Hidden Markov chain model based intelligent recommendation algorithm

Abstract

The invention discloses a hidden Markov chain model based intelligent recommendation algorithm, which is applied to an intelligent recommendation system of a law net. The algorithm comprises the steps of for characteristics of documents, replacing an initial probability and a state transfer probability of a state in a hidden Markov chain model with an information gain ratio and a correlation degree respectively; calculating a partial probability of reading a <t>th document by a client; calculating an optimal probability and an optimal document sequence of each document when a document number t is equal to n, and selecting the optimal probability with the maximum value from all the optimal probabilities; and recording the optimal document sequences, namely, all the document sequences recommended to the client. According to the implementation scheme provided by the invention, the advantages of the information gain ratio and a hidden Markov chain are combined, and the shortcoming of high recommendation deviation quantity due to single use of a hidden Markov chain algorithm as a recommendation algorithm and the shortcoming of complexity in parameter calculation are made up for, so that the hidden Markov chain model based intelligent recommendation algorithm has relatively high practicality and relatively high accuracy in recommendation algorithms.

Description

technical field [0001] The invention relates to the field of intelligent recommendation, in particular to an intelligent recommendation algorithm based on a hidden Markov chain model. Background technique [0002] The emergence and popularization of the Internet has brought a large amount of information to users, which has met the needs of users for information in the information age. Sometimes you can't get the part of information that is really useful to you, and the efficiency of using information is reduced. This is the so-called information overload problem. [0003] A very potential solution to the problem of information overload is the recommendation system, which is a personalized information recommendation system that recommends the information and products that the user is interested in to the user according to the user's information needs and interests. Compared with search engines, the recommendation system conducts personalized calculations by studying the user...

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Problems solved by technology

[0006] Based on this, aiming at the above-mentioned prior art, the purpose of the present invention is to provide an intelligent recommendation algorithm based on hidden Markov chain model, which combines the advantages of information gain rate and hidden Markov chain, and makes up for the hidden Markov chain model alone. When the Cove chain algorithm is used as a recommendation algorithm, the disadvantages of recommending a large number of states, and the complexity of solving parameters

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