Traditional recommender systems typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a "tutorial" presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.
论文检索
输入标题、作者或关键词，从 642 篇学术成果中精准定位
With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces emerging challenges, particularly in terms of biases and unfairness, which may threaten the information ecosystem. In this paper, we present a comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of LLMs. We first unify bias and unfairness issues as distribution mismatch problems, providing a groundwork for categorizing various mitigation strategies through distribution alignment. Subsequently, we systematically delve into the specific bias and unfairness issues arising from three critical stages of LLMs integration into IR systems: data collection, model development, and result evaluation. In doing so, we meticulously review and analyze recent literature, focusing on the definitions, characteristics, and corresponding mitigation strategies associated with these issues. Finally, we identify and highlight some open problems and challenges for future work, aiming to inspire researchers and stakeholders in the IR field and beyond to better understand and mitigate bias and unfairness issues of IR in this LLM era. We also consistently maintain a GitHub repository for the relevant papers and resources in this rising direction at https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey.
Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution. Additionally, we assess the feasibility of detection from a theoretical perspective and propose novel research directions to address the current limitations in this domain.
Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications. Large Language Models (LLMs) have demonstrated impressive capabilities by advancing state-of-the-art on many language-based benchmarks. Their ability to process and understand natural language open exciting possibilities in various domains. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with LLMs remains an understudied problem that has recently gained more attention. This tutorial builds upon recent advances in expressing reasoning problems through the lens of tasks on graph data. The first part of the tutorial will provide an in-depth discussion of techniques for representing graphs as inputs to LLMs. The second, hands-on, portion will demonstrate these techniques in a practical setting. As a learning outcome of participating in the tutorial, participants will be able to analyze graphs either on free-tier Colab or their local machines with the help of LLMs.
AI projects often face the challenge of limited access to meaningful amounts of training data. In traditional approaches, collecting data in a central location can be problematic, especially in industry settings with sensitive and distributed data. However, there is a solution -"moving the computation to the data" through Federated Learning. Federated Learning, a distributed machine learning approach, offers a promising solution by enabling model training across devices. It is a data minimization approach where direct access to data is not required. Furthermore, federated learning can be combined with techniques like differential privacy, secure aggregation, homomorphic encryption, and others, to further enhance privacy protection. In this hands-on tutorial, we delve into the realm of privacy-preserving machine learning using federated learning, leveraging the Flower framework which is specifically designed to simplify the process of building federated learning systems, as our primary tool. Moreover, we present the foundations of federated learning, explore how different techniques can enhance its privacy aspects, how it is being used in real-world settings today and a series of practical, hands-on code examples that showcase how you can federate any AI project with Flower, an open-source framework for all-this federated.
Large language models (LLMs) have been rapidly adopted, as showcased by ChatGPT's overnight popularity, and are integrated in products used by millions of people every day, such as search engines and productivity suites. Yet the societal impact of LLMs, encompassing both benefits and harms, is not well understood. Inspired by cybersecurity practices, red-teaming is emerging as a technique to uncover model vulnerabilities. Despite increasing attention from industry, academia, and government centered around red-teaming LLMs, such efforts are still limited in the diversity of the red-teaming focus, approaches and participants. Importantly, given that LLMs are becoming ubiquitous, it is imperative that red-teaming efforts are scaled out to include large segments of the research, practitioners and the people whom are directly affected by the deployment of these systems. The goal of this tutorial is two fold. First, we introduce the topic of LLM red-teaming by reviewing the state of the art for red-teaming practices, from participatory events to automatic AI-focused approaches, exposing the gaps in both the techniques and coverage of the targeted harms. Second, we plan to engage the audience in a hands-on and interactive exercise in LLM red-teaming to showcase the ease (or difficulty) of exposing model vulnerabilities, contingent on both the targeted harm and model capabilities. We believe that the KDD community of researchers and practitioners are in a unique position to address the existing gaps in red-teaming approaches, given their longstanding research and practice of extracting knowledge from data.
