Collaborative filtering

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Definition

Collaborative Filtering(CF) refers to the use of software algorithms for narrowing down a large set of choices by using collaboration among multiple agents, viewpoints, and data sources.

Overview

The term Collaborative Filtering was first coined by the makers of one of the earliest recommendation systems, Tapestry. The basic assumption in CF is that user A and user B's personal tastes are correlated if both users rate n items similarly.

Collaborative Filtering systems follow this approach to produce recommendations:
1. Gather ratings from users and maintain user's ratings in a database.
2. Compute the correlations between pairs of users to determine a user’s neighbors in taste space
3. Compute the ratings of these neighbors to make recommendations.

Collaborative Filtering requires ratings for an item in order to make a prediction for it. (Novelty and serendipity,accuracy and coverage).Unlike Content filtering, Collaborative filtering does not require content; instead, CF requires ratings for an item to make a prediction for it.(RS)

Collaborative Filtering Techniques

Collaborative Filtering techniques can be separated into 3 classes:

Memory-based(Heuristic) Recommendation Technique

Memory-based algorithms make predictions by operating on data (users, items and ratings) stored in memory. Nearest neighbor algorithms are the most commonly used CF algorithms. They can be classified into two:
1. User-based nearest neighbor
2. Item-based nearest neighbor