I am confused: At the #SIGIR business meeting, the executive committee claimed that "all ACM SIGIR publications are permanent open access on the DL", but that is clearly not the case, see my attempt to download Bruce Croft's SIGIR 2019 keynote paper.
All ACM SIGIR publications and permanent open access in the ACM digital library! #SIGIR2020
#SIGIR2020 Diversity, Equity and Inclusivity checklist
See you in Montreal at #SIGIR2021!
#SIGIR 2020 Test of time award goes to Hao Ma et al. SIGIR 2009 "Learning to recommend with social trust ensemble"
#SIGIR 2020 Test of time award Honorable Mention 2: Guihong Cao et al. SIGIR 2008
#SIGIR 2020 Test of time award Honorable Mention 1: Georges Dupret and Benjamin Piwowarski, SIGIR 2008
Best #SIGIR2020 short paper: Shi Yu et al. "Few-short conversational query rewriting"
#SIGIR 2020 Short paper honorable mention: Jianxin Chang: "Bundle recommendation with graph convolutional networks"
Best #SIGIR2020 paper: Marco Mornik et al. "Controlling fairness and bias in dynamic learning to rank"
Honorable mention best paper award #SIGIR2020: Fan Zhang et al. "Models vs Satisfaction"
Going to find out how deep learning works for Information Retrieval with Dacheng Tao. #SIGIR2020
Advise for #SIGIR:
Towards better experimentation! #SIGIR2020
Norbert Fuhr's recommendations for gaining scientific knowledge from experiments:
1. Do not use MRR or MAP;
2. Instead of relative improvements, regard the effect size!
3. For multiple significance tests, use a correction, such as Bonferoni or Tukey's HSD (NB comparing only to the 2nd best method does not help!)
4. There are no significant improvements for re-usable test collections! (hypotheses have to be formulated before the work)
Nice! Harrie Oosterhuis presenting a new counter-factual learning to rank approach that takes the item display policy (top-k cut-off) into account. #SIGIR2020
Proud to be part of the Women in IR panel this year: 😊
Professor of Federated Search at Radboud University. Formerly known as @hiemstra
The "unofficial" Information Retrieval Mastodon Instance.
Goal: Make idf.social a viable and valuable social space for anyone working in Information Retrieval and related scientific research.
Everyone welcome but expect some level of geekiness on the instance and federated timelines.