论文标题
基于轨迹的播客推荐
Trajectory Based Podcast Recommendation
论文作者
论文摘要
播客推荐是一个越来越多的研究领域，提出了新的挑战和机遇。个人以与大多数其他媒体不同的方式与播客互动；我们关注的主要内容与音乐消费不同。我们表明，可以通过将用户依次移动播客库来获得成功且一致的建议。然后，使用从其顺序行为中获取的轨迹提出了未来播客的建议。我们的实验提供了证据表明用户行为仅限于本地趋势，并且倾向于在类似类型的表演类型的短序列中找到聆听模式。最终，我们的方法使协作过滤基线的有效性增加了A450％。
Podcast recommendation is a growing area of research that presents new challenges and opportunities. Individuals interact with podcasts in a way that is distinct from most other media; and primary to our concerns is distinct from music consumption. We show that successful and consistent recommendations can be made by viewing users as moving through the podcast library sequentially. Recommendations for future podcasts are then made using the trajectory taken from their sequential behavior. Our experiments provide evidence that user behavior is confined to local trends, and that listening patterns tend to be found over short sequences of similar types of shows. Ultimately, our approach gives a450%increase in effectiveness over a collaborative filtering baseline.