Machine Learning for Data Streams has been an important area of research since the late 1990s, and its use in industry has grown significantly over the last few years. However, there is still a gap between the cutting-edge research and the tools that are readily available, which makes it challenging for practitioners, including experienced data scientists, to implement and evaluate these methods in this complex domain. Our tutorial aims to bridge this gap with a dual focus. We will discuss important research topics, such as partially delayed labeled streams, while providing practical demonstrations of their implementation and assessment using CapyMOA, an open-source library that provides efficient algorithm implementations through a high-level Python API. Source code is available in https://github.com/adaptive-machine-learning/CapyMOA while the accompanying tutorials and installation guide are available in https://capymoa.org/.
To improve Large Language Model (LLM) performance on domain specific applications, ML developers often leverage Retrieval Augmented Generation (RAG) and LLM Fine-Tuning. RAG extends the capabilities of LLMs to specific domains or an organization's internal knowledge base, without the need to retrain the model. On the other hand, Fine-Tuning approach updates LLM weights with domain-specific data to improve performance on specific tasks. The fine-tuned model is particularly effective to systematically learn new comprehensive knowledge in a specific domain that is not covered by the LLM pre-training. This tutorial walks through the RAG and Fine-Tuning techniques, discusses the insights of their advantages and limitations, and provides best practices of adopting the methodologies for the LLM tasks and use cases. The hands-on labs demonstrate the advanced techniques to optimize the RAG and fine-tuned LLM architecture that handles domain specific LLM tasks. The labs in the tutorial are designed by using a set of open-source python libraries to implement the RAG and fine-tuned LLM architecture.
In the foundation models era, efficiently processing multi-modal data is crucial. This tutorial covers key techniques for multi-modal data processing and introduces the open-source Data-Juicer system, designed to tackle the complexities of data variety, quality, and scale. Participants will learn how to use Data-Juicer's operators and tools for formatting, mapping, filtering, deduplicating, and selecting multi-modal data efficiently and effectively. They will also be familiar with the Data-Juicer Sandbox Lab, where users can easily experiment with diverse data recipes that represent methodical sequences of operators and streamline the creation of scalable data processing pipelines. This experience solidifies the concepts discussed, as well as provides a space for innovation and exploration, highlighting how data recipes can be optimized and deployed in high-performance distributed environments. By the end of this tutorial, attendees will be equipped with the practical knowledge and skills to navigate the multi-modal data processing for foundation models. They will leave with actionable knowledge with an industrial open-source system and an enriched perspective on the importance of high-quality data in AI, poised to implement sustainable and scalable solutions in their projects. The system and related materials are available at https://github.com/modelscope/data-juicer.
This tutorial offers a comprehensive guide on using multi-armed bandit (MAB) algorithms to improve Large Language Models (LLMs). As Natural Language Processing (NLP) tasks grow, efficient and adaptive language generation systems are increasingly needed. MAB algorithms, which balance exploration and exploitation under uncertainty, are promising for enhancing LLMs. The tutorial covers foundational MAB concepts, including the exploration-exploitation trade-off and strategies like epsilon-greedy, UCB (Upper Confidence Bound), and Thompson Sampling. It then explores integrating MAB with LLMs, focusing on designing architectures that treat text generation options as arms in a bandit problem. Practical aspects like reward design, exploration policies, and scalability are discussed. Real-world case studies demonstrate the benefits of MAB-augmented LLMs in content recommendation, dialogue generation, and personalized content creation, showing how these techniques improve relevance, diversity, and user engagement.
