Item Level Exploration Traffic Allocation in Large-scale Recommendation Systems

• URL: http://arxiv.org/abs/2505.09033v1
• Date: Wed, 14 May 2025 00:05:04 GMT
• Title: Item Level Exploration Traffic Allocation in Large-scale Recommendation Systems
• Authors: Dong Wang, Junyi Jiao, Arnab Bhadury, Yaping Zhang, Mingyan Gao,
• Abstract summary: This paper contributes to addressing the item cold start problem in large-scale recommender systems.<n>We propose an exploration system designed to efficiently allocate impressions to fresh items.
• Score: 7.207863744953401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper contributes to addressing the item cold start problem in large-scale recommender systems, focusing on how to efficiently gain initial visibility for newly ingested content. We propose an exploration system designed to efficiently allocate impressions to these fresh items. Our approach leverages a learned probabilistic model to predict an item's discoverability, which then informs a scalable and adaptive traffic allocation strategy. This system intelligently distributes exploration budgets, optimizing for the long-term benefit of the recommendation platform. The impact is a demonstrably more efficient cold-start process, leading to a significant increase in the discoverability of new content and ultimately enriching the item corpus available for exploitation, as evidenced by its successful deployment in a large-scale production environment.

Related papers

• Where to Explore: A Reach and Cost-Aware Approach for Unbiased Data Collection in Recommender Systems [4.013613514540094]
  This paper introduces an approach for delivering content-level exploration safely and efficiently by optimizing its placement based on reach and opportunity cost.<n> Deployed on a large-scale streaming platform with over 100 million monthly active users, our approach identifies scroll-depth regions with lower engagement.<n>Our method complements existing intra-row diversification and bandit-based exploration techniques by introducing a deployable, behaviorally informed mechanism for surfacing exploratory content at scale.
  arXiv  Detail & Related papers  (2025-12-11T04:03:44Z)
• LLM Reasoning for Cold-Start Item Recommendation [11.180516000970528]
  Large Language Models (LLMs) have shown significant potential for improving recommendation systems.<n>We propose novel reasoning strategies designed for cold-start item recommendations within the Netflix domain.<n>Our method utilizes the advanced reasoning capabilities of LLMs to effectively infer user preferences.
  arXiv  Detail & Related papers  (2025-11-23T03:22:53Z)
• Item-centric Exploration for Cold Start Problem [7.132196640884619]
  We argue that the traditional focus on finding the "best item for a user" can inadvertently obscure the ideal audience for nascent content.<n>We introduce the concept of item-centric recommendations, shifting the paradigm to identify the optimal users for new items.
  arXiv  Detail & Related papers  (2025-07-12T23:22:23Z)
• Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions [50.44719434877687]
  Expert demonstrations and simulators can reset to arbitrary states.<n>We find that using a notion of safety to inform the choice of this auxiliary distribution significantly accelerates learning.
  arXiv  Detail & Related papers  (2025-07-07T01:54:05Z)
• Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation [85.52251362906418]
  This tutorial explores two primary approaches for integrating large language models (LLMs)<n>It provides a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions.<n>Key topics include data quality, scaling laws, user behavior mining, and efficiency in training and inference.
  arXiv  Detail & Related papers  (2025-02-19T14:48:25Z)
• Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms [68.51708490104687]
  We show that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on platforms.
  arXiv  Detail & Related papers  (2024-10-31T07:19:22Z)
• Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems [12.913561033531249]
  This paper develops a re-ranking approach in dynamic settings with fair-exposure optimization driven by strategic agents. Joint objectives ensure the performance of recommendations while enhancing the visibility of tail items. We have substantiated the effectiveness and dominance of the proposed method especially on unveiling the potential of tail items.
  arXiv  Detail & Related papers  (2024-06-25T11:41:50Z)
• LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge.<n>We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems [79.76519917171261]
  This paper addresses the computational overhead and resource inefficiency prevalent in Sequential Recommender Systems (SRSs)<n>We introduce an innovative approach combining pruning methods with advanced model designs.<n>Our principal contribution is the development of a Data-aware Neural Architecture Search for Recommender System (DNS-Rec)
  arXiv  Detail & Related papers  (2024-02-01T07:22:52Z)
• Impression-Aware Recommender Systems [53.48892326556546]
  We present a systematic literature review on recommender systems using impressions.<n>We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Continuous Input Embedding Size Search For Recommender Systems [60.89189829112067]
  Continuous input embedding size search (CIESS) is a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from.<n> CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs.<n> experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets.
  arXiv  Detail & Related papers  (2023-04-07T06:46:37Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• Exploring Customer Price Preference and Product Profit Role in Recommender Systems [0.4724825031148411]
  We show the impact of manipulating profit awareness of a recommender system. We propose an adjustment of a predicted ranking for score-based recommender systems. In the experiments, we show the ability to improve both the precision and the generated recommendations' profit.
  arXiv  Detail & Related papers  (2022-03-13T12:08:06Z)
• Deep Exploration for Recommendation Systems [14.937000494745861]
  We develop deep exploration methods for recommendation systems. In particular, we formulate recommendation as a sequential decision problem. Our experiments are carried out with high-fidelity industrial-grade simulators.
  arXiv  Detail & Related papers  (2021-09-26T06:54:26Z)
• Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems [1.52292571922932]
  We introduce an exploitation-exploration motivated variational auto-encoder (XploVAE) to collaborative filtering. To facilitate personalized recommendations, we construct user-specific subgraphs, which contain the first-order proximity capturing observed user-item interactions. A hierarchical latent space model is utilized to learn the personalized item embedding for a given user, along with the population distribution of all user subgraphs.
  arXiv  Detail & Related papers  (2020-06-05T17:37:46Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

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.