Personalize Learning to Rank Results through Reinforcement Learning

Learning to optimally rank and personalize search results is a difficult and important topic in scientific information retrieval as well as in online retail business, where we typically want to bias customer query results with respect to specific preferences for the purpose of increasing revenue. Reinforcement learning, as a generic-flexible learning model, is able to bias, e.g. personalize, learning-to-rank results at scale, so that externally specified goals, e.g. an increase in sales and probably revenue, can be achieved. This article introduces the topics learning-to-rank and reinforcement learning in a problem-specific way and is accompanied by the example project ‘cli-ranker’, a command line tool utilizing reinforcement learning principles for learning user information retrieval preferences regarding text document ranking.

Time as a Machine Learning Feature

Quite often it is the case that cyclic data is not sufficiently transformed for machine learning algorithms, e.g. feature representation is missing out on the implicit properties of cyclic features often resulting in wrong distance measures. This article introduces cyclic feature transformation for time based features as a mini-howto.

Log Server with Rsyslog, Influx, Telegraf and Grafana

Outside my professional life as a data scientist and software engineer at All.In Data, where we primarily focus on Microsoft Azure and Amazon Web Services cloud development, I am a convinced supporter of providing and hosting the services I use on my own systems.

Categorizing Deep Learning Approaches for Clustering 1

The following first article part is an attempt to categorize and structure current state of the art (SoA) clustering approaches, making use off of the work of Aljalbout et al. and Min et al. for my current research at TECO Institute from Karlsruhe Institute of Technology regarding missing data imputation in large stock quotation and trade data sets, where clustering would be an obvious first step for retrieving highly correlated quotations or trade movements.

Time Series - A Primer

A very quick primer for facilitating understanding and handling of time series and time series decomposition in pandas