Mrec - Revision history

Zeno Gantner at 06:09, 4 January 2014

2014-01-04T06:09:46Z

Alan at 22:53, 9 December 2013

2013-12-09T22:53:19Z

Gamboviol at 20:33, 8 December 2013

2013-12-08T20:33:45Z

Gamboviol at 20:30, 8 December 2013

2013-12-08T20:30:44Z

Gamboviol at 20:27, 8 December 2013

2013-12-08T20:27:03Z

Gamboviol: Created page with "'''mrec''' is a Python package developed at Mendeley to support recommender systems development and evaluation. The package currently focuses on item similarity and other meth..."

2013-12-08T19:39:23Z

Created page with "'''mrec''' is a Python package developed at Mendeley to support recommender systems development and evaluation. The package currently focuses on item similarity and other meth..."

New page

'''mrec''' is a Python package developed at Mendeley to support recommender systems development and evaluation. The package currently focuses on item similarity and other methods that work well on implicit feedback, and on experimental evaluation.

mrec tries to fill two small gaps in the current landscape, firstly by supplying simple tools for consistent and reproducible evaluation, and secondly by offering examples of how to use IPython.parallel to run the same code either on the cores of a single machine or on a cluster. The combination of IPython and scientific Python libraries is very powerful, but there are still rather few examples around that show how to get it to work in practice.

Highlights:

* a (relatively) efficient implementation of the SLIM item similarity method [1].
* an implementation of Hu, Koren & Volinsky's WRMF weighted matrix factorization for implicit feedback [2].
* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss [3].
* a hybrid model optimizing the WARP loss for a ranking based jointly on a user-item matrix and on content features for each item.
* utilities to train models and make recommendations in parallel using IPython.
* utilities to prepare datasets and compute quality metrics.

Documentation for mrec can be found at http://mendeley.github.io/mrec.

The source code is available at https://github.com/mendeley/mrec.

[[Category: Python]]
[[Category: Software]]

@@ Line 1: / Line 1: @@
 '''mrec''' is a BSD licenced [[Python]] package developed at [[Mendeley]] to support recommender systems development and evaluation.
-The package contains implementations of methods which work well in the most common real world recommendation scenario i.e. [[top-n recommendation]] based on implicit feedback. It also supplies tools for consistent and reproducible evaluation of recommendations produced using mrec itself or with any other framework. Last but not least it offers an example of how to use IPython.parallel to run the same code in parallel either on the cores of a single machine or on a cluster.
+The package contains implementations of methods which work well in the most common real world recommendation scenario i.e. [[top-n recommendation]] based on [[implicit feedback]]. It also supplies tools for consistent and reproducible [[evaluation]] of recommendations produced using mrec itself or with any other framework. Last but not least it offers an example of how to use IPython.parallel to run the same code in parallel either on the cores of a single machine or on a cluster.
 mrec includes base class implementations designed to make it very easy to add further recommender algorithms based either on [[matrix factorization]] or [[item similarity]] computation. Please do contribute!
@@ Line 7: / Line 7: @@
 Highlights:
-* a (relatively) efficient implementation of the SLIM item similarity method.<ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13</ref>
+* a (relatively) efficient implementation of the [[SLIM]] item similarity method.<ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13</ref>
-* an implementation of Hu, Koren & Volinsky's WRMF [[weighted matrix factorization]] for implicit feedback.<ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>
+* an implementation of Hu, Koren & Volinsky's [[WRMF]] [[weighted matrix factorization]] for implicit feedback.<ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>
-* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss.<ref>Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35</ref>
+* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise ([[WARP]]) ranking loss.<ref>Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35</ref>
 * a hybrid model optimizing the WARP loss for a ranking based jointly on a [[user-item matrix]] and on content features for each item.
 * utilities to train models and make recommendations in parallel.

