超越算法的推荐系统改进
Improving Recommender Systems Beyond the Algorithm
摘要：
推荐系统在很大程度上依赖于学习算法的预测精度。大多数提高精度的工作都集中在学习算法本身。我们认为，这种算法的重点是短视的。特别是，由于学习算法通常会随着更多更好的数据而改进，因此我们建议将反馈生成过程作为提高准确性的替代和补充途径。为此，我们探讨了用户界面的变化如何影响反馈数据的质量和数量，从而影响学习的准确性。基于信息觅食理论，我们从信息气味和信息获取成本两个维度研究了界面设计选择对反馈质量和数量的影响。我们提出了一个用户研究这些接口因素的共同任务，选择一部电影看，这些因素可以有效地塑造和改善隐式的反馈数据，同时保持用户体验。
英文摘要：
Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that this algorithmic focus is myopic. In particular, since learning algorithms generally improve with more and better data, we propose shaping the feedback generation process as an alternate and complementary route to improving accuracy. To this effect, we explore how changes to the user interface can impact the quality and quantity of feedback data -- and therefore the learning accuracy. Motivated by information foraging theory, we study how feedback quality and quantity are influenced by interface design choices along two axes: information scent and information access cost. We present a user study of these interface factors for the common task of picking a movie to watch, showing that these factors can effectively shape and improve the implicit feedback data that is generated while maintaining the user experience.
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