LCS in context of ML- Martin Mihál

Goal of project

Goal of the project is show possibilities of LCS algorithm in non-robotic enviroment - in area of - we can say - "pure" machine learning. I'll use data of activity of user of given website(http://www.publico.es/) from Piano(http://piano.io/) system and the goal of whole process will be define set of rules which best define users which will come back to website - that means they are "loyal" in some way and we can try to target them with locking of some content and deliver them offer. On the other site, users which are not loyal yet(they won't probablycoma back) - we want to give them freedom in browsing of given website and and develop interest or "addiction" and give them offer later.

Overview

I'll use data of activity of user from period(in this experiment month) A and we want to predict if user will have any interaction in following period B. Between period A and B are some days because we want to track "long-term" loyality no "from-day-to-day" loyality.

Preparing of data

In the process of preparing data are important to take in consideration 2 factors:

1. Understanding of data we have and problem we want solve
2. Type of data which used algorithm need

As a target value I will use number of pageviews(PV) in period B.

For describing users I'm gonna use following data from period A(all data are in binar value (1/0) because that's format of data which LCS need, except number of PV in period A which I will use in different way). All selected variables has background in data - for example we want track if people from different cities/countries has different properties, we check also how many days there is between first and last PV in period A, if they visit particular sections etc. :

I'll use just random sample of users(~10%) because of extremely big number of user on mentioned website.

Data selection

TODO

LCS algorithm

TODO

Results

TODO