Human cognitive biases are numerous and well established. Due
to inherent limitations in our knowledge of the world, and computational
constraints, our judgments and decisions do not rigidly
adhere to the principle of maximizing expected utility. We frequently
employ cognitive shortcuts, ignoring relevant information,
and make errors in how we store and retrieve items from memory.
Human decisions are additionally influenced by moral, emotional
and cultural parameters. People often perceive value in a way that is
very different from well-established decision-theoretic frameworks,
but much of the work on personalization does not capture human
cognitive biases. Our central hypothesis is that a new generation
of recommendation systems can be designed by explicitly modeling
human cognitive biases such as contrast, decoy, distinction,
and framing. We are just now beginning to see explicit non-linear
models of human risk perception being incorporated into machine
learning algorithms, and we believe this trend will accelerate in
the near future. In this paper we review today’s recommendation
systems, give an analysis of their limitations and make an argument
for why future recommendation systems should incorporate
explicit models of human cognitive bias.