Book recommending method based on Markov chain

A Markov chain and recommendation method technology, applied in the field of network applications, can solve the problems of books not conforming to the user's current preferences, slowly changing, and missing information, so as to reduce sparsity, improve efficiency and accuracy, and improve efficiency and accuracy. The effect of practicality

Active Publication Date: 2013-03-20
新讯数字科技(杭州)有限公司
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Problems solved by technology

When recommending books to users, the user's reading preferences are likely to be slowly changing, which can be reflected according to the user's recent reading books. If only the user's static data is used without combining ti

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  • Book recommending method based on Markov chain
  • Book recommending method based on Markov chain

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[0015] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0016] The present invention is based on the Markov chain prediction model, and first uses the reading history data of individual users to establish a corresponding naive Bayesian prediction model for each user, and calculates the probability that the user belongs to the state of liking or disliking the book, that is, obtains The initial state probability vector of the Markov chain; combined with the reading history data of all users, using the book preference status of all users to calculate the transition probability matrix between the book preference states, forming the transition probability matrix in the Markov chain ; Finally, the above two parts of information are combined to form a complete Markov chain prediction model, and the personalized...

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Abstract

A book recommending method based on the Markov chain comprises the steps: (1) inquiring reading history data of a user, and calculating an initial state probability vector of the user based on the simple Bias algorithm, (2) inquiring a current read book and all unread books of the user, and respectively calculating a transition probability matrix from the current read book to each unread book according to like and dislike of the user on the current read book and the unread books of the user, (3) respectively calculating a state probability vector from the current read book to each unread book according to the initial state probability vector of the user and the transition probability matrix from the current reading book to each unread book, and recommending unread books to the user based on the state probability vector which includes the state probability of like of the user on the unread books. The book recommending method belongs to the field of network application technology, and is capable of recommending books in a personalized mode according to dynamic behaviors of the user.

Description

technical field [0001] The invention relates to a Markov chain-based book recommendation method, which belongs to the technical field of network applications. Background technique [0002] With the rapid development of mobile and Internet technologies, the digitization of books has become an inevitable trend. More and more book reading platforms have received high attention from users and achieved rapid development, and have become an important way for people to obtain information and knowledge. [0003] There are usually a large number of digital book resources on the book reading platform. How to effectively use these rich and valuable resources, so that users can find and make full use of them more quickly is very important. A very important function of the platform. [0004] At present, the personalized intelligent recommendation of books is mainly divided into content-based book recommendation, collaborative filtering book recommendation, knowledge-based book recommen...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 廖建新郭伟东张雷赵贝尔崔晓茹
Owner 新讯数字科技(杭州)有限公司
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