An enhanced hybrid recommender system using an adaptive optimization approach

E-commerce platforms generate vast amounts of data, making it challenging for customers to find relevant products efficiently. Recommendation Systems (RS) address this issue by analysing user interactions to suggest personalized items. The two primary RS techniques are Content-Based Filtering (CBF) and Collaborative Filtering (CF). However, CF struggles with tracking changes in user preferences and can perform poorly in sparse datasets. To overcome these limitations, this study proposes a hybrid recommender system that integrates both CBF and CF approaches to enhance recommendation accuracy. The system follows a structured process starting with data collection and preprocessing, which includes data cleaning, discretization, and Principal Component Analysis (PCA) to reduce dimensionality. Feature extraction then captures both content and collaborative features, utilizing methods such as profile construction, content similarity indexing (using Jaccard similarity), neighbor finding (with an improved Manhattan distance refined by Euclidean distance), and item weight generation. For optimizing recommendation ratings, the system employs a novel Adaptive Red Deer Optimization combined with Dandelion Optimizer (ARD-DO). Evaluation metrics such as Accuracy, Precision, Recall, MCC, F-measure, RMSE, MAPE, MSE, and MAE were used to assess performance. Experiments on two datasets show that ARD-DO outperforms other algorithms, achieving minimal MAPE (0.0134 and 0.027186) and RMSE (2.9914 and 2.9225), along with high accuracy (0.9757 and 0.9879). Comparative algorithms like RDO, DLO, BOA, and GFO showed inferior performance, confirming the effectiveness of the proposed hybrid system.