@@ Line 1: / Line 1: @@
-'''mrec''' is a BSD licenced Python package developed at Mendeley to support recommender systems development and evaluation.
+'''mrec''' is a BSD licenced [[Python]] package developed at [[Mendeley]] to support recommender systems development and evaluation.
-The package contains implementations of methods which work well in the most common real world recommendation scenario i.e. top-n recommendation based on implicit feedback. It also supplies tools for consistent and reproducible evaluation of recommendations produced using mrec itself or with any other framework. Last but not least it offers an example of how to use IPython.parallel to run the same code in parallel either on the cores of a single machine or on a cluster.
+The package contains implementations of methods which work well in the most common real world recommendation scenario i.e. [[top-n recommendation]] based on implicit feedback. It also supplies tools for consistent and reproducible evaluation of recommendations produced using mrec itself or with any other framework. Last but not least it offers an example of how to use IPython.parallel to run the same code in parallel either on the cores of a single machine or on a cluster.
-mrec includes base class implementations designed to make it very easy to add further recommender algorithms based either on matrix factorization or item similarity computation. Please do contribute!
+mrec includes base class implementations designed to make it very easy to add further recommender algorithms based either on [[matrix factorization]] or [[item similarity]] computation. Please do contribute!
 Highlights:
 * a (relatively) efficient implementation of the SLIM item similarity method.<ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13</ref>
-* an implementation of Hu, Koren & Volinsky's WRMF weighted matrix factorization for implicit feedback.<ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>
+* an implementation of Hu, Koren & Volinsky's WRMF [[weighted matrix factorization]] for implicit feedback.<ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>
 * a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss.<ref>Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35</ref>
-* a hybrid model optimizing the WARP loss for a ranking based jointly on a user-item matrix and on content features for each item.
+* a hybrid model optimizing the WARP loss for a ranking based jointly on a [[user-item matrix]] and on content features for each item.
 * utilities to train models and make recommendations in parallel.
 * utilities to prepare datasets and compute quality metrics.

@@ Line 1: / Line 1: @@
-'''mrec''' is a Python package developed at Mendeley to support recommender systems development and evaluation. The package contains implementations of methods which work well in the most common real world recommendation scenario i.e. top-n recommendation based on implicit feedback. It also supplies tools for consistent and reproducible evaluation of recommendations produced using mrec itself or with any other framework. Last but not least it offers an example of how to use IPython.parallel to run the same code in parallel either on the cores of a single machine or on a cluster.
+'''mrec''' is a BSD licenced Python package developed at Mendeley to support recommender systems development and evaluation.
 Highlights:
-* a (relatively) efficient implementation of the SLIM item similarity method <ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13</ref>.
+* a (relatively) efficient implementation of the SLIM item similarity method.<ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13</ref>
-* an implementation of Hu, Koren & Volinsky's WRMF weighted matrix factorization for implicit feedback <ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>.
+* an implementation of Hu, Koren & Volinsky's WRMF weighted matrix factorization for implicit feedback.<ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>
-* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss <ref>Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35</ref>.
+* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss.<ref>Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35</ref>
 * a hybrid model optimizing the WARP loss for a ranking based jointly on a user-item matrix and on content features for each item.
 * utilities to train models and make recommendations in parallel.

@@ Line 3: / Line 3: @@
 Highlights:
-* a (relatively) efficient implementation of the SLIM item similarity method [1].
+* a (relatively) efficient implementation of the SLIM item similarity method <ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13</ref>.
-* an implementation of Hu, Koren & Volinsky's WRMF weighted matrix factorization for implicit feedback [2].
+* an implementation of Hu, Koren & Volinsky's WRMF weighted matrix factorization for implicit feedback <ref>Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08</ref>.
-* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss [3].
+* a matrix factorization model that optimizes the Weighted Approximately Ranked Pairwise (WARP) ranking loss <ref>Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35</ref>.
 * a hybrid model optimizing the WARP loss for a ranking based jointly on a user-item matrix and on content features for each item.
 * utilities to train models and make recommendations in parallel.
@@ Line 13: / Line 13: @@
 The source code is available at https://github.com/mendeley/mrec.
 [[Category: Python]]
 [[Category: Software]]