  Literature recommendation systems (LRS) assist readers in the discovery of
relevant content from the overwhelming amount of literature available. Despite
the widespread adoption of LRS, there is a lack of research on the
user-perceived recommendation characteristics for fundamentally different
approaches to content-based literature recommendation. To complement existing
quantitative studies on literature recommendation, we present qualitative study
results that report on users' perceptions for two contrasting recommendation
classes: (1) link-based recommendation represented by the Co-Citation Proximity
(CPA) approach, and (2) text-based recommendation represented by Lucene's
MoreLikeThis (MLT) algorithm. The empirical data analyzed in our study with
twenty users and a diverse set of 40 Wikipedia articles indicate a noticeable
difference between text- and link-based recommendation generation approaches
along several key dimensions. The text-based MLT method receives higher
satisfaction ratings in terms of user-perceived similarity of recommended
articles. In contrast, the CPA approach receives higher satisfaction scores in
terms of diversity and serendipity of recommendations. We conclude that users
of literature recommendation systems can benefit most from hybrid approaches
that combine both link- and text-based approaches, where the user's information
needs and preferences should control the weighting for the approaches used. The
optimal weighting of multiple approaches used in a hybrid recommendation system
is highly dependent on a user's shifting needs.