Time series classification and regression are rapidly evolving fields that find areas of application in all domains of machine learning and data science. This hands on tutorial will provide an accessible overview of the recent research in these fields, using code examples to introduce the process of implementing and evaluating an estimator. We will show how to easily reproduce published results and how to compare a new algorithm to state-of-the-art. Finally, we will work through real world examples from the field of Electroencephalogram (EEG) classification and regression. EEG machine learning tasks arise in medicine, brain-computer interface research and psychology. We use these problems to how to compare algorithms on problems from a single domain and how to deal with data with different characteristics, such as missing values, unequal length and high dimensionality. The latest advances in the fields of time series classification and regression are all available through the aeon toolkit, an open source, scikit-learn compatible framework for time series machine learning which we use to provide our code examples.
In recommendation systems, the timely delivery of new content to their relevant audiences is critical for generating a growing and high quality collection of content for all users. The nature of this problem requires retrieval models to be able to make inferences in real time and with high relevance. There are two specific challenges for cold start contents. First, the information loss problem in a standard Two Tower model, due to the limited feature interactions between the user and item towers, is exacerbated for cold start items due to training data sparsity. Second, the huge volume of user-generated content in industry applications today poses a big bottleneck in the end-to-end latency of recommending new content. To overcome the two challenges, we propose a novel architecture, the Item History Model (IHM). IHM directly injects user-interaction information into the item tower to overcome information loss. In addition, IHM incorporates an inductive structure using attention-based pooling to eliminate the need for recurring training, a key bottleneck for the real-timeness. On both public and industry datasets, we demonstrate that IHM can not only outperform baselines in recommending cold start contents, but also achieves SoTA real-timeness in industry applications.
Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns between the query image and gallery images. In this study, we show that pure image-to- image matching suffers from false positives caused by matching to local visual patterns. To alleviate this issue, we propose to leverage recent advances in vision-language pretraining research. Specifically, we introduce additional image-text alignment losses into deep metric learning, which serve as constraints to the image-to-image matching loss. With additional alignments between the text (e.g., product title) and image pairs, the model can learn concepts from both modalities explicitly, which avoids matching low-level visual features. We progressively develop two variants, a 3-tower and a 4-tower model, where the latter takes one more short text query input. Through extensive experiments, we show that this change leads to a substantial improvement to the image to image matching problem. We further leveraged this model for multimodal search, which takes both image and reformulation text queries to improve search quality. Both offline and online experiments show strong improvements on the main metrics. Specifically, we see 4.95% relative improvement on image matching click through rate with the 3-tower model and 1.13% further improvement from the 4-tower model.
Online controlled experiments play a crucial role in enabling data-driven decisions across a wide range of companies. Variance reduction is an effective technique to improve the sensitivity of experiments, achieving higher statistical power while using fewer samples and shorter experimental periods. However, typical variance reduction methods (e.g., regression-adjusted estimators) are built upon the intuitional assumption of Gaussian distributions and cannot properly characterize the real business metrics with heavy-tailed distributions. Furthermore, outliers diminish the correlation between pre-experiment covariates and outcome metrics, greatly limiting the effectiveness of variance reduction. In this paper, we develop a novel framework that integrates the Student's t-distribution with machine learning tools to fit heavy-tailed metrics and construct a robust average treatment effect estimator in online controlled experiments, which we call STATE. By adopting a variational EM method to optimize the loglikehood function, we can infer a robust solution that greatly eliminates the negative impact of outliers and achieves significant variance reduction. Moreover, we extend the STATE method from count metrics to ratio metrics by utilizing linear transformation that preserves unbiased estimation, whose variance reduction is more complex but less investigated in existing works. Finally, both simulations on synthetic data and long-term empirical results on Meituan experiment platform demonstrate the effectiveness of our method. Compared with the state-of-the-art estimators (CUPAC/MLRATE), STATE achieves over 50% variance reduction, indicating it can reach the same statistical power with only half of the observations, or half the experimental duration.
