Intelligent Model Update Strategy for Sequential Recommendation

• URL: http://arxiv.org/abs/2302.07335v2
• Date: Sat, 24 Aug 2024 02:22:16 GMT
• Title: Intelligent Model Update Strategy for Sequential Recommendation
• Authors: Zheqi Lv, Wenqiao Zhang, Zhengyu Chen, Shengyu Zhang, Kun Kuang,
• Abstract summary: We introduce IntellectReq, abbreviated as IntellectReq. IntellectReq is designed to operate on edge, evaluating the cost-benefit landscape of parameter requests with minimal communication overhead. We employ statistical mapping techniques to convert real-time user behavior into a normal distribution, thereby employing multi-sample outputs to quantify the model's uncertainty and thus its generalization capabilities.
• Score: 34.02565495747133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern online platforms are increasingly employing recommendation systems to address information overload and improve user engagement. There is an evolving paradigm in this research field that recommendation network learning occurs both on the cloud and on edges with knowledge transfer in between (i.e., edge-cloud collaboration). Recent works push this field further by enabling edge-specific context-aware adaptivity, where model parameters are updated in real-time based on incoming on-edge data. However, we argue that frequent data exchanges between the cloud and edges often lead to inefficiency and waste of communication/computation resources, as considerable parameter updates might be redundant. To investigate this problem, we introduce Intelligent Edge-Cloud Parameter Request Model, abbreviated as IntellectReq. IntellectReq is designed to operate on edge, evaluating the cost-benefit landscape of parameter requests with minimal computation and communication overhead. We formulate this as a novel learning task, aimed at the detection of out-of-distribution data, thereby fine-tuning adaptive communication strategies. Further, we employ statistical mapping techniques to convert real-time user behavior into a normal distribution, thereby employing multi-sample outputs to quantify the model's uncertainty and thus its generalization capabilities. Rigorous empirical validation on four widely-adopted benchmarks evaluates our approach, evidencing a marked improvement in the efficiency and generalizability of edge-cloud collaborative and dynamic recommendation systems.

Related papers

• Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning [4.479496001941191]
  Video Anomaly Detection (VAD) is a critical task in applications such as intelligent surveillance, evidence investigation, and violence detection. Traditional approaches to VAD often rely on finetuning large pre-trained models, which can be computationally expensive and impractical for real-time or resource-constrained environments. In this paper, we propose a novel framework that facilitates continuous KG adaptation directly on edge devices, overcoming the limitations of cloud dependency.
  arXiv  Detail & Related papers  (2024-11-13T22:55:45Z)
• Online Relational Inference for Evolving Multi-agent Interacting Systems [14.275434303742328]
  Online Inference (ORI) is designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning technique called AdaRelation.
  arXiv  Detail & Related papers  (2024-11-03T05:43:55Z)
• A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining [7.523158123940574]
  Recommendation systems provide users with content that meets their needs. Traditional click-through rate prediction and TOP-K recommendation mechanisms are unable to meet the recommendations needs. This paper proposes a recommendations system model based on a separation embedding cross-network.
  arXiv  Detail & Related papers  (2024-10-19T07:49:21Z)
• Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models [88.47454470043552]
  We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation. Online adaptation turns parameters into temporally changing states and provides a form of context-length extension with memory in weights.
  arXiv  Detail & Related papers  (2024-03-03T14:03:48Z)
• Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks [2.016365643222463]
  Cross-feature learning is crucial to handle data distribution drift and adapt to changing user behaviour. This work introduces Ad-Rec, a network that leverages feature interaction techniques to address covariate shifts. Our approach improves model quality, accelerates convergence, and reduces training time, as measured by the Area Under Curve (AUC) metric.
  arXiv  Detail & Related papers  (2023-08-28T21:08:06Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion [59.549664231655726]
  A case-based reasoning (CBR) system solves a new problem by retrieving cases' that are similar to the given problem. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs) Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB.
  arXiv  Detail & Related papers  (2020-10-07T17:48:12Z)

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.