Growth incentive book recommendation method and recommendation system

Abstract

The invention provides a growth incentive book recommendation method, which comprises the following steps of 1, collecting book data, and performing book label labeling; 2, performing feature extraction on the reading data of the book to form three-dimensional features; wherein the three-dimensional features comprise interest features, mood features and character features; and 3, obtaining book information read by a reader, wherein the book information comprises three-dimensional features and book labels; calculating the distance between the book read by the reader and the book in the database, and obtaining a forward recommended book according to a k-nearest neighbor calculation result; and calculating reverse labels of the books by counting the book labels of the books read by the readers to generate reverse recommended books. The invention further provides a growth incentive book recommendation system.

Description

technical field [0001] The invention relates to a recommendation method and a recommendation system, in particular to a book recommendation method and a recommendation system. Background technique [0002] With the advent of the era of electronic reading, the concept of "flow thinking" has entered the field of book publishing. By catering to the attention of large-scale users, attracting user traffic; "fragmented" reading has become the norm for readers, and more than 70% of Internet users spend 1 to 3 hours a day on mobile phone reading, of which nearly half of the time is dedicated to WeChat official account and circle of friends , which proves that the so-called "digital reading" is mostly fragmented and aimless reading, which makes it difficult for people to settle down and dig deeper. [0003] One is that readers' reading habits tend to be "fast food" and "flat". Second, with the changes in living habits, readers' access to books is also limited by the "monopoly" of so...

AI Technical Summary

Problems solved by technology

There are three main problems in these methods: first, the algorithm is highly susceptible to power flow

However, good books need time to accumulate, and sales volume cannot guarantee the quality of books, let alone whether books are suitable for readers themselves; second, this type of algorithm uses the same recommendation scheme as other types of products when recommending books, and does not take advantage of the uniqueness of books themselves. The characteristics of the book, such as the text content of the book, the reader's feelings, etc.

In this way, it is impossible to further optimize the book recommendation algorithm according to the characteristics of the book; third, the algorithm only focuses on strengthening user behavior, ignoring the overall growth of readers

It can be easily found that the above algorithm can only recommend books that are similar to or related to the books the reader has read, but this will also make the reader's reading content increasingly narrow, which is tantamount to the overall growth of the reader.

Images