Research On Recommendation Algorithm Based On Hidden Markov Model And Collaborative Filtering

The rapid development of Internet and big data has caused information overload problems.How to quickly obtain high-quality and high-precision value data required by targets from massive data is an important issue that modern information workers need to solve urgently.The recommendation system uses data mining techniques such as statistics,mathematics,machine learning and information retrieval to quickly extract key feature data based on massive historical data,and find items that meet user needs for personalized recommendation,greatly reducing the time and cost for users to view redundant information,and improve the economic benefits of various application areas.Although the traditional recommendation algorithm has been relatively developed mature in theoretical and commercial applications,it still faces many problems that have not been solved well,such as high sparseness of data,cold start and dynamic changes of user preferences.In view of the dynamic change of user preference,this paper proposes a fusion algorithm based on the HMM and collaborative filtering recommendation based on the historical behavior data of users,which takes into account the randomness and timeliness of user preference change and can increase the precision of personalized recommendation to a certain extent.This paper carries out a case study analysis based on the film score dataset,and designs two control experiments of traditional collaborative filtering recommendation,so as to examine the enforceability and validity of the fusion algorithm of HMM and collaborative filtering recommendation.The HMM is established based on the time sequence data of film rating,and the model parameters are estimated by EM algorithm.Based on the model parameters,the most likely observation sequence of the user in the next moment is predicted,in order to predict the movie ratings.Rank the top N movies according to their ratings and recommend them to the target users.Based on the analysis of experimental results,it can be seen that the fusion algorithm of the HMM and CF recommendation proposed in this paper performs slightly better than the control experiment in the precision,recall rate and F1 value of the film scoring data set.