On the Difficulty of Evaluating Baselines: A Study on Recommender Systems — mentioned by samplenoise

Wed, 07 Jul 2021 08:58:19 GMT

The author is still underselling the significance of the progress made during the first years IMO. The simple idea that is still behind most practical recommender systems (using gradient descent to do SVD to complete the rating matrix) was first described in 2006 by Simon Funk [1]. Koren, who ended up taking home a big part of the prize, recently wrote another paper about how that basic idea still outperforms most “AI” (deep neural) recommenders today [2]. [1] https://sifter.org/~simon/journal/20061211.html [2] https://arxiv.org/abs/1905.01395

Paper and discussion history. A mention is not an endorsement.

On the Difficulty of Evaluating Baselines: A Study on Recommender Systems · HackerPapers discussions

On the Difficulty of Evaluating Baselines: A Study on Recommender Systems — mentioned by samplenoise