Mihai Pitu created MAHOUT-1430:
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Summary: GSOC 2014 Proposal of implementing a new recommender
Key: MAHOUT-1430
URL: https://issues.apache.org/jira/browse/MAHOUT-1430
Project: Mahout
Issue Type: New Feature
Components: Collaborative Filtering
Affects Versions: 0.9
Reporter: Mihai Pitu
Fix For: 1.0
I would like to ask about possibilities of implementing Sparse Linear Methods
(SLIM) recommender in Mahout during GSOC 2014.
The SLIM algorithm generates efficient recommendations and its performance is
shown in the original paper
(http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf). The study
demonstrates that SLIM outperforms traditional algorithms (such as itemkNN,
userkNN, SVD or Matrix Factorization approaches) on various data-sets in terms
of run-time and recommendation quality. The algorithm can be paralellized and
Map-Reduce can help us achieve that.
I am aware of real world systems that are using SLIM as a recommendation engine
(e.g. Mendeley: http://www.slideshare.net/MarkLevy/efficient-slides) and I
think it represents the state-of-the-art in collaborative filtering right now.
Would this be an interesting addition to Mahout and is somebody interested in
mentoring this at Google Summer of Code 2014?
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