  There are two principal data sources for collaborative filtering recommenders
in scholarly digital libraries: usage data obtained from harvesting a large,
distributed collection of Open URL web logs and citation data obtained from the
journal articles. This study explores the characteristics of recommendations
generated by implementations of these two methods: the 'bX' system by ExLibris
and an experimental citation-based recommender, Sarkanto. Recommendations from
each system were compared according to their semantic similarity to the seed
article that was used to generate them. Since the full text of the articles was
not available for all the recommendations in both systems, the semantic
similarity between the seed article and the recommended articles was deemed to
be the semantic distance between the journals in which the articles were
published. The semantic distance between journals was computed from the
"semantic vectors" distance between all the terms in the full-text of the
available articles in that journal and this study shows that citation-based
recommendations are more semantically diverse than usage-based ones. These
recommenders are complementary since most of the time, when one recommender
produces recommendations the other does not.
