Measuring Human-perceived Similarity in Heterogeneous Collections
This addresses the challenge of modeling subjective similarity in heterogeneous collections for applications like recommendation systems, though it appears incremental by building on prior techniques with added flexibility.
The paper tackles the problem of estimating human-perceived similarity between objects like movies or foods by combining a modest number of human assessments to infer a pairwise similarity function, capturing notions such as substitutability. The result is a method that accounts for varying user perceptions without assuming all similarity questions can be answered or all users perceive similarity uniformly.
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.