Recommendation systems (RSs) aim to provide personalized content to users by analyzing their interactions with various items. Context-aware recommendation systems (CARSs) extend this by considering contextual factors such as time and location, which strongly influence user decisions. However, incorporating such information often introduces complexity and scalability challenges. In this paper, we propose CARA (Context-Aware Recommendation using Hierarchical Attention and Positional Encoding Based on Multiple Item Attributes), a novel model that integrates hierarchical attention with enhanced positional encoding to capture both item order and attribute-aware relationships. Unlike prior approaches, CARA jointly models shortterm and long-term preferences, producing more interpretable and context-sensitive recommendations.We evaluate CARA on two real-world benchmark datasets, MovieLens-100K and Yelp, and the results show consistent improvements. Specifically, CARAachieves improvements of 4%and 10% onMovieLens-100K, and6%and 15% onYelp, for Recall@20 andNDCG@20 respectively, compared to state-of-the-art baselines.
