Abstract. Sequential and session-based recommendation problems have received substantial attention in academic research in the past few years. While numerous algorithmic approaches are proposed every year for the underlying next-item prediction problem, it turns out that the progress that we make may in fact be rather limited due to widespread methodological issues. Furthermore, the research community continues to focus strongly on prediction accuracy, which is only one of several components of the success of a recommender system in practice. In this talk, we critically review the developments in the literature and provide a subjective selection of important areas which are currently not yet in the focus of the research community.
Dietmar Jannach Dietmar Jannach is a professor of computer science at the University of Klagenfurt, Austria. His main research theme is related to the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In recent years, he worked on various topics in the area of recommender systems. In this area, he also published the first international textbook on the topic.
Abstract. Recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is more nuanced: we explore the shortcomings of a simplistic, one-metric-fits-all approach and fight the false testing dichotomy quantitative-and-scalable vs. qualitative-and-manual. We introduce RecList to address the engineering challenge of a rounded evaluation of recommender systems, and discuss the motivation and opportunities behind two recent data challenges, EvalRS 2022 and 2023.
Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was co-founder and CTO of Tooso, a NLP startup in San Francisco acquired by TSX:CVO. He led Coveo’s AI roadmap from scale-up to IPO, and built out Coveo Labs, an applied R&D practice rooted in collaboration (with Stanford, Netflix, Microsoft, NVIDIA) open source and open science. Co-creator of the RecList library for trustworthy evaluation of recommender systems, his work appeared in venues such as NAACL, RecSys, WWW, SIGIR. While building his new company, Bauplan, he is teaching ML Systems at NYU, which is mostly notable because it is the only job he ever had that his parents understand.