Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration

Ming-Yen Lin, Ping-Chun Wu and Sue-Chen Hsueh ()
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Ming-Yen Lin: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 402, Taiwan
Ping-Chun Wu: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 402, Taiwan
Sue-Chen Hsueh: Department of Information Management, Chaoyang University of Technology, Taichung 413, Taiwan

Future Internet, 2024, vol. 16, issue 2, 1-15

Abstract: This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address the 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 assess our models’ performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-Pre Fix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec +st+TSA , respectively. On the Steam dataset, LCII-Post LP 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 potential 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 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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