Measuring Human-perceived Similarity in Heterogeneous Collections

TL;DR

This paper introduces a method to estimate human-perceived similarity in collections of objects like movies or foods, using limited human assessments to infer a nuanced similarity function that accounts for individual differences.

Contribution

It presents a novel technique that infers human-like similarity functions without assuming uniform perceptions or complete data, accommodating individual taste variations.

Findings

Effective in capturing human perception of similarity

Handles incomplete and subjective similarity data

Accounts for individual differences in taste

Abstract

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.