SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system

• URL: http://arxiv.org/abs/2508.09090v2
• Date: Wed, 13 Aug 2025 01:51:32 GMT
• Title: SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
• Authors: Jialiang Shi, Yaguang Dou, Tian Qi,
• Abstract summary: Current multi-interest retrieval methods pose three major challenges.<n>Online inference typically employs an over-exploited strategy.<n>We propose a novel retrieval framework named "Soft Probabilistic Adaptive Retrieval Model via Codebooks"
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling multi-interests has arisen as a core problem in real-world RS. Current multi-interest retrieval methods pose three major challenges: 1) Interests, typically extracted from predefined external knowledge, are invariant. Failed to dynamically evolve with users' real-time consumption preferences. 2) Online inference typically employs an over-exploited strategy, mainly matching users' existing interests, lacking proactive exploration and discovery of novel and long-tail interests. To address these challenges, we propose a novel retrieval framework named SPARC(Soft Probabilistic Adaptive Retrieval Model via Codebooks). Our contribution is two folds. First, the framework utilizes Residual Quantized Variational Autoencoder (RQ-VAE) to construct a discretized interest space. It achieves joint training of the RQ-VAE with the industrial large scale recommendation model, mining behavior-aware interests that can perceive user feedback and evolve dynamically. Secondly, a probabilistic interest module that predicts the probability distribution over the entire dynamic and discrete interest space. This facilitates an efficient "soft-search" strategy during online inference, revolutionizing the retrieval paradigm from "passive matching" to "proactive exploration" and thereby effectively promoting interest discovery. Online A/B tests on an industrial platform with tens of millions daily active users, have achieved substantial gains in business metrics: +0.9% increase in user view duration, +0.4% increase in user page views (PV), and a +22.7% improvement in PV500(new content reaching 500 PVs in 24 hours). Offline evaluations are conducted on open-source Amazon Product datasets. Metrics, such as Recall@K and Normalized Discounted Cumulative Gain@K(NDCG@K), also showed consistent improvement. Both online and offline experiments validate the efficacy and practical value of the proposed method.

Related papers

• IterResearch: Rethinking Long-Horizon Agents via Markovian State Reconstruction [107.49922328855025]
  IterResearch is a novel iterative deep-research paradigm that reformulates long-horizon research as a Markov Decision Process.<n>It achieves substantial improvements over existing open-source agents with average +14.5pp across six benchmarks.<n>It serves as an effective prompting strategy, improving frontier models by up to 19.2pp over ReAct on long-horizon tasks.
  arXiv  Detail & Related papers  (2025-11-10T17:30:08Z)
• Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction [18.302602011055775]
  We propose DiffuMIN (Diffusion-driven Multi-Interest Network) to model long-term user behaviors.<n>We show that DiffuMIN increased CTR by 1.52% and CPM by 1.10% in online A/B testing.
  arXiv  Detail & Related papers  (2025-08-21T07:10:01Z)
• Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models [0.9262403397108375]
  Large language models (LLMs) have demonstrated potential serendipity in recommendation, thanks to their extensive world knowledge and superior reasoning capabilities.<n>We propose a method that leverages llm to dynamically construct user knowledge graphs, thereby enhancing the serendipity of recommendation systems.
  arXiv  Detail & Related papers  (2025-08-06T02:52:09Z)
• EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors [13.436303786475348]
  We propose to enhance the fidelity and trustworthiness of the data-driven Generative Adversarial Learning (GAIL) method by blending it with the Exploration and Preferential Return EPR model.<n>The core idea of our EPR-GAIL framework is to model user consumption behaviors as a complex EPR decision process.<n>Experiments on two real-world datasets of user consumption behaviors on an online platform demonstrate that the EPR-GAIL framework outperforms the best state-of-the-art baseline by over 19% in terms of data fidelity.
  arXiv  Detail & Related papers  (2025-03-09T01:56:42Z)
• LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation [54.396000434574454]
  We propose a novel multi-interest SR framework combining implicit behavioral and explicit semantic perspectives.<n>It includes two modules: the Implicit Behavioral Interest Module and the Explicit Semantic Interest Module.<n>Experiments on four real-world datasets validate the framework's effectiveness and practicality.
  arXiv  Detail & Related papers  (2024-11-14T13:00:23Z)
• Retrieval Augmentation via User Interest Clustering [57.63883506013693]
  Industrial recommender systems are sensitive to the patterns of user-item engagement. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference. Our approach has been deployed in multiple products at Meta, facilitating short-form video related recommendation.
  arXiv  Detail & Related papers  (2024-08-07T16:35:10Z)
• Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning [70.22819290458581]
  Reinforcement learning with human feedback (RLHF) is a widely adopted approach in current large language model pipelines. Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries.
  arXiv  Detail & Related papers  (2024-07-02T10:09:19Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation [28.29435760797856]
  We propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N) -- for click-through rate prediction in TIR scenarios. We design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. We evaluate our method on several offline and real-world industrial datasets.
  arXiv  Detail & Related papers  (2024-01-15T15:27:24Z)
• Unified Embedding Based Personalized Retrieval in Etsy Search [0.206242362470764]
  We propose learning a unified embedding model incorporating graph, transformer and term-based embeddings end to end. Our personalized retrieval model significantly improves the overall search experience, as measured by a 5.58% increase in search purchase rate and a 2.63% increase in site-wide conversion rate.
  arXiv  Detail & Related papers  (2023-06-07T23:24:50Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning [88.34958680436552]
  We present an exploration method specifically for preference-based reinforcement learning algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms.
  arXiv  Detail & Related papers  (2022-05-24T23:22:10Z)

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.