与Dave一起生成有趣的Song-to-Song Segues
Generating Interesting Song-to-Song Segues With Dave
摘要：
我们介绍了一种新的域独立算法生成有趣的项目到项目的文本连接，或segues。首先，我们的贡献是介绍了一个评分功能的segues，根据他们的“兴趣”。我们提供了我们的算法在音乐领域的实现。我们将我们的实现称为Dave。戴夫能够生成1553种不同类型的接续，可以大致分为信息性或有趣。我们评估戴夫通过比较它与策划来源的歌曲到歌曲的segues，所谓的链。在信息片段的情况下，我们发现戴夫可以产生相同质量的片段，如果不是更好的话，比那些在链中找到的片段。而且，我们报告的值之间的正相关性，我们的评分功能和人类的感知segue质量。结果突出了我们的方法的有效性，并开辟了未来的方向，在应用segues推荐系统的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
