Fairness in recommender systems is an increasingly critical concern as these models mediate access to information, opportunities, and visibility. While collaborative filtering (CF) approaches have been extensively scrutinized for popularity bias and unfair exposure, the fairness properties of content-based recommendation (CBR) models remain underexplored. In this work, we present a comparative evaluation of CF and CBR models—introducing a modular, feature-fused content-based recommender (MultiFuseCB)—on MovieLens 1M and Amazon Beauty datasets. We systematically analyze how the selection and weighting of content features, as well as the choice of embedding models, affect both recommendation accuracy and fairness, using metrics such as item coverage and popularity bias. Our results show that CBR models, with appropriate feature engineering, can achieve competitive accuracy while substantially improving fairness relative to CF baselines. We further demonstrate that certain features (e.g., year, genre, plot) and embedding choices can be leveraged to promote more equitable item exposure. These findings provide actionable insights for designing fairer content-based recommenders and highlight the importance of feature selection and model tuning in achieving both accuracy and fairness.

This paper, in answering the question ”Can effi- cient on-device spectrum sensing be achieved on microcontrollers?”, presents a simple yet compre- hensive approach to signal classification using Con- volutional Neural Networks (CNNs) optimized for deployment on resource-constrained devices. Us- ing data generated via MATLAB’s Wireless Tool- box, as well real world data obtained from testbeds, we created a robust dataset of 9000 samples for training our model. The steps we took while de- veloping a CNN model that performs efficiently on microcontrollers include: data augmentation (pre- processing), model compression and quantization. The model significantly outperformed baseline ac- curacy metrics and maintained competitive infer- ence times, despite the hardware limitations of mi- crocontrollers. This reinforces the idea that Deep Learning has great potential in signal classification. Our research has the potential of being applied to smart homes, IoT networks, industrial automation, and public safety, where our optimized model facil- itates efficient spectrum utilization and minimizes interference.