Hybrid LSTM-SVM with Aquila Optimization Based Optimal Feature Selection for Effective Recommender System
Nidhi Beniwal, Om Prakash VERMA · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2025
Recommender systems have become more crucial for informed service consumption, product selection, and decision-making in the era of overabundant information and the digitized economy. Session-based systems have emerged in recent years as a new paradigm for recommender systems. Although session-based recommenders have been extensively investigated, there are currently no unified issue statements for them nor detailed explanations of their characteristics and difficulties. In this paper, deep learning with optimal feature selection approaches are used for effective feature selection and classification. Different data related to product reviews and movie reviews are considered as input for this suggested approach. Initially, these datasets are given to the count vectorizer for converting the ”message” column's text into numbers. These converted raw data's are pre-processed utilizing similarity based data filling, min-max normalization and fuzzy c-means clustering to fill the absent values, standardization and to reduce the redundant data present in the dataset. Then, the features from pre-processed data are extracted. Aquila Optimization based approach is employed in the suggested method to decrease the number of resources required to describe a large set of data. Finally, a hybrid LSTM-SVM classifier is utilized for classification purpose. In this model, the softmax unit of the LSTM is substituted through SVM to predict the five different classes based on the customers reviews. The valuation outcomes shows that the suggested approach achieves 95%, 96%, 97% of accuracy, 90%, 91%, 93% of precision, 95%, 88%, 95% of specificity rate for three various datasets. As a result, the recommended strategy is the greatest option for a successful recommendation system.