Improving Accountability in Recommender Systems Research Through Reproducibility
Alejandro Bellogín, Alan Said
备注：
Submitted in Nov 2020 to the Special Issue on "Fair, Accountable, and Transparent Recommender Systems" at User Modeling and User-Adapted Interaction journal
Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving towards fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human-Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us.
This work surveys existing definitions of these concepts, and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature, and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem, and facilitate progress in the field by increasing the accountability of research.
Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；College of Computer Science, Zhejiang University（浙江大学计算机科学学院）
Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data
CNRS（法国国家科学研究中心）；LIPN（巴黎第十三大学计算机科学实验室）；USPN（巴黎第十三大学）；LTCI（法国电信学院）；Télécom Paris（巴黎电信学院）；Deezer Research（Deezer研究院）；PACTE（政治与公共行动研究中心）；Sciences Po Grenoble（格勒诺布尔政治学院）
Politecnico di Milano（米兰理工大学）；University of Klagenfurt（克拉根福大学）
The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. In order to obtain a better understanding o…
Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point …
TU Dortmund（多特蒙德工业大学）；University of Torino（都灵大学）；University of Klagenfurt（克拉根福大学）
Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning based ("neural") approaches to session-based recommendations were proposed. However, previous research indicate…
