This paper presents a novel approach to event recommender systems designed for adults with medium to high-functioning autism spectrum disorder (ASD) to facilitate social inclusion. Our system addresses a critical gap in existing recommender technologies by integrating three key dimensions: event preferences, sensory features based on the Sensory Perception Quotient, and personality traits based on the OCEAN model. Unlike conventional recommender systems that primarily focus on preference-based models, our approach acknowledges the impact of sensory sensitivities on decision-making for individuals with ASD. We introduce a weighted loss function with sigmoid transformation to integrate these multifaceted features, optimized through a preliminary analysis on synthetic data. This model is aimed at personalizing event recommendations considering interest alignment and sensory and personality compatibility for social comfort and engagement in the ASD population. Specifically, we plan to estimate the users’ ratings of group events based on the similarity between the events and user profiles. Preliminary results demonstrate the feasibility of our approach, with future work focused on real-world implementation, data collection, and user testing within the SPACES project.