PhD on preferences learning and recommendation under constraints

My work is based on the SLDD compilator SALADD by Nicolas Schmidt.

Here is the manuscript of my thesis.

Experiments reproduction

If you want to reproduce some experiments, you will need a JDK and ant. First get the source code and compile it :

$ git clone https://github.com/PFGimenez/PhD.git --depth 1 
$ cd PhD
$ ant

Then you can browse the experiments and select the experiment you wish to reproduce. Once you are in the correct directory, run the run.sh script by typing :

$ ./run.sh

The experiment reproduction should start. Please let me know if you encounter any issue.

AAAI-18

The two experiments are located in the directory experiments/AAAI-18.

Interactive recommender demonstrator

This demonstrator has been jointly developped with Louis Sablayrolles.

An interactive recommender demonstrator is available. It needs docker to run.

A docker image is available at https://hub.docker.com/r/pfgimenez/reco-demo/ ; to pull the image, type :

$ docker pull pfgimenez/reco-demo

Once the image is built, you can start the server :

$ sudo docker run -d --name instancereco -p 80:80 pfgimenez/reco-demo

To stop the server :

$ sudo docker stop instancereco && sudo docker rm instancereco

Some remarks :

• When the server is started, you can access it with a browser at the adress http://127.0.0.1
• Internet access isn't needed to run the demonstrator.
• Only one instance of the server can run at the same time.