Collaborative filtering: Difference between revisions

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== Overview ==
== Overview ==
The term '''Collaborative Filtering''' was first coined by the makers of one of the earliest recommendation systems, '''Tapestry'''.
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 co-related if both users rate ''n'' items similarly.
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:<br />
Collaborative Filtering systems follow this approach to produce recommendations:<br />

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Definition

A 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 or 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. Combine the ratings of these neighbors to make recommendations.

Collaborative Filtering Techniques

Memory-based(Heuristic) Recommendation Technique

Model-based Recommendation Technique

Hybrid Recommendation Technique

Limitations of Collaborative Filtering

References