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Parent-child reading resource recommendation service system and method
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A technology for recommending services and resources, applied in the digital field, can solve the problems that will affect the accuracy of recommended books, the process of cost is complex, and the accuracy is not high.
Pending Publication Date: 2021-05-07
上海斐杰教育科技有限公司
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Problems solved by technology
[0003] 1. When the platform recommends books to users, it needs to recommend according to the reading behavior of a large number of users. The process is very complicated and the accuracy is not high;
[0004] 2. When the platform uses scoring to recommend books, users will not form the habit of scoring at any time, which will affect the accuracy of recommended books;
Method used
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Embodiment 1
[0074] Utilize the time collection unit to collect the dynamic time length and static time length that the user spends on viewing the content after opening the system, the dynamic viewing time length is D, the static viewing time length is J, and the set of dynamic viewing time length is D={0.5,1, 0.4,1.2,1,0.5,0.4}, the set of static viewing time is J={40,35,20,50,57,62,45}, the set of daily reading volume is A={30,15, 36,45,26,60,32};
[0075] According to the formula:
[0076]
[0077]
[0078]
[0079] From the above calculation, it can be concluded that the time it takes for a user to read a book is 186 minutes;
Embodiment 2
[0081] The score storage unit is used to calculate 2.5 points for every ten pages in the books read by the user. The score storage unit measures that the total number of pages contained in a book is 400, and the set of pages read by the user is E={300,60,262,400,350} , the score set of the total number of people watching 10 and other people watching is Q={60,45,24,71,66};
[0082] According to the formula:
[0083]
[0084]
[0085] From the above calculation, it can be concluded that each user’s evaluation score for the book is {75, 15, 65.5, 100, 87.5}, and the total average score calculated by 10 users for the books they read is 60.9 points;
Embodiment example 3
[0086] Example 3: In the step S3, the data calculation unit is used to select users with similar average scores in a certain book, and the set of scores of several users is Z={65, 42, 90, 85, 76}, a number of The average score given by a user to a book The similarity rate between users is The similarity rate is controlled at 0.01
[0093] It is found that the similarity rate between the first user and the second user is high, and the similarity rate between the third user and the fourth user is high, and it is inferred that the first user has a higher preference rate for such books than other users.
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Abstract
The invention discloses a parent-child reading resource recommendation servicesystem and method, and relates to the technical field of digitization, the parent-child reading resource recommendation servicesystem comprises an information collection module, a prediction recommendation module, a score prediction module and a recommendation updating module, the information collection module and a platform can collect information in a user login mode, and the prediction recommendation module is connected with the score prediction module and the recommendation updating module. The platform can basically speculate the hobbies of the users through the collected information, the system is scientific, reasonable and safe and convenient to use, the score prediction module and the platform can score the books through the browsing amount of the users, similar scoring users are recommended to the users through the scoring result, the hobbies of the users are further speculated. According to the recommendation updating module, the platform updates the hobbies of the user through comparison of two groups of speculation data, and large deviation of a prediction result caused by complete prediction is avoided.
Description
technical field [0001] The invention relates to the field of digital technology, in particular to a parent-child reading resource recommendation service system and method. Background technique [0002] With the accelerated development of the modern network, the functions of the reading recommendation system are becoming more and more diverse. Usually, when providing reading to the user, it will intelligently recommend similar books for the user to read, and will use the information read by the user to infer the user's hobbies. Recommend other users with similar hobbies to the user to form a circle of the same hobbies, or select similar books from the user's friends for recommendation, and recommend similar books to users by using user ratings, but when recommending books, it will The following problems arise: [0003] 1. When the platform recommends books to users, it needs to recommend according to the reading behavior of a large number of users. The process is very compli...
Claims
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Application Information
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