Deep Learning–Based Web Mining to Detect Fake Reviews and Improve E-commerce Recommendations

This study presents a deep learning-based web mining framework to detect fake reviews and improve ecommerce recommendation quality by integrating textual, behavioral, user-item metadata, and temporal signals.Reviews are modeled as tuples 𝑢(𝑖, 𝑡, 𝑥, 𝑠, 𝑦) and transformed into structured feature vectors 𝜙(𝑟) that concatenate behavior/context features, user and item profiles, rating signals (including deviation from item mean), and time-based features extracted from crawled e-commerce pages and logs.A multimodal fake-review detector combines a neural text encoder (e.g., Transformer) with engineered web-mined features to estimate 𝑝(𝑓𝑎𝑘𝑒 | r) and derive a credibility score 𝑐 𝑟 = 1 -𝑝(𝑓𝑎𝑘𝑒) .This credibility is then used to down-weight suspicious reviews during review aggregation and recommendation learning, enabling a credibility-aware recommender that is more robust to spam and coordinated manipulation.The framework supports joint multi-task optimization of detection and recommendation objectives and evaluates performance using standard detection metrics (Precision/Recall/F1, ROC-AUC, PR-AUC) and ranking metrics (HR@K, NDCG@K).