Over the last few years, the arena of mobile application development has expanded considerably beyond the balance of the worldś software markets. With the growing number of mobile software companies, and the mounting sophistication of smartphones\' technology, developers have been building several categories of applications on dissimilar platforms. However, developers confront several challenges through the implementation of mobile application projects. In particular, there is a lack of consolidated systems that are competent to provide developers with personalised services promptly and efficiently. Hence, it is essential to develop tailored systems which can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a fortified set of techniques that are designed to provide mobile app developers with a distinctive platform to browse and search for the personalised artifacts. The proposed system make use of ontology and semantic web technology as well as machine learning techniques. In particular, the new RS framework comprises the following components; (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time-aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users\' query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a userś query with the minimum mismatches.
The closed loop between opinion formation and personalised recommendations
University College Groningen, University of Groningen（格罗宁根大学格罗宁根大学学院）；University of Twente（特文特大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency
Research Center on Information and Communication Technologies (CITIC)（信息与通信技术研究中心（CITIC））；Universidade da Coruña（拉科鲁尼亚大学）
有许多上下文中存在二元数据。社交网络就是一个众所周知的例子。在这些上下文中，成对的元素被链接起来，建立一个反映相互作用的网络。解释为什么建立这些关系对于获得透明度至关重要，透明度是一个越来越重要的概念。由于自然语言理解任务的普及，这些解释通常使用文本来呈现。我们的目标是代表和解释由任何代理建立的对（例如，推荐系统或付费促销机制），从而考虑基于文本的个性化。我们专注于TripAdvisor平台，考虑其他二元数据上下文的适用性。这些项目是用户和餐馆的子集，以及这些用户发布的评论的交互。我们提出了PTER（Personalised Text Based Reviews）模型。我们从给定餐厅的可用评论中预测适合特定用户交互的评论。PTER利用BERT（来自Transformers的双向编码器表示）变换器-编码器模型。我们根据基于特征的方法定制了一个深度神经网络，提出了一个LTR（学习排序）下游任务。我们进行了几次比较，我们的建议与随机基线和其他模型的最先进的，以下的额外（EXplanaTion Ranking）基准。我们的方法优于其他协同过滤建议。
Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems
Research Ireland ML-LABS；Dublin City University（都柏林城市大学）；ADAPT Centre（ADAPT中心）；School of Computing（计算学院）；Insight Research Ireland Centre for Data Analytics（Insight Research Ireland 数据分析中心）；University College Dublin（都柏林大学）
Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
School of Computer Science, University of Technology Sydney（技术科技大学计算机科学学院）；Australian Institute of Health Innovation, Macquarie University（麦觉里大学健康创新研究所）
An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care
Institute of Mathematics；Computer Science, University of S\ ao Paulo S\ ao Carlos Brazil；Department of Gerontology, S\ ao Carlos Federal University S\ ao Carlos Brazil；School of Arts, Sciences；Humanities, University of S\ ao Paulo S\ ao Paulo Brazil；Computer Science, University of S\ ao Paulo；Department of Gerontology, S\ ao Carlos Federal University；Humanities, University of S\ ao Paulo
Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；School of Information and Communication Studies（信息与传播研究学院）；University College Dublin（都柏林大学）
