# Large-Scale Sequential Learning for Recommender and Engineering Systems

arXiv: https://arxiv.org/abs/2205.06893 · HTML: https://mlsanity.com/paper/2205.06893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 151 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming](https://mlsanity.com/paper/1309.5549/) (2013)
- [Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Condition](https://mlsanity.com/paper/1608.04636/) (2016)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition](https://mlsanity.com/paper/1503.02101/) (2015)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://mlsanity.com/paper/1708.05024/) (2017)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [Physics-Informed Neural Networks for Power Systems](https://mlsanity.com/paper/1911.03737/) (2019)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://mlsanity.com/paper/1711.08506/) (2017)
- [E-commerce in Your Inbox: Product Recommendations at Scale](https://mlsanity.com/paper/1606.07154/) (2016)
- ['Zhores' -- Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology](https://mlsanity.com/paper/1902.07490/) (2019)
- [Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks](https://mlsanity.com/paper/1810.05247/) (2018)
- [Swivel: Improving Embeddings by Noticing What's Missing](https://mlsanity.com/paper/1602.02215/) (2016)
- [Importance sampling approach to chance-constrained DC optimal power flow](https://mlsanity.com/paper/2111.11729/) (2021)
- [Tight Risk Bounds for Multi-Class Margin Classifiers](https://mlsanity.com/paper/1507.03040/) (2015)
- [Power Grid Reliability Estimation via Adaptive Importance Sampling](https://mlsanity.com/paper/2105.08753/) (2021)
- [Efficient Numerical Methods to Solve Sparse Linear Equations with Application to PageRank](https://mlsanity.com/paper/1508.07607/) (2015)
- [Entropy Penalized Semidefinite Programming](https://mlsanity.com/paper/1802.04332/) (2018)
- [PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels](https://mlsanity.com/paper/2107.02275/) (2021)
- [Physics-Informed Learning for High Impedance Faults Detection](https://mlsanity.com/paper/2008.02364/) (2020)

## Built on by (most cited first, 1 of 1)

- [Data-Driven Stochastic AC-OPF using Gaussian Processes](https://mlsanity.com/paper/2402.11365/) (2024)

## Abstract (arXiv)

In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. To demonstrate the empirical efficiency of the proposed approaches we investigate their applications for decision making in recommender systems and energy systems domains. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed approach consists in minimizing pairwise ranking loss over blocks constituted by a sequence of non-clicked items followed by the clicked one for each user. We also explore the influence of long memory on the accurateness of predictions. SAROS shows highly competitive and promising results based on quality metrics and also it turn out faster in terms of loss convergence than stochastic gradient descent and batch classical approaches. Regarding power systems, we propose an algorithm for faulted lines detection based on focusing of misclassifications in lines close to the true event location. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach based on convolutional neural networks for faults detection in power grid.