基于文本注意力的推荐系统可解释性研究  

Research on Interpretability of Recommendation System based on Text Attention Mechanism

作　　者：朱芮[1] 刘布楼 刘艺语 邹鑫雨 李晨亮[1] ZHU Rui;LIU Bulou;LIU Yiyu;ZOU Xinyu;LI Chenliang(School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China;Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China)

机构地区：[1]武汉大学国家网络安全学院,武汉中国430072 [2]清华大学计算机科学与技术系,北京中国100084

出　　处：《信息安全学报》2021年第5期128-143,共16页Journal of Cyber Security

基　　金：国家自然科学基金(No.61872278)资助。

摘　　要：可解释性能够提高用户对推荐系统的信任度并且提升推荐系统的说服力和透明性,因此有许多工作都致力于实现推荐系统的可解释性。由于评论中包含了丰富的信息,能够体现用户偏好与情感信息,同时包含了对应商品所具有的特性,最近的一些基于评论的深度推荐系统有效地提高了推荐系统的可解释性。这些基于评论的深度推荐系统中内置的注意力机制能够从对应的评论中识别出有用的语义单元(例如词、属性或者评论),而推荐系统通过这些高权重的语义单元做出决策,从而增强推荐系统的可解释性。但可解释性在很多工作中仅作为一个辅助性的子任务,只在一些案例研究中来做出一些定性的比较,来说明推荐系统是具有可解释性的,到目前为止并没有一个能够综合地评估基于评论推荐系统可解释性的方法。本文首先根据在注意力权重计算机制的不同,将这些具有可解释性的基于评论的推荐系统分为三类:基于注意力的推荐系统,基于交互的推荐系统,基于属性的推荐系统,随后选取了五个最先进的基于评论的深度推荐系统,通过推荐系统内置的注意力机制获得的评论权重文档,在三个真实数据集上进行了人工标注,分别量化地评价推荐系统的可解释性。标注的结果表明不同的基于评论的深度推荐系统的可解释性是具有优劣之分的,但当前的基于评论的深度推荐系统都有超过一半的可能性能够捕捉到用户对目标评论的偏好信息。在评估的五个推荐系统中,并没有哪个推荐系统在所有的数据中具有绝对的优势。也就是说,这些推荐系统在推荐可解释性方面是相互补充的。通过进一步的数据分析发现,如果推荐系统具有更精确的分数预测结果,那推荐系统通过注意力机制获得的高权重的信息确实更能够体现用户的偏好或者商品特征,说明推荐系统内置的注意力�Interpretability can enhance users'trust in the recommendation systems,and improve the persuasion and transparency of the latter.So far,many efforts have been devoted to achieve recommendation interpretability.The rich information provided in user reviews can reflect user's preference and consumption experience,as well as the corresponding item's features.Hence,recent deep review-based recommendation systems capitalize reviews for accurate and interpretable recommendation and have advanced this purpose significantly.The built-in attention module devised in these deep review-based recommendation systems models can identify semantic units(e.g.,words,aspects,or individual reviews)from the corresponding reviews,which also facilitate the interpretability of the recommendation systems.However,interpretability is typically taken as an auxiliary evaluation subtask in some works,where examples are used as case studies for some qualitative comparison to show that the recommendation system is interpretable.Right now,there is no comprehensive evaluation towards how good the interpretability delivered by these review-based recommendation systems are.In this paper,according to the different calculation methods of attention weight,we first summarize existing deep review-based recommendation systems into three categories:attention-based recommendation system,interaction-based recommendation system,and aspect-based recommendation system.Then,we perform a human evaluation based on the built-in attention mechanism of five state-of-the-art deep review-based recommendation systems across three real-world datasets,covering all three categories for interpretability evaluation.The annotation results suggest that the interpretability of different deep review-based recommendation systems is different,but the current deep review-based recommendation systems can successfully uncover more than half of user's preference for the target item with higher chance.We also note that there is no absolute winner in discovering user preference from all

关 键 词：推荐系统 注意力机制 可解释性 用户评论 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]