This Special Issue presents three invited papers, selected from among the best contributions that were presented at the 2017 ACM International Conference on Hypertext and Social Media (HT 2017) held in Prague, Czech Republic on 4–7th July 2017. Since 1987, HT has successfully brought together leading researchers and developers from the Hypertext community. It is concerned with all aspects of modern hypertext research, including social media, adaptation, personalisation, recommendations, user modelling, linked data and semantic web, dynamic and computed hypertext, and its application in digital humanities, as well as with interplay between those aspects such as linking stories with data or linking people with resources. The call for papers of HT 2017 was organised into four technical tracks: Social Networks and Digital Humanities (Linking people), Semantic Web and Linked Data (Linking data), Adaptive Hypertext and Recommendations (Linking resources), News and Storytelling (Linking stories). The Program Committee of HT 2017 accepted 19 papers (acceptance rate 27%) for regular presentation, and an additional 12 short-presentation papers. In addition, the conference featured four demonstrations and two keynotes: Kristina Lerman and Peter Mika. The three papers selected for this Special Issue cover a diverse set of topics, well representing the spectrum of topics that were discussed at HT 2017. The first paper, entitled “Implicit Negative Link Detection on Online Political Networks via Matrix Tri-Factorizations” (Ozer, Yildirim and Davulcu), deals with the prediction of negative connections between users of online political networks. Currently, the majority of social media sites do not support explicit negative links between participating users. However, the very nature of the political discourse often involves users in discussing controversial political issues, which results in a series of agreements and disagreements. The authors present a technically sound approach to extracting negative links from a variety of online political platforms by using a matrix factorisation approach. Matrix factorisation is extended in multiple ways to reflect the information that can be found in the sentiment of the written comments as well as the social balance theory known from the social sciences. The paper concludes with a range of experiments on the real datasets using the Twitter accounts of the politicians of the major UK political parties. The experiments show an improved accuracy of the community detection methods applied on the networks with the extracted negative interaction links as compared to the application of these methods on the networks having only positive links. The second paper, entitled “Hybrid Recommendations by Content-Aligned Bayesian Personalized Ranking” (Peska) focuses on recommender systems that seek to predict the "rating" or "preference" a user would give to an item and hence enabling to display items in order the user might find interesting. A special problem is cold-start recommendation, i.e. for a new user or of a new item. The author proposes a hybrid recommendation technique “ContentAligned Bayesian Personalized Ranking” (CABPR) with several variants. This is based on an existing Bayesian Personalized Ranking matrix factorization (BPR) by Rendle et al. CABPR

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ACM超文本与社交媒体会议特邀论文

本特刊介绍了三篇特邀论文，这些论文选自于 2017 年 7 月 4 日至 7 日在捷克共和国布拉格举行的 2017 年 ACM 超文本和社交媒体国际会议 (HT 2017) 上的最佳贡献。自 1987 年以来，HT成功汇集了来自超文本社区的领先研究人员和开发人员。它涉及现代超文本研究的所有方面，包括社交媒体、适应、个性化、推荐、用户建模、链接数据和语义网络、动态和计算超文本，及其在数字人文学科中的应用，以及这些方面之间的相互作用例如将故事与数据联系起来或将人员与资源联系起来。HT 2017 论文征集分为四个技术轨道：社交网络和数字人文（链接人），语义网和链接数据（链接数据）、自适应超文本和推荐（链接资源）、新闻和讲故事（链接故事）。HT 2017 程序委员会接受了 19 篇常规报告（接受率 27%），另外还有 12 篇短报告。此外，会议还包括四个演示和两个主题演讲：Kristina Lerman 和 Peter Mika。本期特刊入选的三篇论文涵盖了一系列不同的主题，很好地代表了 HT 2017 上讨论的主题范围。第一篇论文题为“通过矩阵三因子化在线政治网络上的隐式负链接检测”（Ozer , Yildirim 和 Davulcu)，处理在线政治网络用户之间的负面联系的预测。现在，大多数社交媒体网站不支持参与用户之间的明确负面链接。然而，政治话语的本质往往涉及用户讨论有争议的政治问题，从而导致一系列的同意和分歧。作者提出了一种技术上合理的方法，通过使用矩阵分解方法从各种在线政治平台中提取负面链接。矩阵分解以多种方式扩展，以反映可以在书面评论的情绪以及社会科学中已知的社会平衡理论中找到的信息。论文最后使用英国主要政党政治家的 Twitter 帐户对真实数据集进行了一系列实验。实验表明，与将这些方法应用于仅具有正链接的网络相比，应用于具有提取的负交互链接的网络的社区检测方法的准确性有所提高。第二篇论文题为“基于内容对齐的贝叶斯个性化排名的混合推荐”（Peska）侧重于推荐系统，该系统试图预测用户对项目的“评级”或“偏好”，从而能够按顺序显示项目用户可能会觉得有趣。一个特殊的问题是冷启动推荐，即针对新用户或新项目。作者提出了一种混合推荐技术“ContentAligned Bayesian Personalized Ranking”（CABPR），有多种变体。这是基于 Rendle 等人现有的贝叶斯个性化排名矩阵分解 (BPR)。CABPR