In this example a positive rating has the value 1 while a negative rating has the value –1, but in other cases a rating could also be a continuous number. Note that a negative correlation can also be used as a weight.

For example, because Amy and Jef have a negative correlation and Amy did not like “Farg” could be used as an indication that Jef will enjoy “Fargo”.

This information is used in the decision on which movie to see.

Most collaborative filtering systems apply the so called neighborhood-based technique.

Item-based collaborative filtering is a model-based algorithm for making recommendations.

In the algorithm, the similarities between different items in the dataset are calculated by using one of a number of similarity measures, and then these similarity values are used to predict ratings for user-item pairs not present in the dataset.

To illustrate how a collaborative filtering system makes recommendations consider the example in movie ratings table below.

This shows the ratings of five movies by five people.

In this paper, we study collaborative filtering for people-to-people recommendation in online dating, comparing this approach to a baseline profile matching method.

Initial data analysis highlights the problem of over-recommending popular users, a standard problem for collaborative filtering applied to product recommendation, but more acute in people-to-people recommendation.

A common perception is that online dating systems “match” people on the basis of profiles containing demographic and psychographic information and/or user interests.