Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning

• URL: http://arxiv.org/abs/2411.11225v2
• Date: Mon, 25 Nov 2024 06:07:41 GMT
• Title: Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning
• Authors: Yunze Luo, Yuezihan Jiang, Yinjie Jiang, Gaode Chen, Jingchi Wang, Kaigui Bian, Peiyi Li, Qi Zhang,
• Abstract summary: We propose a model-agnostic recommendation algorithm called Popularity-Aware Meta-learning (PAM) to address the item cold-start problem. PAM divides incoming data into different meta-learning tasks by predefined item popularity thresholds. These task-fixing design significantly reduces additional computation and storage costs compared to offline methods.
• Score: 14.83192161148111
• License:
• Abstract: With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In both online and offline recommendation, the cold-start problem due to interaction sparsity has been affecting the recommendation effect of cold-start items, which is also known as the long-tail problem of item distribution. Many cold-start scheme based on fine-tuning or knowledge transferring shows excellent performance on offline recommendation. Yet, these schemes are infeasible for online recommendation on streaming data pipelines due to different training method, computational overhead and time constraints. Inspired by the above questions, we propose a model-agnostic recommendation algorithm called Popularity-Aware Meta-learning (PAM), to address the item cold-start problem under streaming data settings. PAM divides the incoming data into different meta-learning tasks by predefined item popularity thresholds. The model can distinguish and reweight behavior-related and content-related features in each task based on their different roles in different popularity levels, thus adapting to recommendations for cold-start samples. These task-fixing design significantly reduces additional computation and storage costs compared to offline methods. Furthermore, PAM also introduced data augmentation and an additional self-supervised loss specifically designed for low-popularity tasks, leveraging insights from high-popularity samples. This approach effectively mitigates the issue of inadequate supervision due to the scarcity of cold-start samples. Experimental results across multiple public datasets demonstrate the superiority of our approach over other baseline methods in addressing cold-start challenges in online streaming data scenarios.

Related papers

• Language-Model Prior Overcomes Cold-Start Items [14.370472820496802]
  The growth ofRecSys is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. Existing solutions for the cold-start problem, such as content-based recommenders and hybrid methods, leverage item metadata to determine item similarities. This paper introduces a novel approach for cold-start item recommendation that utilizes the language model (LM) to estimate item similarities.
  arXiv  Detail & Related papers  (2024-11-13T22:45:52Z)
• Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
  We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
  arXiv  Detail & Related papers  (2024-08-21T06:48:38Z)
• Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems [10.133475523630139]
  Cold-start recommendation is one of the major challenges faced by recommender systems (RS) In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.
  arXiv  Detail & Related papers  (2023-09-27T13:31:43Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation [4.379304291229695]
  We propose a novel sequential recommendation framework based on gradient-based meta-learning. Our work is the first to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios.
  arXiv  Detail & Related papers  (2023-02-28T15:18:42Z)
• Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations [92.47380209981348]
  We propose a Diverse Preference Augmentation framework with multiple source domains based on meta-learning. We generate diverse ratings in a new domain of interest to handle overfitting on the case of sparse interactions. These ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability.
  arXiv  Detail & Related papers  (2022-04-01T10:10:50Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• Privileged Graph Distillation for Cold Start Recommendation [57.918041397089254]
  The cold start problem in recommender systems requires recommending to new users (items) based on attributes without any historical interaction records. We propose a privileged graph distillation model(PGD) Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item.
  arXiv  Detail & Related papers  (2021-05-31T14:05:27Z)
• Cold-start Sequential Recommendation via Meta Learner [10.491428090228768]
  We propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user.
  arXiv  Detail & Related papers  (2020-12-10T05:23:13Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)

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.