[论文解读] A Capsule Network for Recommendation and Explaining What You Like and Dislike

Chenliang Li, Cong Quan+4 more|arXiv (Cornell University)|Jul 1, 2019

Recommender Systems and Techniques参考文献 37被引用 6

一句话总结

该论文提出CARP，一种基于胶囊网络的模型，通过结合用户观点与商品属性的逻辑单元，在细粒度层面建模用户-物品交互，从而提升推荐准确率与可解释性。该模型采用新颖的双一致路由机制（Routing by Bi-Agreement），识别这些逻辑单元的情感感知表征，在七个真实世界数据集上实现最先进性能，同时为用户偏好提供可操作、可解释的洞察。

ABSTRACT

User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for recommendation. The attention mechanism is mainly adopted in these works to identify words or aspects that are important for rating prediction. However, it is still hard to understand whether a user likes or dislikes an aspect of an item according to what viewpoint the user holds and to what extent, without examining the review details. Here, we consider a pair of a viewpoint held by a user and an aspect of an item as a logic unit. Reasoning a rating behavior by discovering the informative logic units from the reviews and resolving their corresponding sentiments could enable a better rating prediction with explanation. To this end, in this paper, we propose a capsule network based model for rating prediction with user reviews, named CARP. For each user-item pair, CARP is devised to extract the informative logic units from the reviews and infer their corresponding sentiments. The model firstly extracts the viewpoints and aspects from the user and item review documents respectively. Then we derive the representation of each logic unit based on its constituent viewpoint and aspect. A sentiment capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed to identify the informative logic unit and the sentiment based representations in user-item level for rating prediction. Extensive experiments are conducted over seven real-world datasets with diverse characteristics. Our results demonstrate that the proposed CARP obtains substantial performance gain over recently proposed state-of-the-art models in terms of prediction accuracy. Further analysis shows that our model can successfully discover the interpretable reasons at a finer level of granularity.

研究动机与目标

• 解决依赖用户评论的深度学习推荐系统中可解释性不足的问题。
• 不仅将用户评分行为建模为整体情感的函数，更将其视为特定用户观点与商品属性共同作用的结果。
• 通过从评论中识别出有信息量的逻辑单元，实现对用户喜欢或不喜欢某商品原因的细粒度解释。
• 通过捕捉用户评论中复杂的多属性情感推理，提升评分预测的准确性。
• 提供一个统一框架，同时支持高精度推荐与人类可理解的解释。

提出的方法

• 在卷积层之上堆叠自注意力机制，从用户和商品评论文档中提取用户观点与商品属性。
• 每个用户-物品对被建模为由用户观点与商品属性配对形成的逻辑单元集合。
• 引入情感胶囊架构来表征每个逻辑单元的情感，采用新颖的双一致路由机制，动态地将信息路由至有信息量的单元。
• 该路由机制确保仅最相关的逻辑单元参与最终评分预测，从而提升可解释性与性能。
• 对原始评分进行最大归一化（Max-Normalization），以增强最终输出的可解释性。
• 通过非线性变换对情感胶囊输出进行处理，实现复杂用户偏好的端到端学习。

实验结果

研究问题

• RQ1 胶囊网络能否通过识别由用户观点与商品属性构成的细粒度逻辑单元，有效建模用户偏好？
• RQ2 所提出的双一致路由机制是否相比胶囊网络中的标准路由机制，能更有效地识别出有信息量的逻辑单元？
• RQ3 该模型能否仅使用用户评论，就实现比当前最先进深度学习模型更高的评分预测准确率？
• RQ4 该模型在多大程度上能通过提取的逻辑单元，为用户评分提供人类可理解的解释？
• RQ5 该模型在具有不同评论特征的多样化真实世界数据集上表现如何？

主要发现

• 在七个真实世界数据集上，CARP 在预测准确率方面显著优于近期最先进模型。
• 该模型成功发现与用户评论中实际情感与偏好相符的可解释逻辑单元。
• 在定性分析中，正负情感胶囊提取的前几大逻辑单元与用户评论的实际内容和情感高度一致。
• 双一致路由机制有效识别并强调了最相关的逻辑单元，表现为对评论文本中语义有意义短语的高注意力。
• 该模型捕捉到细微的情感动态，例如弱正向或负向影响，体现为低向量模长和较小的路由权重，对应于影响力较弱的逻辑单元。
• 采用最大归一化（Max-Normalization）显著提升了最终评分预测的可解释性，通过将原始输出缩放至更直观的范围。