An open framework shows sliding-window training on long sequences is practical for recommenders, with a k-shift embedding enabling million-scale vocabularies on commodity GPUs and up to 6% gains on Retailrocket at 4x training cost.
On the consistency of maximum likelihood estimation of probabilistic principal component analysis
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Is Sliding Window All You Need? An Open Framework for Long-Sequence Recommendation
An open framework shows sliding-window training on long sequences is practical for recommenders, with a k-shift embedding enabling million-scale vocabularies on commodity GPUs and up to 6% gains on Retailrocket at 4x training cost.