Infinite Recommendation Networks: A Data-Centric Approach

• URL: http://arxiv.org/abs/2206.02626v1
• Date: Fri, 3 Jun 2022 00:34:13 GMT
• Title: Infinite Recommendation Networks: A Data-Centric Approach
• Authors: Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian McAuley
• Abstract summary: We leverage the Neural Tangent Kernel to train infinitely-wide neural networks to devise $infty$-AE: an autoencoder with infinitely-wide bottleneck layers. We also develop Distill-CF for synthesizing tiny, high-fidelity data summaries. We observe 96-105% of $infty$-AE's performance on the full dataset with as little as 0.1% of the original dataset size.
• Score: 8.044430277912936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We leverage the Neural Tangent Kernel and its equivalence to training infinitely-wide neural networks to devise $\infty$-AE: an autoencoder with infinitely-wide bottleneck layers. The outcome is a highly expressive yet simplistic recommendation model with a single hyper-parameter and a closed-form solution. Leveraging $\infty$-AE's simplicity, we also develop Distill-CF for synthesizing tiny, high-fidelity data summaries which distill the most important knowledge from the extremely large and sparse user-item interaction matrix for efficient and accurate subsequent data-usage like model training, inference, architecture search, etc. This takes a data-centric approach to recommendation, where we aim to improve the quality of logged user-feedback data for subsequent modeling, independent of the learning algorithm. We particularly utilize the concept of differentiable Gumbel-sampling to handle the inherent data heterogeneity, sparsity, and semi-structuredness, while being scalable to datasets with hundreds of millions of user-item interactions. Both of our proposed approaches significantly outperform their respective state-of-the-art and when used together, we observe 96-105% of $\infty$-AE's performance on the full dataset with as little as 0.1% of the original dataset size, leading us to explore the counter-intuitive question: Is more data what you need for better recommendation?

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