  We introduce a novel domain-independent algorithm for generating interesting
item-to-item textual connections, or segues. Pivotal to our contribution is the
introduction of a scoring function for segues, based on their
"interestingness". We provide an implementation of our algorithm in the music
domain. We refer to our implementation as Dave. Dave is able to generate 1553
different types of segues, that can be broadly categorized as either
informative or funny. We evaluate Dave by comparing it against a curated source
of song-to-song segues, called The Chain. In the case of informative segues, we
find that Dave can produce segues of the same quality, if not better, than
those to be found in The Chain. And, we report positive correlation between the
values produced by our scoring function and human perceptions of segue quality.
The results highlight the validity of our method, and open future directions in
the application of segues to recommender systems research.
