SIGIR Forum Article - Challenges and Research Opportunities in eCommerce Search and Recommendations
*** Underline link for the workshop *** - Participate! [Workshop starts July 15 at 8.55 am EDT/ 2.55 pm CET]
The SIGIR Workshop on eCommerce will serve as a platform for publication and discussion of Information Retrieval and NLP research & their applications in the domain of eCommerce. This workshop will bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in eCommerce.
The theme of this year's workshop is fairness in search and recommendation for eCommerce.
SIGIR eCom is a full day workshop taking place on Thursday, July 15, 2021 in conjunction with SIGIR 2021. SIGIR eCom'21 will be a virtual workshop.
|July 15, 2021 All times below in EDT - GMT-4|
|8:55 am EDT
5:55 am PDT
2:55 pm CET
|9:00 am EDT
6:00 am PDT
3:00 pm CET
|Contributed Papers Spotlight Session
A Deep Reinforcement Learning-Based Approach to Query-Free Interactive Target Item Retrieval
Anna Sepliarskaia, Sahika Genc and Maarten de Rijke
Preventing Contrast Effect Exploitation in Recommendations
Chris Nota, Georgios Theocharous, Michele Saad and Philip S. Thomas
Conditional Sequential Slate Optimization
Yipeng Zhang, Mingjian Lu, Saratchandra Indrakanti, Manojkumar Rangasamy Kannadasan and Abraham Bagherjeiran
|10:00 am EDT||Coffee Break|
|10:30 am EDT
07:30 am PDT
04:30 pm CET
Why Bias Affects Machine Learning and What Can We Do About It?
Kushal Kafle Adobe, USA
|11:05 am EDT
08:05 am PDT
05:05 pm CET
Fashion AI and Algorithmic size advice
Julia Lasserre Zalando, Europe
|12:00 pm EDT
09:00 am PDT
06:00 pm CET
|Long Break and Paper Discussion Session|
|12:45 pm EDT
09:45 am PDT
06:45 pm CET
Fact Checking Using Stance Detection and User Comments
Emine Yilmaz UCL and Amazon, UK
|1:40 pm EDT
10:40 am PDT
7:40 pm CET
Fairness in Search and Recommendation for eCommerce
Vanessa Murdock Amazon, USA
Henriette Cramer Spotify, USA
Michael Ekstrand Boise State University, USA
Nadia Fawaz Pinterest, USA
Alexandra Olteanu Microsoft Research, Canada
|3:00 pm EDT||Coffee Break|
|3:30 pm EDT
12:30 pm PDT
9:30 pm CET
Alex Beutel Google, USA
|4:05 - 5:30 pm EDT
1:05 - 2:30 pm PDT
|Data Challenge Discussion and Overview and Best paper
Jacopo Tagliabue Coveo, USA
|5:30 pm EDT||Group Discussion and Closing
Emine Yilmaz is a Professor and Turing Fellow at University College London (UCL), Department of Computer Science, as well as an Amazon Scholar at Amazon Alexa Shopping. Between 2012 and 2019, she also worked as a research consultant for Microsoft Research Cambridge, where she used to work as a full-time researcher prior to joining UCL. Her research until now has received several awards including a Bloomberg Data Science Research Award in 2018, the Karen Sparck Jones Award in 2015 and the Google Faculty Research Award in 2014. Emine's current research interests include automatic misinformation detection, fairness and bias in machine learning algorithms and conversational assistants. She has published research papers extensively at major venues such as ACM SIGIR, CIKM and WSDM, gave several tutorials as part of top conferences, and organised various workshops. She has served in various roles such as the Best Paper Awards Chair for SIGIR 2021, Panels Chair for The Web Conf 2021, PC Chair for ECIR 2019, and PC Chair for ACM SIGIR 2018.
Julia Lasserre has always been interested in the possibilities offered by machine learning and in applying various techniques to real-world problems. After receiving her PhD in machine learning and computer vision from Cambridge Uiversity, she did a post-doc in bioinformatics at the Max-Planck-Institute for Molecular Genetics, and then joined the industry. At Zalando, she has worked on different topics ranging from brand understanding to product tagging, active learning, representation learning and recommendations. She is currently an Principal Applied Scientist for the Size & Fit team and focuses on personalized size recommendations.
Kushal Kafle is a research scientist in the vision group at Adobe Research. Kushal joined Adobe Research in March 2020, after completing his PhD at the Rochester Institute of Technology. In the past, he has completed research internships at Microsoft and Adobe Research. His work at Adobe Research currently focused on accurate and bias-free prediction and understanding of object attributes and their relationships in images. His broader research interests and expertise lie at the intersection of computer vision and natural language processing, with a specific focus on visual question answering (VQA), where he has published extensively. As an active member of the computer vision and NLP research community, he has served on the program committee of numerous conferences and Journals. He co-organized the annual international workshop on “Shortcomings in vision and language (SiVL)” held at ECCV 2018 and NAACL 2019 and is currently serving as guest associate editor for a special issue in the Frontiers Journal.
Alex Beutel is a Senior Staff Research Scientist and team lead in Google Research, driving research spanning recommender systems, fairness, robustness, reinforcement learning, and ML for databases. He received his Ph.D. in 2016 from Carnegie Mellon University’s Computer Science Department, and previously received his B.S. from Duke University in computer science and physics. His Ph.D. thesis on large-scale user behavior modeling, covering recommender systems, fraud detection, and scalable machine learning, was given the SIGKDD 2017 Doctoral Dissertation Award Runner-Up. He received the Best Paper Award at KDD 2016 and ACM GIS 2010, was a finalist for best paper in KDD 2014, and was awarded the Facebook Fellowship in 2013 and the NSF Graduate Research Fellowship in 2011. More details can be found at alexbeutel.com.
Boise State University
We invite quality research contributions, position and opinion papers addressing relevant challenges in the domain of eCommerce. We invite submission of papers and posters representing original research, preliminary research results, proposals for new work, position and opinion papers. All submitted papers and posters will be single-blind and will be peer reviewed by an international program committee of researchers of high repute. Accepted submissions will be presented at the workshop.
Topics of interest include, but are not limited to:
In order to promote academic research in the eCommerce domain, we plan to accept a small number of high quality dataset contributions. These submissions should be accompanied by a clear and detailed description of the dataset, some potential questions and applications that arise from it. Preliminary empirical investigations conveying any insight about the data will increase the quality of the submission.
All papers will be peer reviewed (single-blind) by the program committee and judged by their relevance to the workshop, especially to the main themes identified above, and their potential to generate discussion. All submissions must be formatted according to the latest ACM SIG proceedings template available at http://www.acm.org/publications/proceedings-template (LaTeX users use sample-sigconf.tex as a template).
Submissions must describe work that is not previously published, not accepted for publication elsewhere, and not currently under review elsewhere. All submissions must be in English. The workshop follows a single-blind reviewing process. We do not accept anonymized submissions. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper.
Long paper limit: 8 pages. References are not counted in the page limit.
Short paper limit: 4 pages. References are not counted in the page limit.
Submissions to SIGIR eCom should be made at https://easychair.org/conferences/?conf=sigirecom21
The deadline for paper submission is May 26, 2021 (11: 59 P.M. AoE)