Effectiveness of different recommender algorithms in the mobile internet: a case study
Kolja Hegelich, Dietmar Jannach · 2009
Despite the broad use of Recommender Systems (RS) technology in various domains, the number of publicly available reports on the actual business value of such systems is limited. This paper presents first results of an empirical evaluation of how different recommendation algorithms affect the navigation and buying behavior of a sample of over 155.000 different customers on a commercial Mobile Internet portal for cell phone games. The evaluated RS algorithms include itembased collaborative filtering, SlopeOne, a content-based as well as a hybrid technique, which were compared with naive approaches based on top-selling and top-rated items. The analysis shows that RS measurably affected the navigation and buying behavior of the portal visitors. The personalized recommendation lists not only attracted more clicks on