Prompt Tuning as User Inherent Profile Inference Machine

• URL: http://arxiv.org/abs/2408.06577v1
• Date: Tue, 13 Aug 2024 02:25:46 GMT
• Title: Prompt Tuning as User Inherent Profile Inference Machine
• Authors: Yusheng Lu, Zhaocheng Du, Xiangyang Li, Xiangyu Zhao, Weiwen Liu, Yichao Wang, Huifeng Guo, Ruiming Tang, Zhenhua Dong, Yongrui Duan,
• Abstract summary: We propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. A profile quantization codebook bridges the modality gap by profile embeddings into collaborative IDs. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms.
• Score: 53.78398656789463
• License:
• Abstract: Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capabilities. However, LLMs face challenges like unstable instruction compliance, modality gaps, and high inference latency, leading to textual noise and limiting their effectiveness in recommender systems. To address these challenges, we propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. It integrates the causal relationship between user profiles and behavior sequences into LLMs' prompts. And employs expectation maximization to infer the embedded latent profile, minimizing textual noise by fixing the prompt template. Furthermore, A profile quantization codebook bridges the modality gap by categorizing profile embeddings into collaborative IDs, which are pre-stored for online deployment. This improves time efficiency and reduces memory usage. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms. Additional tests and case studies confirm its effectiveness, robustness, and transferability.

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)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• On the Worst Prompt Performance of Large Language Models [93.13542053835542]
  Performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts. We introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries. Experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance.
  arXiv  Detail & Related papers  (2024-06-08T13:40:38Z)
• Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning [36.88126051792774]
  Personalization in large language models (LLMs) is increasingly important. One PEFT Per User (OPPU) employs personalized parameter-efficient fine-tuning (PEFT) modules to store user-specific behavior patterns and preferences. OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark.
  arXiv  Detail & Related papers  (2024-02-06T21:03:52Z)
• Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models [11.950478880423733]
  Personalization is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. We propose a novel summary-augmented personalization with task-aware user summaries generated by LLMs.
  arXiv  Detail & Related papers  (2023-10-30T23:40:41Z)
• Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL [62.824464372594576]
  We aim to enhance arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization. We introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data.
  arXiv  Detail & Related papers  (2023-09-13T01:12:52Z)
• Explainable Active Learning for Preference Elicitation [0.0]
  We employ Active Learning (AL) to solve the addressed problem with the objective of maximizing information acquisition with minimal user effort. AL operates for selecting informative data from a large unlabeled set to inquire an oracle to label them. It harvests user feedback (given for the system's explanations on the presented items) over informative samples to update an underlying machine learning (ML) model.
  arXiv  Detail & Related papers  (2023-09-01T09:22:33Z)
• Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization [89.04537372465612]
  Socratic pretraining is a question-driven, unsupervised pretraining objective designed to improve controllability in summarization tasks. Our results show that Socratic pretraining cuts task-specific labeled data requirements in half.
  arXiv  Detail & Related papers  (2022-12-20T17:27:10Z)
• Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction [15.97120392599086]
  We propose textbfM (textbfSampling-based textbfDeep textbfModeling), a simple yet effective sampling-based end-to-end approach for modeling long-term user behaviors. We show theoretically and experimentally that the proposed method performs on par with standard attention-based models on modeling long-term user behaviors.
  arXiv  Detail & Related papers  (2022-05-20T15:20:52Z)
• Denoising User-aware Memory Network for Recommendation [11.145186013006375]
  We propose a novel CTR model named denoising user-aware memory network (DUMN) DUMN uses the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback. Experiments on two real e-commerce user behavior datasets show that DUMN has a significant improvement over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-07-12T14:39:36Z)

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.