Adaptive multilingual app recommendation using user behavior and contextual learning models

There is a need to adopt context-aware and behavior-driven scalable recommendation systems due to rapid development of multilingual mobile applications. Collaborative and content-based methods face challenges in adapting linguistic and contextual models. In order to mitigate this, we propose a Behaviour Driven Multilingual App Recommendation System (BD-MARS) which integrates behaviour modelling, multi-lingual semantic representation, probabilistic inference and reinforcement based adaptation. The experimental data used was obtained from public repositories and included app metadata, multilingual user reviews, and structured behavioral interaction logs belonging to various categories such as Health & Fitness and productivity. In BD-MARS, rank-adjusted TF-IDF is normalized for stabilizing feature weighing. A semantic space is built with cross-lingual embeddings, and the reviews’ contextual dependencies are identified via GRU-based sequential modeling. Further, by means of the Recurrent Neural Networks (RNNs), we can refine the ranking based on state and action as rewards. Results of the evaluation of the experiment show better ranking performance with 85.3% precision, 78.2% recall, 81.5% F1-score, 0.85 MRR, 0.92 NDCG, 0.86 MAP, and 90.2% coverage. The distributed benchmarking of Apache Spark validates scalability and computing efficiency while transforming the nature of the framework to enable large-scale multilingual deployment.