org.grouplens.lenskit.knn.item.package-info Maven / Gradle / Ivy
/*
* LensKit, an open source recommender systems toolkit.
* Copyright 2010-2014 LensKit Contributors. See CONTRIBUTORS.md.
* Work on LensKit has been funded by the National Science Foundation under
* grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of the
* License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along with
* this program; if not, write to the Free Software Foundation, Inc., 51
* Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
/**
* Implementation of item-item collaborative filtering.
*
* The item-item CF implementation is built up of several pieces. The
* {@linkplain org.grouplens.lenskit.knn.item.model.ItemItemModelBuilder model builder} takes the rating data
* and several parameters and components, such as the
* {@linkplain org.grouplens.lenskit.vectors.similarity.VectorSimilarity similarity function} and {@linkplain ModelSize model size},
* and computes the {@linkplain org.grouplens.lenskit.knn.item.model.SimilarityMatrixModel similarity matrix}. The
* {@linkplain ItemItemScorer scorer}
* uses this model to score items.
*
* The basic idea of item-item CF is to compute similarities between items, typically
* based on the users that have rated them, and the recommend items similar to the items
* that a user likes. The model is then truncated — only the {@link ModelSize} most similar
* items are retained for each item – to save space. Neighborhoods are further truncated
* when doing recommendation; only the {@link org.grouplens.lenskit.knn.NeighborhoodSize} most similar items that
* a user has rated are used to score any given item. {@link ModelSize} is typically
* larger than {@link org.grouplens.lenskit.knn.NeighborhoodSize} to improve the ability of the recommender to find
* neighbors.
*
* When the similarity function is asymmetric (\(s(i,j)=s(j,i)\) does not hold), some care
* is needed to make sure that the function is used in the correct direction. Following
* Deshpande and Karypis, we use the similarity function as \(s(j,i)\), where \(j\) is the
* item the user has purchased or rated and \(i\) is the item that is going to be scored. This
* function is then stored in row \(i\) and column \(j\) of the matrix. Rows are then truncated
* (so we retain the {@link ModelSize} most similar items for each \(i\)); this direction differs
* from Deshpande & Karypis, as row truncation is more efficient & simpler to write within
* LensKit's item-item algorithm structure, and performs better in offline tests against the
* MovieLens 1M data set
* (see writeup).
* Computation against a particular item the user has rated is done down that item's column.
*
* The scorers and recommenders actually operate on a generic {@link org.grouplens.lenskit.knn.item.model.ItemItemModel}, so the
* item-based scoring algorithm can be used against other sources of similarity, such as
* similarities stored in a database or text index.
*/
package org.grouplens.lenskit.knn.item;