mathorcup数学建模挑战赛获奖论文-第四届B题_20025E.doc

information-shortage era, but has brought the public overloaded information. In the

present era, both consumers and producers of information are facing significant

challenges: consumers find it is harder to have access to information they are

interested in, with explosively growing information around them; and producers also

find it is harder to make their information focused by readers.

1.Analyze the effects on book remarks by users.

2. Design amodelto predict the remarks by users on their unread book.Information of

users is attached in the file ‘predict.txt’.

3.Recommedend every user (attached in the file;predict.txt’)three books,which they

have yet read before.

processing. Therefore, we need to seize the key and useful date to solve the problem

and transform, screen, analyze, summarize the data to analyze the factors affecting the

user’s score to books, based on which we complete the user score prediction of books

different factors impact on scores of books, and then filter the data for other analyzes,

the relationship between the various stages of books initially been evaluated scores

and the number of labels, the relationship with social friends, relations with views of

books. Finally, the data obtained were analyzed and summarized the scientific obtain

affect users of books score factors.

2.2 Analysis of problem 2

of predict.txt. First, the number of tags, social relationships, books to study three

aspects of views, which is a multi-objective decision-making problems. According

problem, you can use the software to build hierarchical analysis yaahp total score -

neural network structure, the number of different users to tag books, the number of

users of books score as output, based on known data to train the neural network, the

finally the use of the trained network user score prediction for books.

2.3 Analysis of problem 3

annex of predict.txt. Considering the book's praise higher the frequency, the higher

the level of the user's favorite books, using spss for user_book_score.txt attachment

filter, get a fifth of all books rated frequency, and then sort processing in EXCEL,

using LOOKUP function screening this user has not read the book to get the ID, select

three of the most frequently rated as fifth book is recommended to the user's three

books ID, and then finally get circulation problems were analyzed five times the

required five users Recommended books ID.

more.