration value standard value adjustment coefficient S1, a preset second preset browsing duration value standard value adjustment coefficient S2, and a preset browsing duration value T0, where A1 is greater than A2, and 1 is greater than S1 is less than S2,
if A is not more than A1, the central control module judges that the number of the pages actually clicked by the user is within an allowable range and does not adjust a preset browsing time standard value;
if A is greater than A1 and less than or equal to A2, the central control module judges that the number of pages actually clicked by the user is not within an allowable range, and adjusts the preset browsing duration standard value by using S1, the adjusted preset browsing duration standard value is marked as T1, and T1= S1 × T0 is set;
if A is greater than A2, the central control module judges that the number of the pages actually clicked by the user is not within an allowable range, and adjusts the preset browsing duration standard value by using S2, the adjusted preset browsing duration standard value is recorded as T2, and T2= S2 × T0 is set.
6. The cloud computing and block chain service recommendation method according to claim 5, wherein the central control module adjusts the service push period R according to the service search frequency in unit browsing time when completing the adjustment of the preset standard value of browsing time, and the central control module is provided with a preset first service search frequency P1, a preset second service search frequency P2, a preset first service push period adjustment coefficient g1, a preset second service push period adjustment coefficient g2, and a preset service push period R0, wherein P1 < P2, 0 < g1 < g2 < 1,
if P is less than or equal to P1, the central control module judges that the actual service searching frequency is within the allowable range and does not adjust the pushing period of the service;
if P is greater than P1 and less than or equal to P2, the central control module determines that the actual service search frequency exceeds the allowable range and adjusts the service push period by using g1, the adjusted service push period is marked as R1, and R1= g1 × R0 is set;
if P > P2, the central control module determines that the actual service search volume exceeds the allowable range and adjusts the service push period by using g2, the adjusted service push period is recorded as R2, and R2= g2 × R0 is set.
7. The cloud computing and block chain based service recommendation method according to claim 6, wherein the central control module adjusts the occurrence speed of pushing according to the reading completion rate of the same service feature on the page in unit time when completing the adjustment of the service pushing period, and the central control module is provided with a preset first reading completion rate D1, a preset second reading completion rate D2, a preset first pushing occurrence speed adjustment coefficient γ 1, a preset second pushing occurrence speed adjustment coefficient γ 2, and a preset pushing occurrence speed V0, wherein D1 < D2, 1 < γ 2,
if D is not more than D1, the central control module judges that the reading completion rate of the same service features on the page in unit time is within an allowable range and does not adjust the pushing speed;
if D1 is larger than D and is not larger than D2, the central control module judges that the reading completion rate of the same service features on the page in unit time exceeds an allowable range and adjusts the pushing occurrence speed by using gamma 1, the adjusted pushing occurrence speed is marked as V1, and V1= gamma 1 × V0 is set;
if D is larger than D2, the central control module judges that the reading completion rate of the same business features on the page in unit time exceeds an allowable range and adjusts the pushing occurrence speed by using gamma 2, the adjusted pushing occurrence speed is recorded as V2, and V2= gamma 2 xV 0 is set.
8. The cloud computing and block chain based service recommendation method according to claim 7, wherein the central control module initially determines whether to adjust the number of service types recommended by the service according to the click ratio after the service is pushed in a unit period when the adjustment of the push occurrence speed is completed, and the central control module is provided with a preset first click ratio E1 and a preset second click ratio E2, where E1 < E2,
if E is less than or equal to E1, the central control module judges that the actual click ratio after the service push in the unit period is lower than the allowable range and sends the user access information to the storage module for later use;
if E is more than E1 and less than or equal to E2, the central control module judges that the actual click ratio after the service push in the unit period is in an allowable range, calculates the difference value delta E between the actual click ratio after the service push in the unit period and the preset click ratio, adjusts the number of the service types recommended by the service to a corresponding value according to the delta E, and sets delta E = E-E1;
and if E is larger than E2, the central control module judges that the actual click ratio of the pushed service in the unit period is within the allowable range, directly pushes the pushed service in the unit period, and sends the user access information to the storage module after pushing is finished.
