SURE: Session-Based Uninteresting Item Removal for Enhanced Recommendations

Sequential recommendation systems are designed to predict user’ s subsequent actions and recommend relevant items, particularly within session-based interactions where user preferences can change rapidly. A significant challenge in these systems is dealing with short sequences of user interactions, which often lack sufficient data to capture user preferences, leading to imprecise recommendations. Although various research efforts have aimed to improve the handling of short sequences, the presence of uninteresting items within these sequences continues to hinder the accuracy of the recommendation system. To address this challenge, we propose a novel approach, Session-based Uninteresting Item Removal for Enhanced Recommendations (SURE), which integrates Short-Sequence Enhancement with the innovative removal of uninteresting items using association rule mining. Our method incorporates a backward prediction model that generates pseudo-historical items, enriching short sequences while preserving relevant information. The core novelty of our approach lies in the targeted removal of uninteresting items through association rule mining. By systematically identifying and filtering out items deemed uninteresting based on user preferences, we refine the sequence data to ensure that only relevant and engaging items remain. This refinement process enhances the overall quality and accuracy of the recommendations. Our approach significantly improves recommendation performance, achieving an average gain of 7.31% in mean reciprocal rank (MRR) and higher Normalised Discounted Cumulative Gain (nDCG) and accuracy scores across multiple real-world datasets compared to state-of-the-art techniques. These advancements highlight the model’ s ability to deliver more accurate, relevant, and interpretable recommendations, demonstrating its potential for real-world applications.