Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation
Proposes HAF-VT, a multimodal hierarchical attention model, boosting cross-domain sequential recommendation by 3-5% on key metrics.
Key Findings
Methodology
This paper introduces HAF-VT, a cognitive-inspired framework integrating visual and textual embeddings via a frozen CLIP model. It combines these multimodal features with learnable item ID embeddings, processed through hierarchical attention layers that mimic human information integration. The model captures intra- and inter-sequence relationships, using cosine similarity for next-item prediction. Multi-task loss functions optimize the joint learning of domain-specific and cross-domain preferences, enabling robust cross-domain recommendations. The architecture emphasizes layered attention to simulate human cognitive processes, effectively modeling complex user behaviors across multiple domains.
Key Results
- On four e-commerce datasets, HAF-VT outperforms state-of-the-art baselines like Tri-CDR and MIFN, with improvements of 3-5% in MRR and NDCG. For example, in the Movie-Book scenario, MRR reached 6.27, significantly higher than previous models. The model demonstrates superior ability to capture cross-domain user preferences, especially in sparse and cold-start conditions.
- In ablation studies, removing multimodal features or hierarchical attention caused performance drops of around 0.5-1.0 points, confirming their critical roles. The integration of visual and textual modalities notably enhances the model's understanding of user preferences, leading to better recommendation accuracy.
- The experiments validate that combining cognitive principles with deep multimodal learning yields substantial benefits, setting new benchmarks in cross-domain sequential recommendation tasks.
Significance
This work bridges cognitive science and recommendation algorithms, leveraging multimodal data to emulate human preference formation. It addresses longstanding issues of data sparsity and domain bias, offering a scalable, interpretable framework that enhances recommendation quality. The integration of layered attention and multimodal embeddings opens new avenues for personalized AI systems, with potential applications in e-commerce, entertainment, and beyond. It also advances theoretical understanding by modeling human-like information processing within AI architectures.
Technical Contribution
The paper introduces a novel multimodal fusion framework based on CLIP embeddings and hierarchical attention, enabling layered modeling of user preferences. It innovates by simulating cognitive information integration, combining multiple feature sources at different levels, and optimizing via multi-task loss. The architecture supports scalable, interpretable, and highly effective cross-domain recommendations, providing a new paradigm for multimodal AI in recommendation systems.
Novelty
This is the first work to integrate CLIP-based visual and textual embeddings with a hierarchical attention mechanism specifically for cross-domain sequential recommendation. Unlike prior models that rely on shallow feature fusion, this approach models layered cognitive-like information processing, capturing complex intra- and inter-sequence relationships. It bridges the gap between multimodal learning and cognitive-inspired modeling, setting a new standard in the field.
Limitations
- The reliance on pre-trained CLIP models incurs high computational costs, limiting real-time deployment in resource-constrained environments.
- In extremely cold-start or new item scenarios, the model's performance may decline due to insufficient multimodal data quality or coverage.
- The effectiveness heavily depends on the quality of visual and textual data; noisy or poor-quality inputs can impair recommendation accuracy.
Future Work
Future research will explore dynamic updating of multimodal features based on user feedback, enhancing adaptability. Incorporating external knowledge graphs or user profiles could further improve cold-start performance. Additionally, integrating reinforcement learning could optimize long-term user engagement, making recommendations more personalized and context-aware.
AI Executive Summary
In the rapidly expanding landscape of e-commerce and digital content, understanding user preferences across multiple domains remains a core challenge. Traditional sequential recommendation models primarily focus on temporal patterns within single domains, often neglecting the rich, multimodal nature of real-world data. Recognizing that human decision-making involves integrating visual cues, textual descriptions, and contextual information, this research introduces HAF-VT, a novel framework inspired by cognitive science principles.
HAF-VT leverages the power of the pre-trained CLIP model to extract aligned visual and textual embeddings, enriching item representations beyond simple IDs. These multimodal features are fused through a hierarchical attention mechanism that mimics human information processing, capturing both local sequence dependencies and global cross-domain relationships. This layered approach allows the model to weigh different sources of information dynamically, leading to more accurate and personalized recommendations.
Extensive experiments conducted on four large-scale e-commerce datasets demonstrate that HAF-VT surpasses existing state-of-the-art methods by 3-5% in key metrics such as MRR and NDCG. The model's ability to effectively integrate multimodal data and simulate human-like cognition results in significant improvements, especially in sparse and cold-start scenarios. Ablation studies confirm that both the multimodal fusion and hierarchical attention components are critical for these gains.
This work not only advances the technical frontier of recommendation systems but also offers a compelling example of how cognitive principles can inform AI design. By bridging the gap between human perception and machine learning, HAF-VT paves the way for more intelligent, interpretable, and human-centric AI applications. Despite current limitations related to computational costs and data quality, future directions include dynamic feature updating and multi-task learning, promising even more robust and adaptive recommendation solutions.
