Intelligent recommendation algorithm based on hidden Markov chain model

An intelligent recommendation algorithm based on hidden Markov chain model is proposed and used to conduct intelligent recommendation on document search in this paper. This algorithm is faster in operational efficiency than regular Markov chain algorithm and the collaborative filtering algorithm and has a certain improvement in the accuracy of recommendation. Firstly, the algorithm has advantages of both information gain ratio and the hidden Markov chain. The information gain ratio and similarity respectively replaced the initial probability and state transition probability of hidden Markov chain model. Secondly, this paper combines conventional hidden Markov chain to analyze information attributes and gets more strongly related with the target attribute properties at the same time, finally we make a recommendation. The algorithm makes up for the shortcoming of the bias of the state with a large number when a single hidden Markov chain model was used as a recommendation algorithm, and the disadvantage of being extremely complex when introducing parameters in a feasible and practical way.