Fusion Recommendation Algorithm Based on Hidden Markov Models

Abstract

Abstract:

In view of the problems of the traditional collaborative filtering recommendation algorithm based on the project of data sparseness and the low accuracy of

recommendation, the thesis puts forward the HMM-ItemCF recommendation algorithm which combines Hidden Markov Model with the traditional collaborative filtering recommendation

algorithm based on the project. The algorithm using Hidden Markov Model to all the users in the system evaluation behavior and the history of the target user behavior to carry

on the overall analysis, to find the probability of the next moment a group of users with the highest score object, and the probability of occurrence of these scores with

traditional objects project weighted similarity calculation method to get a new recommendation similarity ultimately produce results. The simulation experiment is carried out on

the algorithm with an important parameter in the training, and compared with other algorithms. It proves that the improved algorithm is effective.

Key words: collaborative filtering, sparse data, time effect, Hidden Markov

YANG An-ju, YANG Yun, ZHOU Yuan-yuan, MIN Yu-juan, QIN Yi. Fusion Recommendation Algorithm Based on Hidden Markov Models[J]. Computer and Modernization.

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