Sequential Recommendation Based on Adaptive Bidirectional Information Flow

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Sequential Recommendation Based on Adaptive Bidirectional Information Flow

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[1]MA Li,LIU Wenzhe,LI Yuhao.Sequential Recommendation Based on Adaptive Bidirectional Information Flow[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-8 Column: Public date: 2026-09-10

Title:: Sequential Recommendation Based on Adaptive Bidirectional Information Flow

Author(s):: MA Li¹; 2 ; LIU Wenzhe¹ ; LI Yuhao¹; 1 . College of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China; 2. Intelligent Sensor Network Engineering Research Center of Hebei Province, Hebei GEO University, Shijiazhuang 052161, China

Keywords:: sequential recommendation; adaptive bidirectional information flow; dynamic preference modeling; frequency-domain filtering; attention mechanism

CLC:: TP391. 3TP183F724. 6

DOI:: 10.13705/j.issn.1671-6833.2026.04.003

Abstract:: Addressing the challenges of inefficient information fusion and noise interference in sequential recommendation, a novel method based on an adaptive bidirectional information flow was proposed. Built upon a dual-path encoder architecture, a hierarchical history summarization module was integrated to distill long-term user preferences, and dynamic frequency-domain filtering was introduced to suppress data noise. The approach fully considered the dependency and interactivity between past and future information by employing an adaptive bidirectional information flow mechanism. This mechanism dynamically adjusts fusion weights via uncertainty perception, enabling a precise characterization of the evolution of user preferences. To validate its effectiveness, experiments were conducted on four public datasets including Beauty, Sports, Yelp, and ML1M, and a comparative analysis was performed against 10 mainstream methods. The experimental results demonstrated that the proposed method outperformed the baseline models in three key metrics: NDCG, HR, and MRR. Compared to the three leading baseline models of FMLP-Rec, DualRec, and OracleRec, the proposed method’s HR@20 reached 0.652 0 and 0.913 3 on the Beauty and Yelp datasets, which was 2.08 percentage points and 2.89 percentage points higher than their average performance, respectively. Furthermore, its NDCG@20 on the Beauty and Yelp datasets reached 0.394 4 and 0.564 5, outperforming the average of the three baselines by 2.67 percentage points and 2.72 percentage points, respectively.

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Last Update: 2026-02-27