recommendation

README

Recommendation

A simple item-item and user-item recommendation engine using cosine similarity.

General overview

As input a user x item binary matrix is used. gonum Matrixes can be used or a LabeledMatrix.

• First a item x item similarity matrix is generated, using NewCosineLabeledMatrix. Each cell contains the cosine similarity between the corresponding row and column item.
• Using this matrix a second matrix can be generated containing the top-N most similar items per item. Taking each row (or column) sorting these to get the most similar items. NewTopSimilaritiesFromMatrix will help you to do so.
• Now using this matrix, it's possible to implement item x item, items x items and user x items recommendation. item x item, is simply the first element from the top-N matrix, the latter can be achieved by using the ScoredSimilar function.

Example

The example folder implements a basic user -> items recommendation system. The input is a labeled binary CSV, where labels are present on the first row and column. Each row contains one user, each column is an item, the values are binary (either 0 or 1).

Future ideas

• Calculate a cut off value, don't simply take the top-N similar items, but the top most similar based on the cut off value.