13 Facts About Collaborative filtering

Collaborative filtering is a technique used by recommender systems.

Collaborative filtering has two senses, a narrow one and a more general one.

The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person.

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 applications where the focus is on user data, etc.

Collaborative filtering is one of the techniques used for dealing with this problem.

Motivation for collaborative filtering comes from the idea that people often get the best recommendations from someone with tastes similar to themselves.

Collaborative filtering encompasses techniques for matching people with similar interests and making recommendations on this basis.

Key problem of collaborative filtering is how to combine and weight the preferences of user neighbors.

Collaborative filtering systems have many forms, but many common systems can be reduced to two steps:.

One scenario of collaborative filtering application is to recommend interesting or popular information as judged by the community.

The collaborative filtering system requires a substantial number of users to rate a new item before that item can be recommended.

Collaborative filtering filters are expected to increase diversity because they help us discover new products.

The interaction-associated information - tags - is taken as a third dimension in advanced collaborative filtering to construct a 3-dimensional tensor structure for exploration of recommendation.