This paper introduces a simple and effective form of data augmentation for recommender systems. A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that are added to the user-item graph. This increases the density of the graph without needing further labeled data. The data augmentation is evaluated on a variety of recommendation algorithms, using Euclidean, hyperbolic, and complex spaces, and over three categories of Amazon product reviews with differing characteristics. Results show that the data augmentation technique provides significant improvements to all types of models, with the most pronounced gains for knowledge graph-based recommenders, particularly in cold-start settings, leading to state-of-the-art performance.
Augmenting Rating-Scale Measures with Text-Derived Items Using the Information-Determined Scoring (IDS) Framework
Psychometrics Centre, Judge Business School, University of Cambridge（剑桥大学心理测量中心、剑桥大学商学院）；Faculty of Law, University of Cambridge（剑桥大学法学院）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；Faculty of Psychology, Beijing Normal University（北京师范大学心理学学院）
HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Good
Umeå University（于默奥大学）；The Australian E-Health Research Centre, CSIRO（澳大利亚电子健康研究中心，联邦科学与工业研究组织）；University of Engineering and Technology (UET), Vietnam National University, Hanoi（河内国家大学工程技术大学）；InfoRE Technology Company（InfoRE技术公司）；Hanoi University of Science (HUS), Vietnam National University, Hanoi（河内国家大学自然科学大学）
Parameter-Efficient Conversational Recommender System as a Language Processing Task
Nanyang Technological University（南洋理工大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Singapore University of Technology and Design（新加坡科技设计大学）
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
Tsinghua University（清华大学）；City University of Hong Kong（香港城市大学）；Dongguan University of Technology（东莞理工学院）；Alibaba Group（阿里巴巴集团）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data
College of Economics and Management, Beijing University of Technology（北京工业大学经济与管理学院）；Guilin University of Electronic Technology（桂林电子科技大学）；LAAS-CNRS, Université de Toulouse, CNRS（法国国家科学研究中心图卢兹大学LAAS实验室）
Automatic Paper Summary Generation from Visual and Textual Information
Waseda University（早稻田大学）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；Waseda Research Institute for Science and Engineering（早稻田大学科学与工程研究院）
