At Flickr, we understand that the value in our image corpus is only unlocked when our members can find photos and photographers that inspire them, so we strive to enable the discovery and appreciation of new photos. To further that effort, today we are introducing similarity search on Flickr. If you hover over a photo on a search result page, you will reveal a “…” button that exposes a menu that g

Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity,

Time series often contain noise, redundancies or irrelevant information. As a result most of the extracted features will not be useful for the machine learning task at hand. To avoid extracting irrelevant features, the TSFRESH package has a built-in filtering procedure. This filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classificatio

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization a

PSCMModel is a small set of Python scripts for the user click models based on Yandex version (https://github.com/varepsilon/clickmodels). A Click Model is a probabilistic graphical model used to predict search engine click data from past observations. This project is aimed to implement recently proposed click models and intended to be easy-to-read and easy-to-modify. If it's not, please let me kno

SIGMOD 2015 TUTORIAL Mining and Forecasting of Big Time-series Data Yasushi Sakurai, Yasuko Matsubara (Kumamoto U) and Christos Faloutsos (CMU/SCS) Description Description (pdf): [PDF] Abstract: Given a large collection of time series, such as web-click logs, electric medical records and motion capture sensors, how can we efficiently and effectively find typical patterns? How can we statistically