“Based on the literature review from your reading assignment, conduct a document analysis and write a review to list definitions, factors and indicators of collaborative filtering. Post it in your blog.”
Definitions, factors and indicators of Collaborative Filtering.
According to Wiki, Collaborative filtering (CF) is “the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets.”
“Collaborative filtering methods have been applied to many different kinds of data including sensing and monitoring data – such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data – such as financial service institutions that integrate many financial sources; or in electronic commerce and web 2.0 applications where the focus is on user data, etc. The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well.”
Collaborative filtering is also a method of “making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of the CF approach is that those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user’s tastes (likes or dislikes).”
Webwhompers defines CF as getting expert opinions without the experts using web techniques for generating personalized recommendations. Some of the examples include Amazon, iTunes, Netflix, LastFM, StumbleUpon, and Delicious.
It is also mentioned that CF “needs no built-in subject knowledge to generate recommendations. Many other Web sites do rely on built-in expert subject knowledge to generate recommendations, and so do not use collaborative filtering.”