Added the capability of incremental SVD to python-recsys which is "Folding-in" new users (or items) to the SVD model so they can receive recommendations instantly without re-building the model from scratch each time new users come to the system. #31
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… and update each time
…ed ability to foldin or truncate new items not in the original svd model when originally trying to foldin new users and vice versa
… I added the ability to recommend items unrated by the user for folded-in users even if we loaded the SVD model instead of built it from scratch which wasn't previously allowed under original implementation
Ibrahim-AbouElseoud
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paper_ (Titled: Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems), this latest commit is simply an implementation to it for python-recsys... _
paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.7894&rep=rep1&type=pdfDemonstration video is available_ for this latest commit in form of a demo site built using the MEAN stack which uses the updated python-recsys as backend for the recommender which folds-in the website's user in to the SVD model and gets recommendations instantaneously instead of building the model from scratch... _
Demonstration video is available: https://youtu.be/tIvQxBfa2d4bachelor thesis paper_ (For those interested) which outlines the background, architecture and discusses the "Folding-in" approach... _
bachelor thesis paper: https://drive.google.com/file/d/0BylQe2cRVWE_RmZoUTJYSGZNaXM/view