Personalization Beyond Recommender Systems

Personalization is an interdisciplinary topic that has been discussed in the literature of marketing and information systems as well as in other research areas. In this paper we present findings from a longitudinal research project on personalization of e-commerce systems. The findings were taken from interviews and software development projects with company partners (action research). The main contribution described in this paper is the Personalization Map. The map provides an extensive overview on personalization functions that can be used to individualize and improve human-computer-interaction both in B2C and B2B e-commerce environments. In a first step, the functions are classified according to their order of appearance in the buying process. In a second step they are grouped into subcategories. There is no single strategy for selecting successful personalization functions as the suitability varies depending on the industry and the goods sold. Most definitions of personalization are closely connected to the recommendation of items based on user preferences. The Personalization Map shows that recommender systems are an interesting but rather small part of the universe of personalization functions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.