Validation of similarity and diversity metrics in a user study

24 July 2024

Idea

Similarity and diversity are essential ingredients of recommender systems. Based on such metrics, similar items may be recommended to users (“more of the same”). Or a portfolio might be diversified to prevent having an overly similar set of items.
Research has come up with a wide portfolio of similarity and diversity metrics that are used in so-called offline studies (purely computational). The goal of this project to validate existing metrics in a user study.

Relevant references:

Christoph Trattner & Dietmar Jannach (2020). Learning to recommend similar items from human judgments. User Modeling and User-Adapted Interaction, 30, 1–49. https://doi.org/10.1007/s11257-019-09245-4
Mathias Jesse, Christine Bauer, & Dietmar Jannach (2022). Intra-list similarity and human diversity perceptions of recommendations: the details matter. User Modeling and User-Adapted Interaction, 33(4), 769–802. https://doi.org/10.1007/s11257-022-09351-w

Relevant resources:

A valuable source for this project is the MovieLens dataset.