Golovin, N. ; Rahm, E.

Reinforcement Learning Architecture for Web Recommendations

Proceedings of the ITCC 2004

2004 / 12


Futher information: https://dbs.uni-leipzig.de/files/Research/webusage-Dateien/ITCC2004_Golovin_Rahm.pdf


A large number of websites use online recommendations
to make web users interested in their products or
content. Since no single recommendation approach is
always best it is necessary to effectively combine different
recommendation algorithms. This paper describes the
architecture of a rule-based recommendation system
which combines recommendations from different algorithms
in a single recommendation database. Reinforcement
learning is applied to continuously evaluate the
users’ acceptance of presented recommendations and to
adapt the recommendations to reflect the users’ interests.
We describe the general architecture of the system, the
database structure, the learning algorithm and the test
setting for assessing the quality of the approach.