Deep Analysis
Background
推荐系统经历了从传统协同过滤到深度学习的演变，序列推荐成为捕获用户动态偏好的核心技术。代表性模型如GRU4Rec、SASRec利用时间序列信息进行预测，但在跨域场景中表现有限。近年来，跨域推荐方法如π-Net、MIFN引入知识图谱和迁移学习，缓解数据稀疏问题。多模态学习逐步应用于推荐，结合图像、文本等丰富信息，提升模型表达能力。认知科学研究表明，人类偏好形成涉及多模态信息的整合，启发了多模态推荐的研究方向。
Core Problem
现有跨域序列推荐多关注序列时间关系，忽视了多模态信息的潜在价值，导致在复杂场景下效果有限。尤其在数据稀疏或冷启动时，模型难以捕获用户真实偏好。传统模型缺乏模拟人类认知中信息整合的能力，无法充分利用视觉和文本等多源信息，限制了推荐的准确性和鲁棒性。
Innovation
本文提出结合预训练CLIP模型的多模态特征融合框架，创新性引入层次注意力机制，模拟人类认知中的信息整合。具体包括：1）多模态特征提取：利用CLIP模型同时获得图像和文本嵌入，丰富物品表示；2）层次注意力机制：捕获序列内外关系，平衡单域与跨域偏好；3）多模态融合：结合ID、视觉、文本特征，提升模型表达能力。这些创新突破了传统单一特征或浅层融合的局限，为跨域推荐提供新思路。
Methodology
- �� 物品特征准备：利用ID嵌入矩阵Eid、预训练CLIP模型提取图像Eimg和文本Etex嵌入。
- �� 序列多模态嵌入：根据用户行为序列，生成ID、图像、文本的序列嵌入Fid、Fimg、Ftex。
- �� 层次注意力捕获关系：通过多层注意力机制，分别在序列内部和跨域间学习关系，生成注意力聚合表示H。
- �� 相似度预测：将序列表示与物品嵌入通过余弦相似度计算，预测下一项。
- �� 损失优化：采用多任务损失函数，融合多域信息，训练端到端模型。
Experiments
在四个电商数据集（如Amazon）上，构建跨域推荐场景，采用MRR和NDCG指标评估。设置嵌入维度q=256，图像文本嵌入e=512，批次大小256，训练100轮，使用Adam优化。与SOTA模型（如Tri-CDR、MIFN）对比，验证模型优越性。进行消融实验，验证多模态和层次注意力的重要性。
Plain Language Accessible to non-experts
想象你在一家厨房里做饭，食材代表不同信息：有颜色鲜艳的蔬菜（视觉信息），有香味浓郁的调料（文本信息），还有各种食谱（ID）。以前，只用一种食材做菜，味道有限。现在，你结合多种食材，按照不同的顺序搭配，能做出更美味的菜肴。这个模型就像厨师一样，把不同的食材（多模态信息）融合在一起，按照一定的层次（层次注意力）考虑每个食材的重要性，最终做出最符合用户口味的推荐。它学习如何像人一样，结合多方面信息，做出更聪明的选择。
ELI14 Explained like you're 14
你知道吗？我们平时买东西时，不仅看商品图片，还会读商品介绍，甚至记住商品的品牌和价格。这就像是在用不同的感官（看、闻、记忆）一起判断一个东西是不是喜欢的。这个模型也是一样，它用电脑学会结合图片和文字信息，就像我们用眼睛和耳朵一起了解一个商品。它还会像人脑一样，把这些信息按重要性排序，最后告诉你哪个商品最适合你。这样一来，推荐的东西就更贴合你的喜好，就像朋友帮你挑东西一样聪明。
Glossary
Multimodal Learning (多模态学习)
利用多源信息（如图像、文本）共同训练模型，增强理解和表达能力。技术上结合不同模态的特征表示与融合机制。
本文中通过CLIP模型提取图像和文本特征，融合多模态信息以提升推荐效果。
Hierarchical Attention (层次注意力)
多层次的注意力机制，用于模拟人类认知中的信息逐层整合，捕获序列内外关系。
模型中用以学习序列内部和跨域偏好关系。
CLIP模型 (Contrastive Language-Image Pretraining)
由OpenAI提出的预训练模型，能同时生成图像和文本的对齐嵌入，支持多模态理解。
用于提取物品的视觉和文本特征，丰富物品表示。
Cross-Domain Recommendation (跨域推荐)
利用多个相关领域数据，改善单一领域中的推荐效果，缓解数据稀疏和冷启动问题。
本文通过跨域序列融合提升推荐准确性。
Open Questions Unanswered questions from this research
- 1 多模态特征的动态更新机制仍未充分研究，如何实时调整多模态特征以适应用户偏好变化是未来方向。
- 2 模型在极端冷启动或新物品场景下表现仍有限，需结合外部知识图谱或用户画像增强性能。
Abstract
Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences through intra- and inter-sequence item relationships. Inspired by human cognitive processes, we propose Hierarchical Attention Fusion of Visual and Textual Representations (HAF-VT), a novel approach integrating visual and textual data to enhance cognitive modeling. Using the frozen CLIP model, we generate image and text embeddings, enriching item representations with multimodal data. A hierarchical attention mechanism jointly learns single-domain and cross-domain preferences, mimicking human information integration. Evaluated on four e-commerce datasets, HAF-VT outperforms existing methods in capturing cross-domain user interests, bridging cognitive principles with computational models and highlighting the role of multimodal data in sequential decision-making.