Research On The Hybrid Recommendation Algorithm In WEB Recommendation System

In the rapid development of Internet technology today, people were creating new information while getting information. Being around with huge amounts of data, users might not find the correct information. Then Personalized Recommendation System was created. It could predict a user’s interest preferences by analyzing the historical user-generated data in the system and showed the right items to the user.Collaborative Filtering Algorithm was commonly used in the recommended system technology, which could be divided into two categories algorithms:data mining focus on local properties and data mining focuses on global characteristics. Hybrid Recommendation System contained these two algorithms and could keep their respective advantages. However, Collaborative Filtering Algorithms was faced with the problems of poor scalability and data sparsity, and especially Collaborative Filtering Algorithm focusing on the characteristics of local data mining was more sensitive to data sparsity issues. To solve the problems of poor scalability and data sparsity faced by Collaborative Filtering Algorithm and Hybrid Algorithm, this paper had some research works below:1. Research and design of the Weight-Slope One and the RSVD model solving methods in Hadoop parallel distributed platform, in order to solve the problem of user behavior data in large scale and the algorithm to face the problem of scalability, and this method can be used for improved the Weight-Slope One algorithm and the hybrid recommendation algorithm off-line calculation. Then, proposed the use of similarity and the time factor to the improved Weight-Slope One algorithm, enhance the ability of the algorithm to mining the local characteristics of the data. At the same time, the improved algorithm with the data sparseness problem, the use of user information and the complement of the correlation matrix, to a certain extent, ease the improved algorithm prediction accuracy’ s loss in the face of sparse data. The contents of this part of research to enhance the ability of local characteristics of the data mining algorithm and alleviate the loss of accuracy caused by data sparseness.2. In the basis of the above study on the first point, proposes an improved hybrid algorithm based on Weight-Slope One and RSVD model, the off-line’s Calculate in hybrid recommendation algorithm can be parallel implementation scheme of using the above research and design on Hadoop platform, experimental results show that the hybrid algorithm can better so as to enhance the precision of prediction algorithm, at the same time, the algorithm can adapt to the sparsity of data to some extent.