Thought this was cool: That Netflix RMSE is way too low or is it ? ( Clustering-Based Matrix Factorization – implementation -)
Helping to evaluate my proposed extension on matrix factorization.
Hello eveyone,I have a new extension of matrix factorization named “Clustering-Based Matrix Factorization”. I apply it on many datasets including “Netflix”, “Movielens”, “Epinions”, “Flixter”, and it acheives very good results. For the last three data sets the RMSE result is good and realizable, but for Netflix dataset it acheives very interestng result. As we all know the RMSE result of the Netflix prize winner was 0.8567, now my method achieves the RMSE of 0.8122.I know that the Netflix prize winner’s method includes fusion of lots of different algorithm’s result, and it is hard to believe that one algorithm can reach such a good result. It has been my concern in the last couple of months too. Thus, I check my source code and my setup several time but cannot find any bug there. I also submit the paper in ICML but except a weak acceptation all other reviewers said that my method actually make sense but they all reject my work just because of the extraordinary result!That is why I decide to put the paper and my source code online that everyone can evaluate it. Now, I am going to ask you to kindly joining me to evaluate the paper and the source code more accurately. Lets say if my method works fine, it is going to be a new experience on recommendation systems and may show us that they are still opportunities to improve the RMSE results.Here is the paper’s link following by source code’s link:
source code: http://goo.gl/Az0lS
Thanks everyone in advance.
Recommender systems are emerging technologies that nowadays can be found in many applications such as Amazon, Netflix, and so on. These systems help users find relevant information, recommendations, and their preferred items. Matrix Factorization is a popular method in Recommendation Systems showing promising results in accuracy and complexity. In this paper we propose an extension of matrix factorization that uses the clustering paradigm to cluster similar users and items in several communities. We then establish their effects on the prediction model then. To the best of our knowledge, our proposed model outperforms all other published recommender methods in accuracy and complexity. For instance, our proposed method’s accuracy is 0.8122 on the Netflix dataset which is better than the Netflix prize winner’s accuracy of 0.8567.
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