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IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Knowledge-Based Systems
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iTrust'05 Proceedings of the Third international conference on Trust Management
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Internet users often face the challenge of identifying the most suitable product out of some product assortment available on a certain e-sales platform. Recommender systems can substantially alleviate this typically complex task. Since the rise of such systems a lot of effort has been done in developing different recommendation approaches and algorithms, which all of them have certain strengths and weaknesses. What has been widely ignored by the recommender community so far are the potentials and impacts of psychological and decision theoretical phenomenons, which already have been investigated and applied in the field of marketing. Such phenomenons promise big capability to support users in decision making when facing a comparison situation. This paper concentrates on two classes of phenomenons, which are decoy effects and serial position effects. Tightly coupled to these phenomenons is the problem of getting the utility function of a recommender right, as this function serves both as the basis of result set calculation as well as the fundament of exploitation of above mentioned phenomenons. Putting all these aspects together an extended architecture for recommender systems will be proposed in the end of the paper.