[论文解读] Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps

Joeran Beel|arXiv (Cornell University)|Mar 27, 2017

Mind wandering and attentionNeuroscience被引用 17

一句话总结

本文提出了一种新颖的用户建模方法，用于研究论文推荐系统，以思维导图作为输入。通过分析Docear思维导图中的节点结构、关系以及时间元数据，该系统实现了7.20%的点击率——几乎是最佳基线方法的两倍，表明基于思维导图的建模在效果上与基于下载或引用论文的建模相当。

ABSTRACT

While user-modeling and recommender systems successfully utilize items like emails, news, and movies, they widely neglect mind-maps as a source for user modeling. We consider this a serious shortcoming since we assume user modeling based on mind maps to be equally effective as user modeling based on other items. Hence, millions of mind-mapping users could benefit from user-modeling applications such as recommender systems. The objective of this doctoral thesis is to develop an effective user-modeling approach based on mind maps. To achieve this objective, we integrate a recommender system in our mind-mapping and reference-management software Docear. The recommender system builds user models based on the mind maps, and recommends research papers based on the user models. As part of our research, we identify several variables relating to mind-map-based user modeling, and evaluate the variables' impact on user-modeling effectiveness with an offline evaluation, a user study, and an online evaluation based on 430,893 recommendations displayed to 4,700 users. We find, among others, that the number of analyzed nodes, modification time, visibility of nodes, relations between nodes, and number of children and siblings of a node affect the effectiveness of user modeling. When all variables are combined in a favorable way, this novel approach achieves click-through rates of 7.20%, which is nearly twice as effective as the best baseline. In addition, we show that user modeling based on mind maps performs about as well as user modeling based on other items, namely the research articles users downloaded or cited. Our findings let us to conclude that user modeling based on mind maps is a promising research field, and that developers of mind-mapping applications should integrate recommender systems into their applications. Such systems could create additional value for millions of mind-mapping users.

研究动机与目标

• 开发一种基于思维导图的高效用户建模方法，用于研究论文推荐。
• 评估思维导图的结构和时间变量对用户建模效果的影响。
• 将基于思维导图的用户建模与基于下载或引用论文的传统方法进行性能比较。
• 在Docear中集成一个推荐系统，利用思维导图数据提供个性化推荐。
• 证明基于思维导图的用户建模是现有建模技术的可行且有效的替代方案。

提出的方法

• 系统从Docear中的思维导图提取用户模型，利用节点深度、子节点数、兄弟节点数以及节点可见性等结构特征。
• 结合时间元数据，包括修改时间和创建时间，根据节点的新近程度和参与度对节点进行加权。
• 分析节点之间的关系，以捕捉用户知识结构中的语义和概念关联。
• 采用混合推荐算法，结合上述特征，基于用户的思维导图结构生成个性化论文推荐。
• 通过4,700名用户的430,893条推荐，开展离线评估、用户研究和在线评估，以衡量系统有效性。
• 采用点击率作为主要评估指标，与基线方法进行性能对比。

实验结果

研究问题

• RQ1 思维导图的结构属性（如节点深度、子节点数和兄弟节点数）如何影响用户建模的有效性？
• RQ2 节点的修改时间在多大程度上影响推荐系统中用户建模的准确性？
• RQ3 节点的可见性和节点间的关系连接如何影响推荐的相关性？
• RQ4 基于思维导图的用户建模能否实现与基于下载或引用研究论文的建模相当的性能？
• RQ5 在真实推荐场景中，哪些思维导图特征的组合能带来最高的点击率？

主要发现

• 所提出的基于思维导图的用户建模方法实现了7.20%的点击率，几乎是最佳基线方法的两倍。
• 分析的节点数量、修改时间、节点可见性以及节点间的关系显著影响推荐效果。
• 节点的子节点数和兄弟节点数被证明是用户建模质量的强预测因子。
• 基于思维导图的用户建模在推荐效果方面与基于下载或引用研究论文的建模表现相当。
• 将结构特征与时间特征以有利方式结合，可实现最高性能，验证了该方法的鲁棒性。
• 结果表明，基于思维导图的用户建模是学术场景下推荐系统的可行且有效的替代方案。