Flexible Recommendations over Rich Data

Abstract

CourseRank is a course planning tool aimed at helping students at Stanford. Recommendations comprise an integral part of the system. However, implementing existing recommendation methods leads to fixed, pre-specified recommendations that cannot adapt to each particular student's changing requirements and do not help exploit the full extent of the available learning opportunities at the university. In this paper, we describe the concept of a flexible recommendation workflow, i.e., a high-level description of a parameterized process for computing recommendations. The input parameters of a flexible recommendation process comprise the 'knobs' that control the final output and hence support flexible recommendations. We describe how flexible recommendations can be expressed over a relational database and we present our prototype system that allows defining and executing different, fully-parameterized, recommendation workflows over relational data. Finally, we describe a user interface in CourseRank that allows students to make use of two flexible recommendation workflows.