There is much work done on Recommender Systems, systems that automate the recommendation process; however there is little work done on explaining recommendations. The only study we know did an experiment measuring which explanation system increased user's acceptance of the item how much (promotion). We took a different approach and measured which explanation system estimated the true quality of the item the best so that the user can be satisfied with the selection in the end (satisfaction).

PDF, PS

unpublished. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.

@unpublished{bilgic:honors04, title={Explanation for Recommender Systems: Satisfaction vs. Promotion}, author={Mustafa Bilgic}, month={May}, address={Austin, TX}, institution={Department of Computer Sciences, University of Texas at Austin}, note={Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?bilgic:honors04", year={2004} }