one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting...

Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also known as the personality-based approach), as...

Robust collaborative filtering, or attack-resistant collaborative filtering, refers to algorithms or techniques that aim to make collaborative filtering more...

systems typically use collaborative filtering approaches or a combination of the collaborative filtering and content-based filtering approaches, although...

Item-item collaborative filtering, or item-based, or item-to-item, is a form of collaborative filtering for recommender systems based on the similarity...

Slope One is a family of algorithms used for collaborative filtering, introduced in a 2005 paper by Daniel Lemire and Anna Maclachlan. Arguably, it is...

using one or more of the following: Collaborative filtering Semantic analysis Social rating Collaborative filtering is a method of forecasting often used...

characteristics (content-based filtering) or the user's social environment and past behavior (collaborative filtering). Depending on the system, the user...

Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing...

a hard-thresholding and a Wiener filter stage, both involving the following parts: grouping, collaborative filtering, and aggregation. This algorithm...