Analyzing law decision texts for personalized recommendation
Most of the people are interested in law only if they are confronted with it, as for example if the employer did not pay the extra hours. However there are no opportunities to find out what the consequences of this confrontation could be. Another fact is that not everybody has the chance to be informed by experts, as by a lawyer, nevertheless there are several platforms where the user can ask for help. The disadvantage of this approach is that the user will not get a hundred percent correct answer. Due to this problem we propose a personalized recommendation for a given users issue according the law. This is done by collecting the law decisions from RIS, the law information system of the Austrian government, and analyzing their texts. This approach allows to sum up all similar decisions together, so that a pool of corresponding decisions can be recommended to a given textual issue.