earticle

영어

As an important one of recommendation technologies, collaborative filtering algorithms have many advantages, and have been very successful in both research and practice. However they also remain fundamental challenges, such as data sparsity, cold-start, dynamic changes of users’ preferences and interests, and scalability. For purpose of lessening inaccurate recommendations caused by data sparsity, the pre-filled rating matrix is created by introducing a novel similarity computation method to replace the traditional ones. To address the cold-start issue, we propose a hybrid recommendation method that combines collaborative filtering and content-based filtering exploiting the advantages of both methods. To respond positively to dynamic changes of users’ preferences and interests, the improved algorithm takes time factor into consideration as well. Finally we implement parallel execution of the improved algorithm on Hadoop platform, which addresses serious scalability issue when working on big data. The experimental evaluation of our proposed methods took place and the results showed that the improved algorithm has better recommendation quality and real-time performance.