Research On Personalized Movie Recommendation Algorithm Based On Cloud Platform

With the rapid growth and widely using of Internet, people have entered the era of the lack of information from the era of big data, Consistent with search engines,personalized recommendation system is also a tool to help users quickly and find useful information in the case of information overload, it is based on the user's characteristics and historical behavior, the initiative to recommend content to users in line with their personal interests.It is collaborative filtering recommendation algorithm that the most important method of personalized system recommender has been widely applied in practice. In this paper, the key issues of the cold start problem with the existing collaborative filtering recommendation algorithm faced scalability issues,user trust issues in the following areas accordingly theoretical and applied research:Firstly, we overview the latest trends in the overall development of the recommendation system, and then summarizes the main characteristics of each recommendation algorithm, scope, and finally focuses on the new generation of large data processing framework Spark, including the design of mechanisms and principles for future research laid the theoretical basis and experimental basis.In this paper, two collaborative filtering models, namely CSVD and NCSVD, are investigated to deal with two problems of the traditional model-based collaborative filtering algorithms,in particular, the problem of data sparsity and the problem of cold start. In the CSVD model,a confidence factor is introduced to the matrix factorization model to adjust the bias weight of each item according to its size. The NCSVD model then solve the cold start problem by using a implicity feedback factor based on the CSVD model. Experimental results on realistic datasets showed that our proposed models have better prediction results than the state of the art methods.In order to construct a collaborative filtering algorithm based on trust and matrix factorization,trust mechanism will be introduced to these improved collaborative filtering algorithm,The results show that trust is an important factor affecting the recommendation system, the recommendation system is significant for its research.In order to breakthrough in the face of huge amounts of data computing bottleneck, this article on the Spark platform implementation CSVD algorithm parallelization. Aiming at the shortcomings of the CSVD algorithm, After theparallelization of the experimental results show that the algorithm is better clustering results,in the face of huge amounts of data have a good speedup and scalability.