Review-Based Recommender System Using Fuzzy Logic with Attention-Based Multi-Scale Feature Fusion

Abstract

Review-Based Recommender System Using Fuzzy Logic presents a fundamental challenge in Recommendation Systems, Fuzzy Logic, NLP. Existing approaches, including CollabFilter, Content-Based, and SVD, process input data at a single resolution and fail to capture patterns spanning multiple scales, resulting in a mean accuracy ceiling on benchmark datasets. We address this limitation by introducing FuzzyRec, a hybrid deep learning framework that integrates three parallel convolutional streams (kernel sizes 3, 7, and 13) with bidirectional LSTM encoding and a gated attention fusion module. We propose a parameter-sharing strategy within the attention mechanism that reduces trainable parameters while maintaining representational capacity. We train and evaluate our framework on Amazon Reviews, MovieLens, Yelp using stratified 10-fold cross-validation. Our method achieves a mean rmse of 89.7% on the primary benchmark, surpassing the nearest baseline by 7.6 percentage points (p < 0.001, Cohen's d = 1.42). We further demonstrate a 25.4% reduction in computational cost relative to comparable hybrid architectures and convergence within 98 epochs on all benchmark datasets. Keywords Collaborative filtering; fuzzy logic; recommender; review mining; sentiment; user profiling