<div dir="ltr"><div><div>Hi all</div><div><br></div><div>I am a final year undergrad, pursuing my Bachelors in Engineering at NSIT, Delhi, majoring in Computer Engineering. My main area of research is social discovery in large scale graphs; I have worked on link prediction in social network graphs and I mostly use Python or C++ for implementations.</div>
<div><br></div><div>As a part of my undergraduate thesis on recommendation systems, I have been working on implementing matrix factorization models. I have already implemented the base model for matrix factorization using stochastic gradient descent in python as proposed in Yehuda Koren's paper [1]. It's quite slow, but it achieves an RMSE of 0.98 on the MovieLens dataset [5]. Besides the approaches mentioned in [1], my final thesis involves implementing Probabilistic Matrix Factorization [2], Bayesian Probabilistic Tensor factorization [3] and on distributed stochastic gradient descent for matrix factorization [4] (almost implemented).�</div>
<div><br></div><div>I am interested in developing the collaborative engine package for mlpack and I think quite a lot of my work on my thesis can be subsequently deployed as a part it. From my current vantage point, the collaborative engine package would have a group of such models, sample data for testing, and supporting functions for them like parameter selection, RMSE, plots for convergence rate, and comparing different models. I would like to discuss how you would ideally want me to proceed, and how you view the package as a whole.</div>
<div><br></div><div>I am also attaching the baseline code. Although it's in Python, the final work I am planning will have a similar structure. I would lke your views on the structure and quality of code.�</div><div><br>
</div><div>Thanks,</div><div>Sarthak Kukreti</div><div><br></div><div>[1] Yehuda Koren - Collaborative Filtering with Temporal Dynamics :�<a href="http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf">http://sydney.edu.au/engineering/it/~josiah/lemma/kdd-fp074-koren.pdf</a></div>
<div>[2] Ruslan Salakhutdinov - Probabilistic Matrix Factorization :�<a href="http://www.cs.utoronto.ca/~amnih/papers/pmf.pdf">http://www.cs.utoronto.ca/~amnih/papers/pmf.pdf</a></div><div>[3] Liang Xiong - Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization :�<a href="http://www.cs.cmu.edu/~xichen/images/Xi%20Chen%20SDM%202010.pdf">http://www.cs.cmu.edu/~xichen/images/Xi%20Chen%20SDM%202010.pdf</a></div>
<div>[4] Rainer Gemulla - Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent : �<a href="http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf">http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf</a></div>
</div><div style>[5] MovieLens 100k Dataset :�<a href="http://www.grouplens.org/node/73">http://www.grouplens.org/node/73</a></div>
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