The study introduces the PTSR framework to improve model transparency and recommendation performance. By extracting multi-level patterns and utilizing probabilistic embeddings, the model achieves superior results compared to existing methods. The analysis includes an ablation study, hyper-parameter impact assessment, and interpretability analysis.
The study focuses on enhancing the interpretability of sequential recommendation models through pattern-wise transparent decision-making processes. It introduces a novel framework named PTSR that breaks down item sequences into multi-level patterns for improved model transparency and recommendation performance. The research demonstrates the effectiveness of the proposed approach through experiments on various datasets.
The paper discusses the challenges in achieving both model transparency and recommendation performance simultaneously in sequential recommendation systems. It presents a novel interpretable framework called PTSR that quantifies the contribution of each pattern to the outcome in probability space. Extensive experiments validate the remarkable recommendation performance of PTSR while ensuring model transparency.
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by Kun Ma,Cong ... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.11480.pdfDeeper Inquiries