Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material.
Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.
The many academic areas covered in this publication include, but are not limited to:
Information technology specialists survey recent trends and patterns in data mining tools and techniques in the area of collaborative filtering. Their topics include a modified single-pass clustering algorithm based on median as a threshold similarity value, a classification framework for applying data mining in collaborative filtering, big data mining using collaborative filtering, a data analytics approach to combining user co-rating and social trust for collaborative recommendation, and statistical relational learning for collaborative filtering.