Incremental Learning for Personalized Recommender Systems

• URL: http://arxiv.org/abs/2108.13299v1
• Date: Fri, 13 Aug 2021 04:21:21 GMT
• Title: Incremental Learning for Personalized Recommender Systems
• Authors: Yunbo Ouyang, Jun Shi, Haichao Wei, Huiji Gao
• Abstract summary: We present an incremental learning solution to provide both the training efficiency and the model quality. The solution is deployed in LinkedIn and directly applicable to industrial scale recommender systems.
• Score: 8.020546404087922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment. The former requires a complex machine learning model that is trained on a large amount of data; the latter requires frequent update to the model. We present an incremental learning solution to provide both the training efficiency and the model quality. Our solution is based on sequential Bayesian update and quadratic approximation. Our focus is on large-scale personalized logistic regression models, with extensions to deep learning models. This paper fills in the gap between the theory and the practice by addressing a few implementation challenges that arise when applying incremental learning to large personalized recommender systems. Detailed offline and online experiments demonstrated our approach can significantly shorten the training time while maintaining the model accuracy. The solution is deployed in LinkedIn and directly applicable to industrial scale recommender systems.

Related papers

• Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models [62.5501109475725]
  Knowledge distillation (KD) is a technique that compresses large teacher models by training smaller student models to mimic them. This paper introduces Online Knowledge Distillation (OKD), where the teacher network integrates small online modules to concurrently train with the student model. OKD achieves or exceeds the performance of leading methods in various model architectures and sizes, reducing training time by up to fourfold.
  arXiv  Detail & Related papers  (2024-09-19T07:05:26Z)
• Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation [20.851925464903804]
  This paper introduces a novel learning paradigm, Dynamic Sparse Learning, tailored for recommendation models. DSL innovatively trains a lightweight sparse model from scratch, periodically evaluating and dynamically adjusting each weight's significance. Our experimental results underline DSL's effectiveness, significantly reducing training and inference costs while delivering comparable recommendation performance.
  arXiv  Detail & Related papers  (2024-02-05T10:16:20Z)
• Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model [74.62272538148245]
  We show that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other. We investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation.
  arXiv  Detail & Related papers  (2023-10-26T17:59:46Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• Effective and Efficient Training for Sequential Recommendation using Recency Sampling [91.02268704681124]
  We propose a novel Recency-based Sampling of Sequences training objective. We show that the models enhanced with our method can achieve performances exceeding or very close to stateof-the-art BERT4Rec.
  arXiv  Detail & Related papers  (2022-07-06T13:06:31Z)
• WeiPS: a symmetric fusion model framework for large-scale online learning [6.88870384575896]
  We propose a symmetric fusion online learning system framework called WeiPS, which integrates model training and model inference. Specifically, WeiPS carries out second level model deployment by streaming update mechanism to satisfy the consistency requirement. It uses multi-level fault tolerance and real-time domino degradation to achieve high availability requirement.
  arXiv  Detail & Related papers  (2020-11-24T09:25:39Z)
• Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data [13.080986170257782]
  Pruning is an effective technique that reduces both memory and compute demand for model inference. This work presents an adaptive dense to sparse paradigm equipped with a novel pruning algorithm for pruning a large scale recommendation system with non-stationary data distribution.
  arXiv  Detail & Related papers  (2020-10-16T22:33:14Z)
• Lambda Learner: Fast Incremental Learning on Data Streams [5.543723668681475]
  We propose a new framework for training models by incremental updates in response to mini-batches from data streams. We show that the resulting model of our framework closely estimates a periodically updated model trained on offline data and outperforms it when model updates are time-sensitive. We present a large-scale deployment on the sponsored content platform for a large social network.
  arXiv  Detail & Related papers  (2020-10-11T04:00:34Z)
• Learning to Reweight with Deep Interactions [104.68509759134878]
  We propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.
  arXiv  Detail & Related papers  (2020-07-09T09:06:31Z)
• Efficient Learning of Model Weights via Changing Features During Training [0.0]
  We propose a machine learning model, which dynamically changes the features during training. Our main motivation is to update the model in a small content during the training process with replacing less descriptive features to new ones from a large pool.
  arXiv  Detail & Related papers  (2020-02-21T12:38:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.