Collaborative filtering uses data from multiple sources to develop profiles of people with similar tastes and spending habits. It is used for marketing, predicting user tastes, and curating user input on websites. The system recommends items based on similar user preferences, but requires a large amount of data to be effective. It is also used for personalized marketing and is a hot commodity among marketers.
Collaborative filtering is a data processing method that relies on using data from numerous sources to develop profiles of people who are related by similar tastes and spending habits. This technique is used in a number of different settings. Some of the most popular collaborative filtering applications can be seen on the Internet, where it is used for marketing, predicting user tastes, and curating sites that rely on user input to function.
In a simple example of how collaborative filtering works, a website might want to set up a recommendation system for television shows. Site users provide data upon login and list the shows they like. This data is in turn used to identify users with similar tastes. If 75% of the people who like show A like show B, the system can infer that people who like one show probably like the other. Therefore, when a user logs in and identifies himself as a fan of Show A looking for suggestions, the system can recommend Show B.
For collaborative filtering to work, a lot of data is needed. The larger the population from which data is extracted, the more useful and effective the data will be. Small amounts of data are more likely to end up with insignificant results, such as false connections resulting in poor predictions of tastes. Such systems often suffer from a cold boot problem, where they are slow to develop because the database needs to be populated first. Early adopters may get frustrated with the system because it makes wrong recommendations since it doesn’t have enough data.
Collaborative filtering is also used extensively on social networking sites and sites that provide tools such as corporate bookmarking, where users share and promote links to sites they find interesting. As users add to the body of data in the system, the system can begin making recommendations designed to suit each user’s tastes. For example, a social bookmarking site might generate random links based on connections and users that someone has expressed sympathy for in the past.
Marketers can use the collaborative filter to provide users with very precisely targeted marketing. This personalized marketing can be very effective as users feel that they are being addressed personally and are more likely to accept recommendations as a result. Voluntarily provided large amounts of data on websites such as social networking sites are a hot commodity among marketers, who buy data from such sites to develop customized campaigns.
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