A sentiment-guided session-aware recommender system

Abstract

Session-aware recommender systems analyze the sequential patterns of user actions to uncover the shifting preferences across sessions. User reviews enriched with sentiments can act as a guiding tool for session-aware systems. Existing methods for session-aware recommendations based on deep learning models do not consider the user’s sentiment granularity for generating reliable recommendations. In this paper, we have employed fuzzy-sentiment to guide the recommendation process toward a personalized and varied range of recommendations, resulting in an improved satisfaction level for the user. Fuzzy-sentiment provides a spectrum of sentiment scores (Highly positive, Positive, Neutral, Negative, and Highly Negative). This precise sentiment information allows the system to grasp the emotional tone and specific aspects of user experiences, shedding light on why users appreciated or were dissatisfied with a product. The sentiment scores are utilized to guide the recommendation process in the three-phase Sentiment-Guided Session-aware Recommender System, Fuzzy-SGSaRS. The first phase determines users’ sentiments from reviews about purchased products using the Fuzzy LSTM (FLSTM) technique. The learning process in the second phase employs a Graph Convolutional Network (GCN) to derive embeddings for Users, Interaction Sessions, and Products. The acquired embedding vectors are subsequently fed into the Double Deep Q-Network (DDQN) during the third phase to recommend intriguing products to the user(s). A series of experimental evaluations on four datasets of Amazon reviews illustrate that the proposed system outperformed various state-of-the-art methods.