Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation

• URL: http://arxiv.org/abs/2312.16262v1
• Date: Tue, 26 Dec 2023 08:24:24 GMT
• Title: Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation
• Authors: Zhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu, Hui Fang, Yew-Soon Ong, Wenyuan Liu
• Abstract summary: This paper explores two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference based on users' interactions in a session. We introduce a dynamic in-context learning paradigm, which enables ChatGPT to seek tailored and dynamic lessons from closely related sessions. We develop (1) a self-correction strategy to foster mutual improvement in both tasks without supervision signals; and (2) an auto-feedback mechanism to recurrently offer dynamic supervision.
• Score: 33.25497578184437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Product bundling has evolved into a crucial marketing strategy in e-commerce. However, current studies are limited to generating (1) fixed-size or single bundles, and most importantly, (2) bundles that do not reflect consistent user intents, thus being less intelligible or useful to users. This paper explores two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference based on users' interactions in a session, leveraging the logical reasoning capability of large language models. We introduce a dynamic in-context learning paradigm, which enables ChatGPT to seek tailored and dynamic lessons from closely related sessions as demonstrations while performing tasks in the target session. Specifically, it first harnesses retrieval augmented generation to identify nearest neighbor sessions for each target session. Then, proper prompts are designed to guide ChatGPT to perform the two tasks on neighbor sessions. To enhance reliability and mitigate the hallucination issue, we develop (1) a self-correction strategy to foster mutual improvement in both tasks without supervision signals; and (2) an auto-feedback mechanism to recurrently offer dynamic supervision based on the distinct mistakes made by ChatGPT on various neighbor sessions. Thus, the target session can receive customized and dynamic lessons for improved performance by observing the demonstrations of its neighbor sessions. Finally, experimental results on three real-world datasets verify the effectiveness of our methods on both tasks. Additionally, the inferred intents can prove beneficial for other intriguing downstream tasks, such as crafting appealing bundle names.

Related papers

• Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID [82.12123628480371]
  Unsupervised person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations for model learning. Previous methods unify pseudo-labels of cross-modality images through label association algorithms and then design contrastive learning framework for global feature learning. We propose a Semantic-Aligned Learning with Collaborative Refinement (SALCR) framework, which builds up objective for specific fine-grained patterns emphasized by each modality.
  arXiv  Detail & Related papers  (2025-04-27T13:58:12Z)
• Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training [60.38082979765664]
  CPRec is an All-domain Continual Pre-Training framework for Recommendation. It holistically align LLMs with universal user behaviors through the continual pre-training paradigm. We conduct experiments on five real-world datasets from two distinct platforms.
  arXiv  Detail & Related papers  (2025-04-11T20:01:25Z)
• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts. With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS) Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements. High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• LIBER: Lifelong User Behavior Modeling Based on Large Language Models [42.045535303737694]
  We propose Lifelong User Behavior Modeling (LIBER) based on large language models. LIBER has been deployed on Huawei's music recommendation service and achieved substantial improvements in users' play count and play time by 3.01% and 7.69%.
  arXiv  Detail & Related papers  (2024-11-22T03:43:41Z)
• Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling [51.38330727868982]
  Bidirectional Decoding (BID) is a test-time inference algorithm that bridges action chunking with closed-loop operations. We show that BID boosts the performance of two state-of-the-art generative policies across seven simulation benchmarks and two real-world tasks.
  arXiv  Detail & Related papers  (2024-08-30T15:39:34Z)
• Spatio-Temporal Context Prompting for Zero-Shot Action Detection [13.22912547389941]
  We propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction. To address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism. Our method achieves superior results compared to previous approaches and can be further extended to multi-action videos.
  arXiv  Detail & Related papers  (2024-08-28T17:59:05Z)
• ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation [24.743048965822297]
  This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2) IT2 is designed to ensure consistent predictions from peer LiDAR representations, thereby improving the perturbation effectiveness in consistency learning. Results on public benchmarks show that our approach achieves remarkable improvements over the previous state-of-the-art (SOTA) methods in the field.
  arXiv  Detail & Related papers  (2024-07-09T18:26:53Z)
• Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning [49.262407095098645]
  Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. Users tend to adjust their job preferences to secure employment opportunities continually. We propose a novel session-based framework, BISTRO, to model user preference through fusion learning of semantic and behavioral information.
  arXiv  Detail & Related papers  (2024-06-24T14:38:04Z)
• Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation [26.58882747016846]
  Session-based recommendation (SBR) methods often rely on user behavior data, which can struggle with the sparsity of session data, limiting performance. We propose an LLM-enhanced SBR framework that integrates semantic and behavioral signals from multiple views. In the first stage, we use multi-view prompts to infer latent user intentions at the session semantic level, supported by an intent localization module to alleviate hallucinations. In the second stage, we align and unify these semantic inferences with behavioral representations, effectively merging insights from both large and small models.
  arXiv  Detail & Related papers  (2024-02-21T14:38:02Z)
• Large Language Models for Intent-Driven Session Recommendations [34.64421003286209]
  We introduce a novel ISR approach, utilizing the advanced reasoning capabilities of large language models (LLMs) We introduce an innovative prompt optimization mechanism that iteratively self-reflects and adjusts prompts. This new paradigm empowers LLMs to discern diverse user intents at a semantic level, leading to more accurate and interpretable session recommendations.
  arXiv  Detail & Related papers  (2023-12-07T02:25:14Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)
• USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval [115.28586222748478]
  Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant to the given query from the other modality. Existing approaches typically suffer from two major limitations.
  arXiv  Detail & Related papers  (2023-01-17T12:42:58Z)
• Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following [26.386772715777223]
  This paper proposes using multimodal generative models for semi-supervised learning in the instruction following tasks. The models learn a shared representation of the paired data, and enable semi-supervised learning by reconstructing unpaired data. Experiments on BabyAI and Room-to-Room environments show that the proposed method improves the performance of instruction following by leveraging unpaired data.
  arXiv  Detail & Related papers  (2022-12-29T03:23:43Z)
• SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning [5.346468677221906]
  Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions. We propose SR-GCL, a novel contrastive learning framework for a session-based recommendation.
  arXiv  Detail & Related papers  (2022-09-22T06:18:20Z)
• Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning [15.729812221628382]
  We introduce a simple neural encoder architecture that can be trained using an unsupervised contrastive learning objective. We show that when built on top of recent self-supervised audio representations, this method can be applied iteratively and yield competitive SSE.
  arXiv  Detail & Related papers  (2022-04-11T14:28:01Z)
• Intent Contrastive Learning for Sequential Recommendation [86.54439927038968]
  We introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm.
  arXiv  Detail & Related papers  (2022-02-05T09:24:13Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Unsupervised Conversation Disentanglement through Co-Training [30.304609312675186]
  We explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence.
  arXiv  Detail & Related papers  (2021-09-07T17:05:18Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [66.04457457299218]
  We propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model.
  arXiv  Detail & Related papers  (2020-05-06T14:17:05Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.