SISA-Rec：一种具有对比对齐的语义集成序列推荐器
SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment
- DHA Suffa University（DHA 苏法大学）
- SZABIST
- National University of Computer and Emerging Sciences (FAST)（国立计算机与新兴科学大学（FAST））
- Applied Science Private University（应用科学私立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对基于Transformer的序列推荐未充分利用项目语义的问题，提出SISA-Rec框架，通过融合多种嵌入、注入语义相似性等方法构建用户表示，结合联合学习目标，实验表明该模型在稀疏数据集上性能优异，能有效解决冷启动问题。
AI中文摘要：
推荐系统帮助用户从大量选择中推荐相关项目。当前基于Transformer的序列推荐工作从交互日志中学习用户偏好，但大多关注项目标识符，未充分利用项目语义。在稀疏和冷启动场景中，这一限制成为重大挑战。为此引入SISA-Rec框架，将语义上下文直接嵌入序列建模。该方法通过门控融合模块融合项目ID嵌入与基于BERT的文本嵌入，将语义相似性注入自注意力机制，利用基于注意力的聚合模块构建综合用户表示。最后，结合贝叶斯个性化排序和对比对齐损失的联合学习目标对齐行为和语义空间。在两个高稀疏度数据集上实验，结果表明SISA-Rec在所有评估指标上优于现有基线模型，在冷启动分析中对交互历史记录有限的用户改进最大，证明将语义信息集成到注意力机制可带来更准确可靠的推荐。
英文摘要：
Recommendation systems help users recommend relevant items from a large collection of choices. Present work on transformer-based sequential recommendation learns user preferences from interaction logs, but it mostly focuses on item identifiers and doesn't fully use the semantic meaning of items. This limitation becomes a major challenge in sparse and cold-start scenarios where historical interaction data is limited. To solve this problem, we introduce SISA-Rec (Semantically Integrated Sequential Recommendation), a transformer-based framework that embeds semantic context directly into sequential modeling. Our approach fuses item ID embeddings with BERT-based text embeddings via a gated fusion module, injects semantic similarity into the self-attention mechanism, and leverages an attention-based aggregation module to construct comprehensive user representations. Finally, a joint learning objective which combines Bayesian Personalized Ranking (BPR) and contrastive alignment loss, aligns the underlying behavioral and semantic spaces. Experiments were conducted on the two highly sparse Amazon Beauty and Amazon Toys \& Games datasets, both having 99.93\% sparsity. The results show that SISA-Rec outperforms state-of-the-art baseline models across all evaluation metrics. Compared with the BERT4Rec \cite{petrov2022systematic}, SISA-Rec improves HR@10 by 16.6\% and NDCG@10 by 10.3\% on Amazon Beauty, and HR@10 by 23.1\% and NDCG@10 by 17.9\% on Amazon Toys \& Games. Cold-start analysis further shows that the proposed model achieves the largest improvements for users with limited interaction historical records. This showcases the value of semantic information when user behavior data is scarce. Overall, the results demonstrate that integrating semantic information into the attention mechanism leads to more accurate and reliable recommendations.
