Comparative analysis of similarity and diversity metrics for recommender systems

Supervisor: Christine Bauer
24 July 2024

Idea

Similarity and diversity are essential for recommender systems, as they help predict items a user may like. Research has come up with a broad portfolio of similarity and diversity metrics. The core of this project is to perform a comparative analysis of such metrics. The idea is to run computational experiments (offline study) and analyze how congruent (or different) the indications for similarity or diversity are for a set of metrics.

Relevant resources:

A valuable source for this project is the MovieLens dataset.

Comparative Study Validation Diversity Similarity Data Analysis Recommender Systems Metrics