Generating Interesting Song-to-Song Segues With Dave
This addresses the problem of creating engaging item-to-item transitions for users in domains like music, though it appears incremental as it builds on existing segue concepts with a new scoring approach.
The authors tackled the problem of generating interesting textual connections between items by introducing a domain-independent algorithm with a scoring function for segue 'interestingness', implemented as Dave in the music domain. They found that Dave could produce 1553 different segue types, with informative segues matching or exceeding the quality of a curated source (The Chain), and reported positive correlation between their scoring function and human perceptions of quality.
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.