Deep Hierarchical Attention Based Approach for Multilingual Automatized Recommendation

In today’s technologically advanced society, online services are rapidly expanding, with a growing emphasis on customer satisfaction. To enhance the value of cloud services for users, it is essential to provide relevant and authentic recommendations. To address this requirement, our model DeepHaB-MMF integrates an automated recommendation system with advanced contextual and sequential embedding, designed to handle multilingual inputs. The first phase of our model is DeepHaB, which processes the user reviews by generating embedding that combine contextual information with extracted features. These embedding are then passed through a deep BiLSTM network to capture the bidirectional dependencies in the data. An attention mechanism further enhances the process by highlighting the most informative features which further help in classifying the truthful or fake reviews. These results are then provided with other extracted features to the next phase of the model, i.e., DeepMMF which modifies the traditional matrix factorization technique to provide relevant recommendations. Thus our model, DeepHaB-MMF first filters out the fake review and based on only truthful reviews it provides authentic as well as relevant recommendations. This model is evaluated on three low resource languages like Hindi, Marathi and Bengali and the results clearly shows that it out performs other state-of-the-art approaches.