Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems

The Collaborative Filtering recommendation systems have been developed to address the information overload problem and personalize the content to the users for business and organizations. However, the Collaborative Filtering approach has its limitation of data sparsity and online scalability problems which result in low recommendation quality. In this thesis, a novel Collaborative Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means clustering to partition the entire dataset reduces the time complexity and improves the online scalability as well as the data density. Moreover, the similarity comparison method predicts and fills up the missing value in sparsity dataset to enhance the data density which boosts the recommendation quality. This thesis uses MovieLens dataset to investigate the proposed method, which yields amazing experimental outcome on a large sparsity data set that has a higher quality with lower time complexity than the traditional Collaborative Filtering approaches

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area