测量异构集合中的人类感知相似性
Measuring Human-perceived Similarity in Heterogeneous Collections
摘要：
我们提出了一种技术，用于估计对象之间的相似性，如电影或食物，其正确的表示取决于人类的感知。我们的技术结合了适度数量的人类相似性评估来推断对象之间的成对相似性函数。该相似性函数捕获了可能难以或不可能自动提取的一些人类相似性概念，例如当所需的电影不可用时，集合中的哪部电影将是更好的替代品。与现有技术相比，我们的方法不假设集合上的所有相似性问题都可以回答，或者所有用户都以相同的方式感知相似性。当与用户模型相结合时，我们发现每个评估员的品味如何变化，影响他们对相似性的感知。
英文摘要：
We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception. Our technique combines a modest number of human similarity assessments to infer a pairwise similarity function between the objects. This similarity function captures some human notion of similarity which may be difficult or impossible to automatically extract, such as which movie from a collection would be a better substitute when the desired one is unavailable. In contrast to prior techniques, our method does not assume that all similarity questions on the collection can be answered or that all users perceive similarity in the same way. When combined with a user model, we find how each assessor's tastes vary, affecting their perception of similarity.
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。
