Abstract
A recommender
system takes on-line user histories and demographic information, and
automatically creates personalized recommendations for goods and services.
Already common for small items such as books and movies, such systems are
destined to play an increasing role in the economy as new applications emerge
for them, such as firms searching for suppliers and workers searching for jobs.
Previous research, however, has shed little light on the economic benefits and
costs that recommender systems bring, nor on which of the many sorts of systems
work best in each current or prospective niche.
This project will
improve metrics for measuring performance of recommender systems, use these
metrics to evaluate existing recommender algorithms in major niches, and develop
and test new algorithms and systems adapted to under-served niches. The
research builds on previous research by computer scientists, and connects it to
established economic theory as well as to controlled experiments with human
subjects. The improved metrics explicitly incorporate the economic goals of
users, suppliers, and platform providers. The algorithms include collaborative
filtering, content based filtering, and hybrids as well as more advanced
algorithms that use heterogeneous information and actively trade off
exploration and exploitation while interacting with the users. The data will
include proprietary industry data already compiled for this project, together
with new data obtained in controlled laboratory experiments conducted at UCSC and
in on-line field experiments.
The direct impact
will be to increase understanding of the strengths and weaknesses of various
sorts of recommender systems. More broadly, the research will shed light on how
humans process freely available information, an important question in many
social science disciplines, and practical benefits may ultimately accrue to
millions of users of recommender systems. Additionally, conducting this
research will promote on-campus teaching, training and learning for graduate students
who serve as research assistants and programmers; for undergraduates (mostly
game design engineering majors) who contribute to the programming; and for
hundreds of undergraduates who will serve as laboratory subjects.