OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine-tuning
Authors: Wangyang Ying, Dongjie Wang, Xuanming Hu, Yuanchun Zhou, Charu C. Aggarwal, Yanjie Fu
Venue: Conference KDD 2024
Year: 2024

Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experiments. This issue motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior literature, such as manual transformation, supervised feedback guided search, and PCA, either relies on domain knowledge or expensive supervised feedback, or suffers from large search space, or overlooks non-linear feature-feature interactions. UFTL imposes a major challenge on existing methods: how to design a new unsupervised paradigm that captures complex feature interactions and avoids large search space? To fill this gap, we connect graph, contrastive, and generative learning to develop a measurement-pretrain-finetune paradigm for UFTL. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective and develop a mean discounted cumulative gain like unsupervised metric to evaluate feature set utility. For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation. We develop a deep generative feature transformation model that coordinates the pretrained feature set encoder and the gradient information extracted from a feature set utility evaluator to optimize a transformed feature generator. Finally, we conduct extensive experiments to demonstrate the effectiveness, efficiency, traceability, and explicitness of our framework.
Aiming at the target: Filter collaborative information for cross-domain recommendation
Authors: Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma
Venue: Conference SIGIR 2024
Year: 2024

As recommender systems become pervasive in various scenarios, cross-domain recommenders (CDR) are proposed to enhance the performance of one target domain with data from other related source domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. Most existing efforts to tackle this issue primarily focus on designing adaptive representations for overlapped users. Whereas, these methods rely on the learned representations of the model, lacking explicit constraints to filter irrelevant source-domain collaborative information for the target domain, which limits their cross-domain transfer capability. In this paper, we propose a novel Collaborative information regularized User Transformation (CUT) framework to tackle the negative transfer problem by directly filtering users' collaborative information. In CUT, target domain user similarity is adopted as a constraint for user transformation to filter user collaborative information from the source domain. First, CUT learns user similarity relationships from the target domain. Then, source-target information transfer is guided by the user similarity, where we design a user transformation layer to learn target-domain user representations and a contrastive loss to supervise the user collaborative information transferring. As a flexible and lightweight framework, CUT can be applied with various single-domain recommender systems as the backbone and extend them to multi-domain tasks. Empirical studies on two real-world datasets show that CUT effectively alleviates the negative transfer problem, and it significantly outperforms other SOTA single and cross-domain methods.
A review of modern recommender systems using generative models (gen-recsys
Authors: Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano
Venue: Conference KDD 2024
Year: 2024

Traditional recommender systems (RS) 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.
Unsupervised lifelong learning with curricula
Authors: Yi He, Sheng Chen, Baijun Wu, Xu Yuan, Xindong Wu
Venue: Conference WWW 2021
Year: 2021

Lifelong machine learning (LML) has driven the development of extensive web applications, enabling the learning systems deployed on web servers to deal with a sequence of tasks in an incremental fashion. Such systems can retain knowledge from learned tasks in a knowledge base and seamlessly apply it to improve the future learning. Unfortunately, most existing LML methods require labels in every task, whereas providing persistent human labeling for all future tasks is costly, onerous, error-prone, and hence impractical. Motivated by this situation, we propose a new paradigm named unsupervised lifelong learning with curricula (ULLC), where only one task needs to be labeled for initialization and the system then performs lifelong learning for subsequent tasks in an unsupervised fashion. A main challenge of realizing this paradigm lies in the occurrence of negative knowledge transfer, where partial old knowledge becomes detrimental for learning a given task yet cannot be filtered out by the learner without the help of labels. To overcome this challenge, we draw insights from the learning behaviors of humans. Specifically, when faced with a difficult task that cannot be well tackled by our current knowledge, we usually postpone it and work on some easier tasks first, which allows us to grow our knowledge. Thereafter, once we go back to the postponed task, we are more likely to tackle it well as we are more knowledgeable now. The key idea of ULLC is similar – at any time, a pool of candidate tasks are organized in a curriculum by their distances to the knowledge base. The learner then starts from the closer tasks, accumulates knowledge from learning them, and moves to learn the faraway tasks with a gradually augmented knowledge base. The viability and effectiveness of our proposal are substantiated through extensive empirical studies on both synthetic and real datasets.
Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification
Authors: Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu, Shaoping Ma
Venue: Conference SIGIR 2014
Year: 2014

Current approaches for contextual sentiment lexicon construction in phrase-level sentiment analysis assume that the numerical star rating of a review represents the overall sentiment orientation of the review text. Although widely adopted, we find through user rating analysis that this is not necessarily true. In this paper, we attempt to bridge the gap between phrase-level and review/document-level sentiment analysis by leveraging the results given by review-level sentiment classification to boost phrase-level sentiment polarity labeling in contextual sentiment lexicon construction tasks, using a novel constrained convex optimization framework. Experimental results on both English and Chinese reviews show that our framework improves the precision of sentiment polarity labeling by up to 5.6%, which is a significant improvement from current approaches.
Online Drift Detection with Maximum Concept Discrepancy
Authors: Ke Wan 0002, Yi Liang, Susik Yoon
Venue: Conference KDD 2024
Year: 2024

Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era. However, data streams often do not conform to the same distribution over time, leading to a phenomenon called concept drift. Since a fixed static model is unreliable for inferring concept-drifted data streams, establishing an adaptive mechanism for detecting concept drift is crucial. Current methods for concept drift detection primarily assume that the labels or error rates of downstream models are given and/or underlying statistical properties exist in data streams. These approaches, however, struggle to address high-dimensional data streams with intricate irregular distribution shifts, which are more prevalent in real-world scenarios. In this paper, we propose MCD-DD, a novel concept drift detection method based on maximum concept discrepancy, inspired by the maximum mean discrepancy. Our method can adaptively identify varying forms of concept drift by contrastive learning of concept embeddings without relying on labels or statistical properties. With thorough experiments under synthetic and real-world scenarios, we demonstrate that the proposed method outperforms existing baselines in identifying concept drifts and enables qualitative analysis with high explainability.
Guided text-based item exploration
Authors: Behrooz Omidvar-Tehrani, Aurélien Personnaz, Sihem Amer-Yahia
Venue: Conference CIKM 2022
Year: 2022

Exploratory Data Analysis (EDA) provides guidance to users to help them refine their needs and find items of interest in large volumes of structured data. In this paper, we develop GUIDES, a framework for guided Text-based Item Exploration (TIE). TIE raises new challenges: (i) the need to abstract and query textual data and (ii) the need to combine queries on both structured and unstructured content. GUIDES represents text dimensions such as sentiment and topics, and introduces new text-based operators that are seamlessly integrated with traditional EDA operators. To train TIE policies, it relies on a multi-reward function that captures different textual dimensions, and extends the Deep Q-Networks (DQN) architecture with multi-objective optimization. Our experiments on Amazon and IMDb, two real-world datasets, demonstrate the necessity of capturing fine-grained text dimensions, the superiority of using both text-based and attribute-based operators over attribute-based operators only, and the need for multi-objective optimization.
DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning
Authors: Aurélien Personnaz, Sihem Amer-Yahia, Laure Berti-Équille, Maximilian Fabricius, Srividya Subramanian
Venue: Conference CIKM 2021
Year: 2021

We demonstrate DORA THE EXPLORER, a system that guides users in finding items of interest in a very large data set. DORA THE EXPLORER provides users with the full spectrum of exploration modes and is driven by Data Familiarity or Curiosity, as well as User Interventions. DORA THE EXPLORER is able to handle data and search scenario complexity, i.e., the difficulty to find scattered/clustered individual records in the data set, and user ability to express what s/he needs. DORA THE EXPLORER relies on Deep Reinforcement Learning that combines intrinsic (curiosity) and extrinsic (familiarity) rewards. DORA's main goal is to support scientific discovery from data. We describe the system architecture and illustrate it with three demonstration scenarios on a 2.6 mil-lion galaxies SDSS, a large sky survey data set1. A video of DORA THE EXPLORER is available at https://bit.ly/dora-demo, the codehttps://github.com/apersonnaz/rl-guided-galaxy-exploration, and the application at https://bit.ly/dora-application
3M- Health: Multimodal Multi-Teacher Knowledge Distillation for Mental Health Detection
Authors: Rina Carines Cabral, Siwen Luo, Josiah Poon, Soyeon Caren Han
Venue: Conference CIKM 2024
Year: 2024

The significance of mental health classification is paramount in contemporary society, where digital platforms serve as crucial sources for monitoring individuals' well-being. However, existing social media mental health datasets primarily consist of text-only samples, potentially limiting the efficacy of models trained on such data. Recognising that humans utilise cross-modal information to comprehend complex situations or issues, we present a novel approach to address the limitations of current methodologies. In this work, we introduce a Multimodal and Multi-Teacher Knowledge Distillation model for Mental Health Classification, leveraging insights from cross-modal human understanding. Unlike conventional approaches that often rely on simple concatenation to integrate diverse features, our model addresses the challenge of appropriately representing inputs of varying natures (e.g., texts and sounds). To mitigate the computational complexity associated with integrating all features into a single model, we employ a multimodal and multi-teacher architecture. By distributing the learning process across multiple teachers, each specialising in a particular feature extraction aspect, we enhance the overall mental health classification performance. Through experimental validation, we demonstrate the efficacy of our model in achieving improved performance.
PAR- GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks
Authors: Junjie Chen, Wendy Hui Wang, Hongchang Gao, Xinghua Shi
Venue: Conference KDD 2021
Year: 2021

Recent works have shown that Generative Adversarial Networks (GANs) may generalize poorly and thus are vulnerable to privacy attacks. In this paper, we seek to improve the generalization of GANs from a perspective of privacy protection, specifically in terms of defending against the membership inference attack (MIA) which aims to infer whether a particular sample was used for model training. We design a GAN framework, partition GAN (PAR-GAN), which consists of one generator and multiple discriminators trained over disjoint partitions of the training data. The key idea of PAR-GAN is to reduce the generalization gap by approximating a mixture distribution of all partitions of the training data. Our theoretical analysis shows that PAR-GAN can achieve global optimality just like the original GAN. Our experimental results on simulated data and multiple popular datasets demonstrate that PAR-GAN can improve the generalization of GANs while mitigating information leakage induced by MIA.
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood-and Homogeneous-level Dependencies
Authors: Guohui Li, Zhiqiang Guo, Jianjun Li, Chaoyang Wang
Venue: Conference CIKM 2022
Year: 2022

Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and item representations, but neglect to mine the sufficient dependencies between nodes, e.g., the relationships between users/items and their neighbors (or congeners), resulting in inadequate graph representation learning. To address these problems, we propose a novel Multi-Dependency Graph Collaborative Filtering (MDGCF) model, which mines the neighborhood- and homogeneous-level dependencies to enhance the representation power of graph-based CF models. Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features. Besides, by adaptively mining the homogeneous-level dependencies among users and items, we construct two homogeneous graphs, based on which we further aggregate features from homogeneous users and items to supplement their representations, respectively. Extensive experiments on three real-world benchmark datasets demonstrate the effectiveness of the proposed MDGCF. Further experiments reveal that our model can capture rich dependencies between nodes for explaining user behaviors.
RareBench: Can LLMs Serve as Rare Diseases Specialists?
Authors: Xuanzhong Chen, Xiaohao Mao, Qihan Guo, Lun Wang, Shuyang Zhang, Ting Chen 0006
Venue: Conference KDD 2024
Year: 2024

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.
A recommender system for crowdsourcing food rescue platforms
Authors: Zheyuan Ryan Shi, Leah Lizarondo, Fei Fang
Venue: Conference WWW 2021
Year: 2021

The challenges of food waste and insecurity arise in wealthy and developing nations alike, impacting millions of livelihoods. The ongoing pandemic only exacerbates the problem. A major force to combat food waste and insecurity, food rescue (FR) organizations match food donations to the non-profits that serve low-resource communities. Since they rely on external volunteers to pick up and deliver the food, some FRs use web-based mobile applications to reach the right set of volunteers. In this paper, we propose the first machine learning based model to improve volunteer engagement in the food waste and security domain. We (1) develop a recommender system to send push notifications to the most likely volunteers for each given rescue, (2) leverage a mathematical programming based approach to diversify our recommendations, and (3) propose an online algorithm to dynamically select the volunteers to notify without the knowledge of future rescues. Our recommendation system improves the hit ratio from 44% achieved by the previous method to 73%. A pilot study of our method is scheduled to take place in the near future.
Predicting and Presenting Task Difficulty for Crowdsourcing Food Rescue Platforms
Authors: Zheyuan Ryan Shi, Jiayin Zhi, Siqi Zeng, Zhicheng Zhang, Ameesh Kapoor, Sean Hudson, Hong Shen, Fei Fang
Venue: Conference WWW 2024
Year: 2024

Food waste and food insecurity are two problems that co-exist worldwide. A major force to combat food waste and insecurity, food rescue platforms (FRP) match food donations to low-resource communities. Since they rely on external volunteers to deliver the food, communicating rescue task difficulty to volunteers is very important for volunteer engagement and retention. We develop a hybrid model with tabular and natural language data to predict the difficulty of a given rescue trip, which significantly outperforms baselines in identifying easy and hard rescues. Furthermore, using storyboards, we conducted interviews with different stakeholders to understand their perspectives on how to integrate such predictions into volunteers' workflow. Motivated by our findings, we developed three explanation methods to generate interpretable insights for volunteers to better understand the predictions. The results from this study are in the process of being adopted at Food Rescue Hero, a large FRP serving over 25 cities across the United States.
Learning to Score Economic Development from Satellite Imagery
Authors: Sungwon Han 0001, Donghyun Ahn, Sungwon Park 0001, Jeasurk Yang, Susang Lee, Jihee Kim, Hyunjoo Yang, Sangyoon Park, Meeyoung Cha
Venue: Conference KDD 2020
Year: 2020

Reliable and timely measurements of economic activities are fundamental for understanding economic development and designing government policies. However, many developing countries still lack reliable data. In this paper, we introduce a novel approach for measuring economic development from high-resolution satellite images in the absence of ground truth statistics. Our method consists of three steps. First, we run a clustering algorithm on satellite images that distinguishes artifacts from nature (siCluster). Second, we generate a partial order graph of the identified clusters based on the level of economic development, either by human guidance or by low-resolution statistics (siPog). Third, we use a CNN-based sorter that assigns differentiable scores to each satellite grid based on the relative ranks of clusters (siScore). The novelty of our method is that we break down a computationally hard problem into sub-tasks, which involves a human-in-the-loop solution. With the combination of unsupervised learning and the partial orders of dozens of urban vs. rural clusters, our method can estimate the economic development scores of over 10,000 satellite grids consistently with other baseline development proxies (Spearman correlation of 0.851). This efficient method is interpretable and robust; we demonstrate how to apply our method to both developed (e.g., South Korea) and developing economies (e.g., Vietnam and Malawi).