Semi-metric Behavior in Document Networks and its Application to Recommendation Systems
L. M. Rocha
发表信息：
In: Soft Computing Agents: A New Perspective for Dynamic Information Systems. V. Loia (Ed.) International Series Frontiers in Artificial Intelligence and Applications. IOS Press, pp. 137-163, 2002
Recommendation systems for different Document Networks (DN) such as the World Wide Web (WWW) and Digital Libraries, often use distance functions extracted from relationships among documents and keywords. For instance, documents in the WWW are related via a hyperlink network, while documents in bibliographic databases are related by citation and collaboration networks. Furthermore, documents are related to keyterms. The distance functions computed from these relations establish associative networks among items of the DN, referred to as Distance Graphs, which allow recommendation systems to identify relevant associations for individual users. However, modern recommendation systems need to integrate associative data from multiple sources such as different databases, web sites, and even other users. Thus, we are presented with a problem of combining evidence (about associations between items) from different sources characterized by distance functions. In this paper we describe our work on (1) inferring relevant associations from, as well as characterizing, semi-metric distance graphs and (2) combining evidence from different distance graphs in a recommendation system. Regarding (1), we present the idea of semi-metric distance graphs, and introduce ratios to measure semi-metric behavior. We compute these ratios for several DN such as digital libraries and web sites and show that they are useful to identify implicit associations. Regarding (2), we describe an algorithm to combine evidence from distance graphs that uses Evidence Sets, a set structure based on Interval Valued Fuzzy Sets and Dempster-Shafer Theory of Evidence. This algorithm has been developed for a recommendation system named TalkMine.
Multi-criteria recommendation systems to foster online grocery
College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT)（阿拉伯科学、技术与海运学院（AASTMT）计算与信息技术学院）；atlanTTic, Universidade de Vigo（维戈大学atlanTTic研究中心）
AMIEDoT: An annotation model for document tracking and recommendation service
无论是手动还是电子形式的文件注释，其主要目的是允许那些可能无法控制原始文件的人提供对信息源的个人看法。除了为原始信息源提供个人评估之外，我们还在研究一种情况，即所做的注释可以用作文档跟踪和推荐服务的额外信息源。今天存在的大多数注释工具都是为独立使用而设计的，没有参考注释的创建者。我们提出了一个可以辅助文档跟踪和推荐服务的标注模型AMIEDoT（Annotation Model for Information Exchange and Document Tracking）。该模型基于注释行为中的三个参数。我们相信，将文档参数、时间和注释创建者的参数引入注释过程可以成为了解谁使用了文档、何时使用文档以及文档用于什么的可靠来源。除了文档跟踪，我们的模型不仅可以用于信息的选择性传播，还可以用于推荐服务。AMIEDoT还可用于信息共享和信息重用。
Enhancing Academic Paper Recommendations Using Fine-Grained Knowledge Entities and Multifaceted Document Embeddings
Department of Information Management（信息管理系）；Nanjing University of Science & Technology（南京理工大学）；School of Computer Engineering（计算机工程学院）；Jiangsu University of Technology（江苏科技大学）；School of Information Management（信息管理学院）；Central China Normal University（中央财经大学）
Evaluating Document Representations for Content-based Legal Literature Recommendations
Open Legal Data（开放法律数据）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；ETH Zurich（苏黎世联邦理工学院）；University of Wuppertal（伍珀塔尔大学）
推荐系统帮助法律专业人士找到相关文献来支持他们的案件。尽管它的重要性的专业，法律应用并没有反映在推荐系统和表示学习研究的最新进展。同时，法律推荐系统通常在没有任何公共基准数据集的情况下在小规模用户研究中进行评估。因此，这些研究的重现性有限。为了解决研究与实践之间的差距，我们探索了一套国家的最先进的文件表示方法检索语义相关的美国判例法的任务。我们评估基于文本的（例如，fastText、Transformers）、基于引用的（例如，DeepWalk，Poincaré）和混合方法。我们使用两种银标准对2，964份文件进行了注释，共比较了27种方法。银标准是从Open Case Book和Wikispecies新创建的，可以在开放许可下重复使用，以促进可重复性。我们的实验表明，来自平均fastText单词向量（在法律语料库上训练）的文档表示产生了最好的结果，紧随其后的是庞加莱引用嵌入。以混合方式组合fastText和Poincaré进一步改善了整体结果。除了整体性能外，我们还根据文档长度，引用计数和建议的覆盖范围分析了这些方法。我们在https://github.com/malteos/legal-document-similarity/上公开我们的源代码、模型和数据集。
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation
School of Information Management Sun Yat-sen University Guangzhou, China；School of Information Management Sun Yat-sen University Guangzhou, China corresponding author
Semi-supervised Nonnegative Matrix Factorization for Document Classification
Harvey Mudd College（哈维穆德学院）；Colorado State Univ.（科罗拉多州立大学）；Univ. of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cal. State Univ. Channel Islands（加州州立大学海峡群岛分校）；Wofford College（沃福德学院）；New York Univ.（纽约大学）；Univ. of Alabama（阿拉巴马大学）；Univ. of Cal., Los Angeles（加州大学洛杉矶分校）；Boston Medical Cent.（波士顿医疗中心）；Southern Methodist Univ.（南卫理公会大学）；Occidental College（西方学院）
