LSF-SR：基于流式条件变分自编码器的序列推荐潜在语义融合
LSF-SR: Latent Semantic Fusion for Sequential Recommendation via Flow-based Conditional Variational Autoencoders
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中文总结 AI 辅助
针对序列推荐中协同信号与语义知识难以对齐的问题，提出基于流式条件变分自编码器的LSF-SR框架，融合物品ID嵌入与LLM语义信号，在五个基准数据集上显著提升推荐性能。
中文摘要 AI 辅助
序列推荐旨在根据用户的历史交互预测其未来兴趣。尽管大型语言模型（LLMs）能够捕获丰富的物品语义，但现有方法往往难以将协同信号与文本语义知识对齐。因此，学习到的物品表示无法同时利用两种信号的互补优势，导致推荐质量欠佳。为解决这一局限，我们提出了基于流式条件变分自编码器的序列推荐潜在语义融合框架（LSF-SR），该框架利用带有归一化流的条件变分自编码器（CVAE）来融合物品ID嵌入和LLM生成的语义信号。LSF-SR的核心是一个由平面流或径向流增强的条件融合模块。该模块学习一个灵活的潜在空间，促使具有相似语义特征的物品在潜在流形内聚集。通过在五个公开基准数据集上进行的大量实验，我们证明LSF-SR持续优于最先进的基线方法，在Recall@20和NDCG@20上分别取得了高达12.98%和14.13%的提升。
英文摘要
Sequential recommendation aims to predict users' future interests from their historical interactions. Although Large Language Models (LLMs) capture rich item semantics, existing methods often struggle to align collaborative signals with textual semantic knowledge. As a result, the learned item representations fail to capture the complementary strengths of both signals, leading to suboptimal recommendation quality. To address this limitation, we propose Latent Semantic Fusion for Sequential Recommendation via Flow-based Conditional Variational Autoencoders (LSF-SR), a novel framework that uses a Conditional Variational Autoencoder (CVAE) with Normalizing Flows to fuse item ID embeddings and LLM-generated semantic signals. At the core of LSF-SR is a conditional fusion module augmented with planar or radial flows. This module learns a flexible latent space that encourages items with similar semantic profiles to cluster together within the latent manifold. Through extensive experiments on five public benchmark datasets, we demonstrate that LSF-SR consistently outperforms state-of-the-art baselines, achieving gains of up to 12.98% and 14.13% in Recall@20 and NDCG@20, respectively.
发表机构
- National Yang Ming Chiao Tung University（国立阳明交通大学）
- National Chung Hsing University（国立中兴大学）
机构由 AI 辅助整理，请以论文原文为准。
