Understanding Distribution Structure on Calibrated Recommendation Systems

• URL: http://arxiv.org/abs/2508.13568v1
• Date: Tue, 19 Aug 2025 06:59:10 GMT
• Title: Understanding Distribution Structure on Calibrated Recommendation Systems
• Authors: Diego Correa da Silva, Denis Robson Dantas Boaventura, Mayki dos Santos Oliveira, Eduardo Ferreira da Silva, Joel Machado Pires, Frederico Araújo Durão,
• Abstract summary: Traditional recommender systems omit genres from less prominent areas of a user's profile.<n>We develop a calibrated recommendation system that includes less representative areas in the recommended list.<n>We evaluate the users' patterns in three datasets from the movie domain.
• Score: 2.6718643310547607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional recommender systems aim to generate a recommendation list comprising the most relevant or similar items to the user's profile. These approaches can create recommendation lists that omit item genres from the less prominent areas of a user's profile, thereby undermining the user's experience. To solve this problem, the calibrated recommendation system provides a guarantee of including less representative areas in the recommended list. The calibrated context works with three distributions. The first is from the user's profile, the second is from the candidate items, and the last is from the recommendation list. These distributions are G-dimensional, where G is the total number of genres in the system. This high dimensionality requires a different evaluation method, considering that traditional recommenders operate in a one-dimensional data space. In this sense, we implement fifteen models that help to understand how these distributions are structured. We evaluate the users' patterns in three datasets from the movie domain. The results indicate that the models of outlier detection provide a better understanding of the structures. The calibrated system creates recommendation lists that act similarly to traditional recommendation lists, allowing users to change their groups of preferences to the same degree.

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