# Intelligent Model Update Strategy for Sequential Recommendation

arXiv: https://arxiv.org/abs/2302.07335 · HTML: https://mlsanity.com/paper/2302.07335/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis](https://mlsanity.com/paper/2204.09934/) (2022)
- [Sequential Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2106.14226/) (2021)
- [BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation](https://mlsanity.com/paper/2203.02533/) (2022)
- [Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing](https://mlsanity.com/paper/2007.09236/) (2020)
- [DeVLBert: Learning Deconfounded Visio-Linguistic Representations](https://mlsanity.com/paper/2008.06884/) (2020)
- [DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization](https://mlsanity.com/paper/2209.05227/) (2022)
- [TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning](https://mlsanity.com/paper/2007.11622/) (2020)
- [MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning](https://mlsanity.com/paper/2112.06558/) (2021)
- [End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding](https://mlsanity.com/paper/2203.08013/) (2022)
- [Session-aware Recommendation: A Surprising Quest for the State-of-the-art](https://mlsanity.com/paper/2011.03424/) (2020)
- [Mitigating Spurious Correlations for Self-supervised Recommendation](https://mlsanity.com/paper/2212.04282/) (2022)

## Built on by (most cited first, 25 of 25)

- [HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models](https://mlsanity.com/paper/2403.13447/) (2024)
- [Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels](https://mlsanity.com/paper/2304.10539/) (2023)
- [Universal Domain Adaptation via Compressive Attention Matching](https://mlsanity.com/paper/2304.11862/) (2023)
- [Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation](https://mlsanity.com/paper/2501.05647/) (2025)
- [FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection](https://mlsanity.com/paper/2410.11397/) (2024)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Disentangled Knowledge Tracing for Alleviating Cognitive Bias](https://mlsanity.com/paper/2503.02539/) (2025)
- [Distributionally Generative Augmentation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2403.06606/) (2024)
- [Debiased Collaborative Filtering with Kernel-Based Causal Balancing](https://mlsanity.com/paper/2404.19596/) (2024)
- [Semantic Codebook Learning for Dynamic Recommendation Models](https://mlsanity.com/paper/2408.00123/) (2024)
- [AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation](https://mlsanity.com/paper/2403.07030/) (2024)
- [Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration](https://mlsanity.com/paper/2406.01601/) (2024)
- [Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating](https://mlsanity.com/paper/2505.19543/) (2025)
- [Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization](https://mlsanity.com/paper/2305.15889/) (2023)
- [DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://mlsanity.com/paper/2406.08804/) (2024)
- [Forward Once for All: Structural Parameterized Adaptation for Efficient Cloud-coordinated On-device Recommendation](https://mlsanity.com/paper/2501.02837/) (2025)
- [Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions](https://mlsanity.com/paper/2503.09311/) (2025)
- [Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference](https://mlsanity.com/paper/2404.19620/) (2024)
- [Learning to Reweight for Graph Neural Network](https://mlsanity.com/paper/2312.12475/) (2023)
- [Cloud-Device Collaborative Agents for Sequential Recommendation](https://mlsanity.com/paper/2509.01551/) (2025)
- [CAusal and collaborative proxy-tasKs lEarning for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2303.17526/) (2023)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration](https://mlsanity.com/paper/2501.07596/) (2025)
- [Tackling Device Data Distribution Real-time Shift via Prototype-based Parameter Editing](https://mlsanity.com/paper/2509.06552/) (2025)
- [CHORD: Customizing Hybrid-precision On-device Model for Sequential Recommendation with Device-cloud Collaboration](https://mlsanity.com/paper/2510.03038/) (2025)

## Abstract (arXiv)

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.