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.