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If you publish research that uses RankSys, please cite the papers listed here that best match the parts of the framework that you used. RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications (see here) and a PhD thesis. While it is envisioned as
Ming-Hen Tsai Introduction There are many tedious data processing techinques needed to be conducted before training in many machine learning problems. The project written in C aims to include some of state-of-the-art feature extraction methods to ease the pain to analysis and generate useful data. A discussion forum of the software can be found HERE. News Using lib-gundam to mine the features, we
Count-Min sketch is a great algorithm, but it has a tendency to overestimate low frequency items when dealing with highly skewed data, at least it’s the case on zipfian data. Amit Goyal had some nice ideas to work around this, but I’m not that fund of the whole scheme he sets up to reduce the frequency of the rarest cells. I’m currently thinking about a whole new and radical way to deal with text
Non-Metric Space Library (NMSLIB) is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The core-library does not have any third-party dependencies. It has been gaining popularity recently. In particular, it has become a part of Amazon Elasticsearch Service. The goal of the project is to create an effective and comprehensive toolkit for
What I Learned From The Kaggle Criteo Data Science OdysseyEach Kaggle challenge is like an odyssey : you start with nothing, you don’t know how it will end, and when it’s finished, it reminds you good old memories. The goal of the Criteo challenge was to predict if display ads will be clicked, based on traffic logs. Criteo is one of the french company that has the most challenging real-time data p
We are often interested in finding users, hashtags and ads that are very similar to one another, so they may be recommended and shown to users and advertisers. To do this, we must consider many pairs of items, and evaluate how “similar” they are to one another. We call this the “all-pairs similarity” problem, sometimes known as a “similarity join.” We have developed a new efficient algorithm to so
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This summer, I’m interning at Spotify in New York City, where I’m working on content-based music recommendation using convolutional neural networks. In this post, I’ll explain my approach and show some preliminary results. Overview This is going to be a long post, so here’s an overview of the different sections. If you want to skip ahead, just click the section title to go there. Collaborative fil
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