Collaborative Filtering and Recommender System in Knowledge Management
During live interactions, techniques enabling discovery of knowledge are used in recommender systems. Their use is normally in solving the issue of recommending information, services or products. This is the concept referred to as collaborative filtering. The systems, particularly the k-nearest neighbor based on collaborative filtering have achieved increasing effectiveness on the Web. The techniques have a critical role of filling in the user-item association matrix’s missing entries. Model-based collaborative filtering is currently supported by MLlib, where the defining of products and users is done by a miniature set of concealed factors that can apply to predicting the entries that are missing. The alternating least squares (ALS) algorithm is applied by MLlib in learning the concealed factors (Adomavicius and Tuzhilin, 2005).
From a knowledge management point of view, collaborative filtering is basically information filtering process or models utilizing techniques that involve collaboration amongst some viewpoints, agents, data sources, etc. use of these techniques typically entails data sets that are extremely large. According to Francesco, Lior and Bracha (2011), the methods of collaborative filtering are applicable to diverse types of data such as sensing and monitoring data, financial data, environmental sensing, or in e-commerce and web-based applications. From a simpler perspective, the conce…
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