On Interpretation and Measurement of Soft Attributes for Recommendation

TL;DR

This paper explores how to interpret and measure soft, subjective attributes in recommender systems using personalized relative statements, novel data collection, and scoring methods, advancing understanding of user critiques.

Contribution

Introduces a new framework for representing soft attributes as personalized relative statements, along with novel data collection techniques, evaluation methods, and scoring approaches.

Findings

Developed a new public dataset for soft attribute analysis.

Proposed multiple scoring methods from unsupervised to supervised.

Demonstrated improved interpretation of subjective user critiques.

Abstract

We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.