SIGIR 2021 E-Commerce Workshop Data Challenge
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Abstract (arXiv)
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Apr 2021.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 2; 2024: 2; 2025: 1.
11 papers.
- 2022M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendationscited by 9
- 2021Beyond NDCG: behavioral testing of recommender systems with RecListcited by 8
- 2021You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stackcited by 5
- 2023Reasonable Scale Machine Learning with Open-Source Metaflowcited by 5
- 2021"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shopscited by 4
- 2022"Does it come in black?" CLIP-like models are zero-shot recommenderscited by 4
- 2021Text Classification for Predicting Multi-level Product Categoriescited by 1
- 2023(Vector) Space is Not the Final Frontier: Product Search as Program Synthesiscited by 0
- 2024Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Predictioncited by 0
- 2024A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequencescited by 0
- 2025Machine Learning to Predict Digital Frustration from Clickstream Datacited by 0
- 2025Machine Learning to Predict Digital Frustration from Clickstream Data23 Dec 2025
- 2024A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences9 Oct 2024
- 2024Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction28 Aug 2024
- 2023(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis22 Apr 2023
- 2023Reasonable Scale Machine Learning with Open-Source Metaflow21 Mar 2023
- 2022M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations23 Sep 2022
- 2022"Does it come in black?" CLIP-like models are zero-shot recommenders5 Apr 2022
- 2021Beyond NDCG: behavioral testing of recommender systems with RecList18 Nov 2021
- 2021Text Classification for Predicting Multi-level Product Categories2 Sep 2021
- 2021You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack15 Jul 2021
- 2021"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops7 Jul 2021