How similar is rating similarity to content similarity?

The success of a recommendation algorithm is typically mea- sured by its ability to predict rating values of items. Al- Though accuracy in rating value prediction is an important property of a recommendation algorithm there are other properties of recommendation algorithms which are impor- Tant for user satisfaction. One such property is the diversity of recommendations. It has been recognized that being able to recommend a diverse set of items plays an important role in user satisfaction. One convenient approach for diversifi- cation is to use the rating patterns of items. However, in what sense the resulting lists will be diversified is not clear. In order to assess this we explore the relationship between rating similarity and content similarity of items. We discuss the experimental results and the possible implications of our findings.