arXiv subjects

Frederico Araújo Durão

Publications and source records attributed to Frederico Araújo Durão.

3 recordsLinked to original sources

arXiv2026

How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations

Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information. However, the actual contribution of review-derived representations remains an open question, particularly when strong collaborative baselines are employed. In this work, we systematically investigate the impact of textual information on Matrix Factorization by introducing and comparing three enrichment strategies over a common collaborative backbone. First, we propose a learnable gating mechanism that adaptively balances collaborative and textual signals during training. This mechanism is applied to two distinct review representations: (i) aggregated topic profiles extracted from user and item histories, and (ii) full text embedding representations derived from reviews. Additionally, we explore a cross-attention mechanism that identifies and emphasizes the most informative dimensions of the textual representation before fusion with collaborative factors. We evaluate six variants: pure, enriched with topic profiles and text via gating; enriched with topics and text via gating; and enhanced with cross-attention over textual features. Experiments across multiple review-based datasets reveal that although adaptive fusion mechanisms improve representation flexibility, the marginal contribution of textual signals remains limited compared to the collaborative backbone. These findings suggest that, under typical rating-prediction settings, collaborative information continues to dominate performance, raising important considerations for the effective integration of semantic review signals into recommendation models.

cs.IR↗

arXiv2025

Understanding Distribution Structure on Calibrated Recommendation Systems

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.

cs.IR↗

arXiv2022

Introducing a Framework and a Decision Protocol to Calibrate Recommender Systems

Recommender Systems use the user's profile to generate a recommendation list with unknown items to a target user. Although the primary goal of traditional recommendation systems is to deliver the most relevant items, such an effort unintentionally can cause collateral effects including low diversity and unbalanced genres or categories, benefiting particular groups of categories. This paper proposes an approach to create recommendation lists with a calibrated balance of genres, avoiding disproportion between the user's profile interests and the recommendation list. The calibrated recommendations consider concomitantly the relevance and the divergence between the genres distributions extracted from the user's preference and the recommendation list. The main claim is that calibration can contribute positively to generate fairer recommendations. In particular, we propose a new trade-off equation, which considers the users' bias to provide a recommendation list that seeks for the users' tendencies. Moreover, we propose a conceptual framework and a decision protocol to generate more than one thousand combinations of calibrated systems in order to find the best combination. We compare our approach against state-of-the-art approaches using multiple domain datasets, which are analyzed by rank and calibration metrics. The results indicate that the trade-off, which considers the users' bias, produces positive effects on the precision and to the fairness, thus generating recommendation lists that respect the genre distribution and, through the decision protocol, we also found the best system for each dataset.

cs.IR↗