Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation

• URL: http://arxiv.org/abs/2409.14810v1
• Date: Mon, 23 Sep 2024 08:39:07 GMT
• Title: Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
• Authors: Li Li, Mingyue Cheng, Zhiding Liu, Hao Zhang, Qi Liu, Enhong Chen,
• Abstract summary: We propose a sequential recommendation algorithm based on a pre-trained language model and knowledge distillation. The proposed algorithm enhances recommendation accuracy and provide timely recommendation services.
• Score: 51.25461871988366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ neural networks to extract features of user interests, achieving good performance. However, due to the recommendation system datasets sparsity, these algorithms often employ small-scale network frameworks, resulting in weaker generalization capability. Recently, a series of sequential recommendation algorithms based on large pre-trained language models have been proposed. Nonetheless, given the real-time demands of recommendation systems, the challenge remains in applying pre-trained language models for rapid recommendations in real scenarios. To address this, we propose a sequential recommendation algorithm based on a pre-trained language model and knowledge distillation. The key of proposed algorithm is to transfer pre-trained knowledge across domains and achieve lightweight inference by knowledge distillation. The algorithm operates in two stages: in the first stage, we fine-tune the pre-trained language model on the recommendation dataset to transfer the pre-trained knowledge to the recommendation task; in the second stage, we distill the trained language model to transfer the learned knowledge to a lightweight model. Extensive experiments on multiple public recommendation datasets show that the proposed algorithm enhances recommendation accuracy and provide timely recommendation services.

Related papers

• 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)
• Predictive accuracy of recommender algorithms [0.0]
  A variety of algorithms for recommender systems have been developed and refined including applications of deep learning neural networks. Recent research reports point to a need to perform carefully controlled experiments to gain insights about the relative accuracy of different recommender algorithms. This investigation used publicly available sources of ratings data with a suite of three conventional recommender algorithms and two deep learning (DL) algorithms in controlled experiments to assess their comparative accuracy.
  arXiv  Detail & Related papers  (2024-06-26T19:25:07Z)
• Informed Spectral Normalized Gaussian Processes for Trajectory Prediction [0.0]
  We propose a novel regularization-based continual learning method for SNGPs. Our proposal builds upon well-established methods and requires no rehearsal memory or parameter expansion. We apply our informed SNGP model to the trajectory prediction problem in autonomous driving by integrating prior drivability knowledge.
  arXiv  Detail & Related papers  (2024-03-18T17:05:24Z)
• Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling [18.297332953450514]
  We propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations.
  arXiv  Detail & Related papers  (2023-09-19T08:54:47Z)
• The Wisdom of Hindsight Makes Language Models Better Instruction Followers [84.9120606803906]
  Reinforcement learning has seen wide success in finetuning large language models to better align with instructions via human feedback. In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner. We propose Hindsight Instruction Relabeling (HIR), a novel algorithm for aligning language models with instructions.
  arXiv  Detail & Related papers  (2023-02-10T12:16:38Z)
• Personalized Prompt Learning for Explainable Recommendation [46.69922623691051]
  We propose two training strategies: sequential tuning and recommendation as regularization. Our continuous prompt learning approach consistently outperforms strong baselines on three datasets of explainable recommendation.
  arXiv  Detail & Related papers  (2022-02-15T12:53:52Z)
• D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning [0.3058685580689604]
  This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture. The proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.
  arXiv  Detail & Related papers  (2021-10-28T13:11:29Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.