Embarrassingly Shallow Autoencoders for Sparse Data

By Harald Steck. The Web Conference, 2019

331 citations recorded by the source corpus.

Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show that its training objective has a closed-form solution, and discuss the resulting conceptual insights. Surprisingly, this simple model achieves better ranking accuracy than various state-of-the-art collaborative-filtering approaches, including deep non-linear models, on most of the publicly available data-sets used in our experiments.

Subfields: Information Systems, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0c96adf2309812f0bce0ddce7dee50af6dcf3851

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Wayne Xin Zhao
2. Yupeng Hou
3. Ji-rong Wen
4. Yaliang Li
5. Changxin Tian
6. Zihan Lin
7. Haibo Wang
8. Shanlei Mu

In Management Science and Operations Research

1. Ji-rong Wen
2. Yaliang Li
3. Wayne Xin Zhao
4. Haibo Wang

In Signal Processing

1. Yupeng Hou
2. Ji-rong Wen
3. Yaliang Li
4. Wayne Xin Zhao

Citations in the corpus

• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, background): “EASE [57] WWW 2019 MovieLens, Million Song, Netflix”
• beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems (2024, methodology): “SLAs became popular recently, mainly because the EASE [42] model has a closed-form solution while yielding high performance comparable to deep models.”