This chapter discusses content-based methods for reciprocal recommendation. The focus is on two representative case studies: RECON, which uses categorical data to make recommendations, and ImRec, which uses photo data. Both of these are introduced in the context of a wider discussion on data extraction and implementation of content-based filtering algorithms. We also discuss the advantages and disadvantages of these types of algorithms compared to collaborative filtering.