Marketing optimization plays an important role to enhance user engagement in online Internet platforms. Existing studies usually formulate this problem as a budget allocation problem and solve it by utilizing two fully decoupled stages, i.e., machine learning (ML) and operation research (OR). However, the learning objective in ML does not take account of the downstream optimization task in OR, which causes that the prediction accuracy in ML may be not positively related to the decision quality. Decision Focused Learning (DFL) integrates ML and OR into an end-to-end framework, which takes the objective of the downstream task as the decision loss function and guarantees the consistency of the optimization direction between ML and OR. However, deploying DFL in marketing is non-trivial due to multiple technological challenges. Firstly, the budget allocation problem in marketing is a 0-1 integer stochastic programming problem and the budget is uncertain and fluctuates a lot in real-world settings, which is beyond the general problem background in DFL. Secondly, the counterfactual in marketing causes that the decision loss cannot be directly computed and the optimal solution can never be obtained, both of which disable the common gradient-estimation approaches in DFL. Thirdly, the OR solver is called frequently to compute the decision loss during model training in DFL, which produces huge computational cost and cannot support large-scale training data. In this paper, we propose a decision focused causal learning framework (DFCL) for direct counterfactual marketing optimization, which overcomes the above technological challenges. Both offline experiments and online A/B testing demonstrate the effectiveness of DFCL over the state-of-the-art methods. Currently, DFCL has been deployed in several marketing scenarios in Meituan, one of the largest online food delivery platform in the world.
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.
Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy
PDF ↗As Large Language Models (LLMs) have made significant advancements across various tasks, such as question answering, translation, text summarization, and dialogue systems, the need for accuracy in information becomes crucial, especially for serious financial products serving billions of users like Alipay. However, for a real-world product serving millions of users, the inference speed of LLMs becomes a critical factor compared to a mere experimental model. Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our LLM-based scenarios, with lossless generation accuracy. In the traditional inference process, each token is generated sequentially by the LLM, leading to a time consumption proportional to the number of generated tokens. To enhance this process, our framework, named lookahead, introduces a multi-branch strategy. Instead of generating a single token at a time, we propose a Trie-based retrieval and verification mechanism to be able to accept several tokens at a forward step. Our strategy offers two distinct advantages: (1) it guarantees absolute correctness of the output, avoiding any approximation algorithms, and (2) the worst-case performance of our approach could be comparable with the performance of the conventional process. We conduct extensive experiments to demonstrate the significant improvements achieved by applying our inference acceleration framework. Our framework has been widely deployed in Alipay since April 2023, and obtained remarkable 2.66x to 6.26x speedup. Our code is available at https://github.com/alipay/PainlessInferenceAcceleration.
Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model. However, the application of explanation supervision to higher-dimensional data, such as 3D medical images, remains an under-explored domain. Challenges associated with supervising visual explanations in the presence of an additional dimension include: 1) spatial correlation changed, 2) lack of direct 3D annotations, and 3) uncertainty varies across different parts of the explanation. To address these challenges, we propose a Dynamic Uncertainty-aware Explanation supervision (DUE\footnoteCode available at: https://github.com/AlexQilong/DUE.) framework for 3D explanation supervision that ensures uncertainty-aware explanation guidance when dealing with sparsely annotated 3D data with diffusion-based 3D interpolation. Our proposed framework is validated through comprehensive experiments on diverse real-world medical imaging datasets. The results demonstrate the effectiveness of our framework in enhancing the predictability and explainability of deep learning models in the context of medical imaging diagnosis applications.
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
PDF ↗The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of complex diseases, failing to model the potential disease subtypes regarding their corresponding patient visits and clinical concept subgroups. In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. Specifically, we develop a novel self-supervised co-clustering framework that can be guided by the risk prediction task of specific diseases. Furthermore, we enhance the hypergraph model of EHR data with textual embeddings and enforce the alignment between the clusters of clinical concepts and patient visits through a contrastive objective. Comprehensive experiments conducted on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction demonstrate an average 31.25% performance improvement compared to traditional ML baselines and a 5.26% improvement on top of the vanilla hypergraph model without our co-clustering mechanism. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO. Code is available at https://github.com/PericlesHat/TACCO.