9. The method of claim 8, wherein the central control module adjusts the number of the recommended service types to corresponding values according to a difference Δ E between an actual click ratio and a preset click ratio after the service push in a unit cycle when performing the preliminary determination of whether to adjust the number of the recommended service types, and the central control module is provided with a preset first click ratio difference Δ E1, a preset second click ratio difference Δ E2, a preset first recommended service type number adjustment coefficient K1, a preset second recommended service type number adjustment coefficient K2, and a preset recommended service type number M0, where Δ E1 is less than Δ E2, and 1 < K1 < K2,
if the delta E is less than or equal to the delta E1, the central control module judges that the difference value between the actual click ratio and the preset click ratio after the service is pushed in the unit period is in an allowable range and does not adjust the number of the service recommendation service types;
if delta E1 is less than delta E and is equal to or less than delta E2, the central control module judges that the difference value between the actual click ratio and the preset click ratio after the service push in the unit period is not within an allowable range, and adjusts the number of the service recommendation service types by using K1, the adjusted number of the service recommendation service types is recorded as M1, and M1= K1 xM 0 is set;
if Δ E > - Δ E2, the central control module determines that the difference between the actual click ratio after the service push in the unit period and the preset click ratio is not within the allowable range, adjusts the number of the service recommendation service types by using K2, records the adjusted number of the service recommendation service types as M2, and sets M2= K2 × M0.
10. A cloud computing system based on cloud computing and blockchain business recommendation using the method of any of claims 1 to 9, comprising:
the information acquisition module is used for extracting user information of the user and sending the user information to the service analysis module;
the service analysis module is connected with the information acquisition module and is used for analyzing, calculating and processing the received user information;
the storage module is connected with the service analysis module and is used for storing the processed information;
the network module, the business analysis module and the storage module are used for keeping communication support for the processing process;
the service recommendation module is connected with the network module and is used for recommending the service to be recommended;
and the central control module is connected with the modules and used for analyzing and calculating the received information transmitted by the modules and adjusting the operation parameters and the system preset parameters corresponding to the modules to corresponding values by using corresponding adjusting coefficients.
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| CN115187452A (en) * | 2022-09-05 | 2022-10-14 | 山东科技职业学院 | Image processing method and device |
| CN115544362A (en) * | 2022-10-11 | 2022-12-30 | 读书郎教育科技有限公司 | AI-based content recommendation system |
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| CN117478937A (en) * | 2023-12-01 | 2024-01-30 | 陕西伟辰科技有限公司 | Processing method based on push information and information push platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115187452A (en) * | 2022-09-05 | 2022-10-14 | 山东科技职业学院 | Image processing method and device |
| CN115544362A (en) * | 2022-10-11 | 2022-12-30 | 读书郎教育科技有限公司 | AI-based content recommendation system |
| CN116824824A (en) * | 2023-01-28 | 2023-09-29 | 太原杰安易科技有限公司 | Wireless signal acquisition and transmission system based on node controller for coal dressing |
| CN116824824B (en) * | 2023-01-28 | 2023-11-21 | 太原杰安易科技有限公司 | Wireless signal acquisition and transmission system based on node controller for coal dressing |
| CN116700968A (en) * | 2023-06-09 | 2023-09-05 | 广州银汉科技有限公司 | Intelligent interaction system based on elastic expansion |
| CN116756427A (en) * | 2023-06-27 | 2023-09-15 | 鼎翰文化股份有限公司 | Travel information pushing system based on big data |
| CN116756427B (en) * | 2023-06-27 | 2024-05-14 | 鼎翰文化股份有限公司 | Travel information pushing system based on big data |
| CN117493677A (en) * | 2023-11-10 | 2024-02-02 | 成达文化科技(广州)有限公司 | Personalized search information recommendation system and method based on user portraits |
| CN117493677B (en) * | 2023-11-10 | 2024-05-28 | 成达文化科技(广州)有限公司 | Personalized search information recommendation system and method based on user portraits |
| CN117478937A (en) * | 2023-12-01 | 2024-01-30 | 陕西伟辰科技有限公司 | Processing method based on push information and information push platform |
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