RecSysWiki - User contributions [en]

Main Page

2013-12-08T20:35:22Z

Gamboviol:

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<div style="font-size:162%;border:none;margin: 0;padding:.1em;color:#000">Welcome to the Recommender Systems Wiki (RecSysWiki) </div>
<div style="top:+0.2em;font-size: 95%">''sharing information on all aspects of [[Recommender System|Recommender Systems]]''</div>
<div id="articlecount" style="width:100%;text-align:center;font-size:85%;">{{NUMBEROFPAGES}} pages and {{NUMBEROFARTICLES}} articles, created by [[Special:ActiveUsers|{{NUMBEROFUSERS}} users]] as of {{CURRENTDAYNAME}}, {{CURRENTMONTHNAME}} {{CURRENTDAY}}, {{CURRENTYEAR}}</div>
<div style="width:100%;text-align:center;font-size:85%;">started on February 10th, 2011</div>
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[[RecSysWiki:About|This Wiki]] is about everything related to [[Recommender System|Recommender Systems]].

[[Special:UserLogin|Registration]] is open to anyone who wishes to contribute.


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* [[:Category:API|APIs]]
* [[:Category:Book|Books]]
* [[:Category:Class|Classes]]
* [[:Category:Company|Companies]]
* [[:Category:Competition|Competitions]]
* [[:Category:Conference|Conferences]]
* [[:Category:Dataset|Datasets]]
* [[:Category:Educational recommender systems|Educational Recommender Systems]]
* [[:Category:Evaluation|Evaluation]]
* [[:Category:Evaluation measure|Evaluation Measures]]
* [[:Category:Event|Events]]
* [[:Category:Job offer|Job Offers]]
* [[:Category:Journal|Journals]]
* [[:Category:List|Lists]]
* [[:Category:Literature|Literature]]
* [[:Category:Method|Methods]]
* [[:Category:Movie recommendation|Movie Recommendation]]
* [[:Category:Music recommendation|Music Recommendation]]
* [[:Category:Paper|Papers]]
* [[:Category:People|People]]
* [[:Category:Project|Projects]]
* [[:Category:Research group|Research Groups]]
* [[:Category:Software|Software]]
* [[:Category:Task|Task]]
* [[:Category:User-centric evaluation|User-centric evaluation]]
* [[:Category:Workshop|Workshops]]

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<h2 style="margin:0;background-color:#cedff2;font-size:120%;font-weight:bold;border:1px solid #a3b0bf;text-align:left;color:#000;padding:0.2em 0.4em;">Information on RecSysWiki</h2>
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* [[:RecSysWiki:Current events| Current Events]]
* [[Special:Statistics|RecSysWiki statistics]]
* [[Special:AllPages|All pages]]
* [[Special:Categories|All categories]]
* [[Special:WantedPages|Wanted pages]]
* [[To Do List]] - ''please help by contributing''

<h2 style="margin:0;background-color:#cedff2;font-size:120%;font-weight:bold;border:1px solid #a3b0bf;text-align:left;color:#000;padding:0.2em 0.4em;">Recent additions and updates</h2>
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* [[Mrec]] 
* [[MovieLens]] 
* [[MovieLens 1M]] 
* [[MovieLens 10M]] 
* [[MovieLens 100k]] 
* [[UMAP 2013]] 
* [[Open positions at Nokia 2013]] 
* [[Plista Contest]] 
* [[MyMediaLite]] 
* [[RecSys 2013]] 
* [[Open positions at Otto Group 2012]] 
* [[Open positions at Zalando 2012]] 
* [[RecSys get-together in Berlin]] 
* [[RecommenderAPI for Drupal]] 
* [[Open positions at Apaxo 2012]] 
* [[Recommendable]] 
* [[Filmtipset]] 
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[[Category:RecSys Wiki Information]]

Mrec

2013-12-08T20:33:45Z

Gamboviol:

'''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.

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>
* 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.
* 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.

<references/>

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

Mrec

2013-12-08T20:30:44Z

Gamboviol:

'''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.

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>.
* 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.
* 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.

<references/>

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

Mrec

2013-12-08T20:27:03Z

Gamboviol:

'''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.

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.
* 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]]

Mrec

2013-12-08T19:39:23Z

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..."

'''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]]