Popular commercial on-line services such as Google, e-Bay, Amazon, and Netflix have evolved quickly over the last decade to help people find what they want, developing information retrieval strategies such as usefully ranked results, spelling correction, and recommender systems. Online library catalogs (OPACs), in contrast, have changed little and are notoriously difficult for patrons to use (University of California Libraries, 2005). Over the past year (June 2005 to the present), the Melvyl Recommender Project (California Digital Library, 2005) has been exploring methods and feasibility of closing the gap between features that library patrons want and have come to expect from information retrieval systems and what libraries are currently equipped to deliver.

The project team conducted exploratory work in five topic areas: relevance ranking, auto-correction, use of a text-based discovery system, user interface strategies, and recommending. This article focuses specifically on the recommending portion of the project and potential extensions to that work.