[论文解读] A Context-Aware User-Item Representation Learning for Item Recommendation

Libing Wu, Cong Quan+3 more|arXiv (Cornell University)|Dec 6, 2017

Recommender Systems and Techniques参考文献 34被引用 7

一句话总结

该论文提出CARL，一种上下文感知的用户-物品表征学习模型，通过独立的基于评论的和基于交互的双分支学习组件，联合编码来自评论和评分交互的用户与物品特征。通过注意力机制与因子分解机建模动态特征交互，CARL在五个真实世界数据集上实现了最先进的评分预测性能，并通过注意力驱动的显著性分析提升了可解释性。

ABSTRACT

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate item. We argue that this static encoding scheme is difficult to fully capture the users' preference. In this paper, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. Namely, CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher-order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data respectively: review-based feature learning and interaction-based feature learning. In review-based learning component, with convolution operations and attention mechanism, the relevant features for a user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only review-driven and may not be comprehensive. Hence, interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on five real-world datasets show that CARL achieves significantly better rating prediction accuracy than existing state-of-the-art alternatives. Also, with attention mechanism, we show that the relevant information in reviews can be highlighted to interpret the rating prediction.

研究动机与目标

• 为解决现有推荐模型中静态、独立的用户与物品表征所导致的局限性，这些表征无法捕捉特定上下文下的偏好。
• 通过建模源自文本评论与用户-物品交互数据的动态、上下文感知的用户-物品特征交互，提升评分预测的准确性。
• 通过注意力机制突出相关评论特征，增强模型的可解释性。
• 在统一的、动态的表征框架中整合评论与评分得分的互补信息，以实现更好的泛化能力与性能。

提出的方法

• CARL采用两个并行的专用学习组件：基于CNN与注意力机制的评论特征学习模块，用于从用户与物品评论中提取上下文相关的特征。
• 交互特征学习模块独立地从用户-物品评分交互中提取潜在特征，捕捉用户特定偏好，而无需完全依赖评论内容。
• 通过一种动态线性融合机制将两个组件的输出进行融合，根据输入上下文自适应地加权各成分的贡献。
• 在用户-物品对的上下文感知联合表征上应用因子分解机，以建模高阶特征交互。
• 在评论处理流程中集成注意力机制，以突出语义相关的词语与短语，实现可解释的预测。
• 最终的评分预测作为上下文感知表征的可微函数计算得出，通过随机梯度下降端到端优化。

实验结果

研究问题

• RQ1 与静态、独立的表征相比，通过其潜在特征交互联合建模用户与物品表征，是否能提升评分预测的准确性？
• RQ2 注意力机制在多大程度上能有效突出相关评论内容，从而同时提升预测性能与模型可解释性？
• RQ3 结合基于评论与基于交互的特征学习组件，是否能优于仅使用任一数据源？
• RQ4 在稀疏或冷启动推荐场景下，多源特征（评论与评分）的动态融合在多大程度上提升了泛化能力？

主要发现

• CARL在五个真实世界数据集上显著优于现有最先进基线模型，展现出一致的性能提升。
• 注意力机制成功突出评论中语义相关的词语与短语，为预测决策提供了可解释的洞察。
• 基于交互的学习组件提供了超越基于评论特征的互补信息，提升了模型的鲁棒性与准确性。
• 动态线性融合机制有效平衡了评论与交互信号的贡献，优于固定融合或早期融合策略。
• 在上下文感知表征上集成因子分解机，有效建模了高阶特征交互，显著增强了预测能力。
• 消融实验确认，基于评论与基于交互的组件均不可或缺，各自对最终性能有独特贡献。