Lin, M.-Y.; Wu, P.-C.; Hsueh, S.-C. Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration. Future Internet2024, 16, 51.
Lin, M.-Y.; Wu, P.-C.; Hsueh, S.-C. Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration. Future Internet 2024, 16, 51.
Lin, M.-Y.; Wu, P.-C.; Hsueh, S.-C. Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration. Future Internet2024, 16, 51.
Lin, M.-Y.; Wu, P.-C.; Hsueh, S.-C. Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration. Future Internet 2024, 16, 51.
Abstract
This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary ad-vancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post) with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to as-sess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their po-tential for both academic and practical applications in the field.
Keywords
recommender system; session-aware recommendation; latent-context information; long-term and short-term preference; gated recurrent unit
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
