Beyond Existing Retrievals: Cross-Scenario Incremental Sample Learning Framework

• URL: http://arxiv.org/abs/2512.06381v1
• Date: Sat, 06 Dec 2025 10:26:18 GMT
• Title: Beyond Existing Retrievals: Cross-Scenario Incremental Sample Learning Framework
• Abstract summary: In this paper, we propose a novel retrieval framework IncRec for cross-scenario incremental sample learning.<n>In particular, we deploy IncRec in the Taobao homepage recommendation, achieving a 1% increase in online transaction count.
• Score: 14.269153599045152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The parallelized multi-retrieval architecture has been widely adopted in large-scale recommender systems for its computational efficiency and comprehensive coverage of user interests. Many retrieval methods typically integrate additional cross-scenario samples to enhance the overall performance ceiling. However, those model designs neglect the fact that a part of the cross-scenario samples have already been retrieved by existing models within a system, leading to diminishing marginal utility in delivering incremental performance gains. In this paper, we propose a novel retrieval framework IncRec, specifically for cross-scenario incremental sample learning. The innovations of IncRec can be highlighted as two aspects. Firstly, we construct extreme cross-scenario incremental samples that are not retrieved by any existing model. And we design an incremental sample learning framework which focuses on capturing incremental representation to improve the overall retrieval performance. Secondly, we introduce a consistency-aware alignment module to further make the model prefer incremental samples with high exposure probability. Extensive offline and online A/B tests validate the superiority of our framework over state-of-the-art retrieval methods. In particular, we deploy IncRec in the Taobao homepage recommendation, achieving a 1% increase in online transaction count, demonstrating its practical applicability.

Related papers

• Reason to Contrast: A Cascaded Multimodal Retrieval Framework [60.99421225506685]
  TTE-v2, a hybrid multimodal retrieval framework, introduces reasoning-driven performance scaling based on additional input token budget rather than model or embedding size.<n>Our approach augments the initial multimodal retrieval with additional reasoning steps for reranking, enabling more expressive query-candidate interactions at test time.<n> Experiments on the MMEB-V2 benchmark demonstrate that TTE-v2-7B achieves a new state-of-the-art accuracy of 75.7%, and that TTE-v2-2B matches or surpasses leading 7B models trained with significantly larger external data.
  arXiv  Detail & Related papers  (2025-12-21T04:52:48Z)
• SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling [58.05959902776133]
  We introduce Single-Pass.<n>with Reference-Guided Evaluation (SPARE), a novel structured framework that enables efficient per-step annotation.<n>We demonstrate SPARE's effectiveness across four diverse datasets spanning mathematical reasoning (GSM8K, MATH), multi-hop question answering (MuSiQue-Ans), and spatial reasoning (SpaRP)<n>On ProcessBench, SPARE demonstrates data-efficient out-of-distribution generalization, using only $sim$16% of training samples compared to human-labeled and other synthetically trained baselines.
  arXiv  Detail & Related papers  (2025-06-18T14:37:59Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models generate target documents directly from a query.<n>We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• Few-Shot, No Problem: Descriptive Continual Relation Extraction [27.296604792388646]
  Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in real-world domains.<n>Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge.<n>We propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation.
  arXiv  Detail & Related papers  (2025-02-27T23:44:30Z)
• OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment [9.99840965933561]
  We propose OneRec, which replaces the cascaded learning framework with a unified generative model.<n>OneRec includes: 1) an encoder-decoder structure, which encodes the user's historical behavior sequences and gradually decodes the videos that the user may be interested in.
  arXiv  Detail & Related papers  (2025-02-26T09:25:10Z)
• Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark [65.13288661320364]
  We introduce our benchmark, textbfScenario-Wise Rec, which comprises 6 public datasets and 12 benchmark models, along with a training and evaluation pipeline.<n>We aim for this benchmark to offer researchers valuable insights from prior work, enabling the development of novel models.
  arXiv  Detail & Related papers  (2024-12-23T08:15:34Z)
• Simple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao [21.01879010592532]
  We introduce an innovative multi-scenario nearline retrieval framework. It operates by harnessing ranking logs from various scenarios through Flink. Our method has shown substantial improvements-most notably, a 5% uptick in product transactions.
  arXiv  Detail & Related papers  (2024-08-01T02:54:23Z)
• Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection [3.913403111891027]
  We modify the commonly used "divide and conquer" framework in parallel computing by adding a correlation-based clustering step.<n>This seemingly simple modification yields an $mathcalO(p)$ reduction in sample complexity for a widely used class of sample-optimal R&S procedures.<n>In large-scale AI applications such as neural architecture search, our methods demonstrate superior performance.
  arXiv  Detail & Related papers  (2024-02-03T15:56:03Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Metric Compatible Training for Online Backfilling in Large-Scale Retrieval [67.72644952719791]
  Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems.<n>We propose an online backfilling algorithm, which enables us to achieve a progressive performance improvement during the backfilling process.<n>We incorporate a reverse transformation module for more effective and efficient merging, which is further enhanced by adopting a metric-compatible contrastive learning approach.
  arXiv  Detail & Related papers  (2023-01-10T03:10:32Z)
• Generalization Properties of Retrieval-based Models [50.35325326050263]
  Retrieval-based machine learning methods have enjoyed success on a wide range of problems. Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored. We present a formal treatment of retrieval-based models to characterize their generalization ability.
  arXiv  Detail & Related papers  (2022-10-06T00:33:01Z)
• Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning [27.04857039060308]
  We propose to combine the best of both worlds to build a retrieval system. We employ a fast bi-encoder to replace the traditional feature-based pre-retrieval model. We train the pre-retrieval model and the re-ranking model at the same time via mutual learning.
  arXiv  Detail & Related papers  (2021-10-01T01:32:33Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)

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.