  We address the problem of recommending relevant items to a user in order to
"complete" a partial set of items already known. We consider the two scenarios
of citation and subject label recommendation, which resemble different
semantics of item co-occurrence: relatedness for co-citations and diversity for
subject labels. We assess the influence of the completeness of an already known
partial item set on the recommender performance. We also investigate data
sparsity through a pruning parameter and the influence of using additional
metadata. As recommender models, we focus on different autoencoders, which are
particularly suited for reconstructing missing items in a set. We extend
autoencoders to exploit a multi-modal input of text and structured data. Our
experiments on six real-world datasets show that supplying the partial item set
as input is helpful when item co-occurrence resembles relatedness, while
metadata are effective when co-occurrence implies diversity. This outcome means
that the semantics of item co-occurrence is an important factor. The simple
item co-occurrence model is a strong baseline for citation recommendation.
However, autoencoders have the advantage to enable exploiting additional
metadata besides the partial item set as input and achieve comparable
performance. For the subject label recommendation task, the title is the most
important attribute. Adding more input modalities sometimes even harms the
result. In conclusion, it is crucial to consider the semantics of the item
co-occurrence for the choice of an appropriate recommendation model and
carefully decide which metadata to exploit.
