--- title: "RecSys 2021 - Papers and Talks to Chew on" url: https://daily.dev/posts/recsys-2021---papers-and-talks-to-chew-on-cpxhcvk2j source_url: https://eugeneyan.com/writing/recsys2021 type: article source: "Eugene Yan" published: 2026-05-31T07:40:33.858Z updated: 2026-05-31T08:20:15.789Z tags: ["machine-learning", "deep-learning", "transformers"] reading_time: 5 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # RecSys 2021 - Papers and Talks to Chew on **[Eugene Yan](https://daily.dev/sources/eugeneyan)** · 5 min read · 0 upvotes · 0 comments ## Summary A curated roundup of notable papers from RecSys 2021, covering topics such as higher-order collaborative filtering extensions to EASE^R, a revisited comparison of matrix factorization vs. neural collaborative filtering (MF still wins on accuracy), a serverless open-source recommender deployment stack on AWS, the Transformers4Rec library for session-based recommendations, best practices for operating large-scale recommender systems (RecSysOps), semi-supervised fashion compatibility modeling, and cold-start handling via shared item embeddings. Key takeaways include the continued competitiveness of simple baselines over deep learning models and the value of data quality over model complexity. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#deep-learning](https://daily.dev/tags/deep-learning), [#transformers](https://daily.dev/tags/transformers) [View this post on daily.dev](https://daily.dev/posts/recsys-2021---papers-and-talks-to-chew-on-cpxhcvk2j)