OpenRaiser/Intern-Atlas huggingface dataset:
WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data
Authors: Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif
Venue: WSDM
Year: 2024

We present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided with a pre-built pipeline to access the main modules through jupyter widgets. It results in the encapsulation of a whole vocabulary exploration process within a single jupyter notebook cell, with straightforward parameters settings and interactive plots. WordGraph pipeline is fully customizable by adding/removing widgets or changing default parameters. To assist users with no background in Python nor jupyter notebook, but willing to explore large corpora topics, we also propose an automatic dashboard generation from the customizable jupyter notebook pipeline in a web application style. WordGraph is available through a GitHub repository at https://github.com/MLDS-software/WordGraph.
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
Authors: Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng, Yunfeng Zhao, Guoxian Yu, Shuguang Han, Yuning Jiang, Jian Xu, Bo Zheng
Venue: WSDM
Year: 2024

In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recognized the importance of privileged features and explored ways to tackle online-offline discrepancies. A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving. In practice, the pointwise cross-entropy loss is often adopted for PFD. However, this loss is insufficient to distill the ranking ability for CTR prediction. First, it does not consider the non-i.i.d. characteristic of the data distribution, i.e., other items on the same page significantly impact the click probability of the candidate item. Second, it fails to consider the relative item order ranked by the teacher model's predictions, which is essential to distill the ranking ability. To address these issues, we first extend the pointwise-based PFD to the listwise-based PFD. We then define the calibration-compatible property of distillation loss and show that commonly used listwise losses do not satisfy this property when employed as distillation loss, thus compromising the model's calibration ability, which is another important measure for CTR prediction. To tackle this dilemma, we propose Calibration-compatible LIstwise Distillation (CLID), which employs carefully-designed listwise distillation loss to achieve better ranking ability than the pointwise-based PFD while preserving the model's calibration ability. We theoretically prove it is calibration-compatible. Extensive experiments on public datasets and a production dataset collected from the display advertising system of Alibaba further demonstrate the effectiveness of CLID.
EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence
Authors: Ambreen Hanif, Amin Beheshti, Xuyun Zhang, Steven Wood, Boualem Benatallah, EuJin Foo
Venue: WSDM
Year: 2024

Explainable Artificial Intelligence (XAI) aims to make artificial intelligence (AI) systems transparent and understandable to humans, providing clear explanations for the decisions made by AI models. This paper presents a novel pipeline and a digital dashboard that provides a user-friendly platform for interpreting the results of machine learning algorithms using XAI technology. The dashboard utilizes evidence-based design principles to deliver information clearly and concisely, enabling users to better understand the decisions made by their algorithms. We integrate XAI services into the dashboard to explain the algorithm's predictions, allowing users to understand how their models function and make informed decisions. We demonstrate a motivating scenario in banking and present how the proposed system enhances transparency and accountability and improves trust in the technology.
Ginkgo-P: General Illustrations of Knowledge Graphs for Openness as a Platform
Authors: Blaine Hill, Lihui Liu, Hanghang Tong
Venue: WSDM
Year: 2024

Accessibility and openness are two of the most important factors in motivating AI and Web research. One example is as costs to train and deploy large Knowledge Graph (KG) systems increases, valuable auxiliary features such as visualization, explainability, and automation are often overlooked, diminishing impact and popularity. Furthermore, current KG research has undergone a vicissitude to become convoluted and abstract, dissuading collaboration. To this end, we present Ginkgo-P, a platform to automatically illustrate any KG algorithm with nothing but a script and a data file. Additionally, Ginkgo-P elucidates modern KG research on the UMLS dataset with interactive demonstrations on four categories: KG Node Recommendation, KG Completion, KG Question Answering, and KG Reinforcement Learning. These categories and their many applications are increasingly ubiquitous yet lack both introductory and advanced resources to accelerate interest and contributions: with just a few clicks, our demonstration addresses this by providing an open platform for users to integrate individual KG algorithms. The source code for Ginkgo-P is available: we hope that it will propel future KG systems to become more accessible as an open source project.
SCAD: Subspace Clustering based Adversarial Detector
Authors: Xinrong Hu, Wushuan Chen, Jie Yang, Yi Guo, Xun Yao, Bangchao Wang, Junping Liu, Ce Xu
Venue: WSDM
Year: 2024

Adversarial examples pose significant challenges for Natural Language Processing (NLP) model robustness, often causing notable performance degradation. While various detection methods have been proposed with the aim of differentiating clean and adversarial inputs, they often require fine-tuning with ample data, which is problematic for low-resource scenarios. To alleviate this issue, a Subspace Clustering based Adversarial Detector (termed SCAD) is proposed in this paper, leveraging a union of subspaces to model the clean data distribution. Specifically, SCAD estimates feature distribution across semantic subspaces, assigning unseen examples to the nearest one for effective discrimination. The construction of semantic subspaces does not require many observations and hence ideal for the low-resource setting. The proposed algorithm achieves detection results better than or competitive with previous state-of-the-arts on a combination of three well-known text classification benchmarks and four attacking methods. Further empirical analysis suggests that SCAD effectively mitigates the low-resource setting where clean training data is limit.
User Consented Federated Recommender System Against Personalized Attribute Inference Attack
Authors: Qi Hu, Yangqiu Song
Venue: WSDM
Year: 2024

Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines. Our code is available at https://github.com/HKUST-KnowComp/UC-FedRec.
From Second to First: Mixed Censored Multi-Task Learning for Winning Price Prediction
Authors: Jiani Huang, Zhenzhe Zheng, Yanrong Kang, Zixiao Wang
Venue: WSDM
Year: 2024

A transformation from second-price auctions (SPA) to first-price auctions (FPA) has been observed in online advertising. The consequential coexistence of mixed FPA and SPA auction types has further led to the problem of mixed censorship, making bid landscape forecasting, the prerequisite for bid shading, more difficult. Our key insight is that the winning price (WP) under SPA can be effectively transferred to FPA scenarios if they share similar user groups, advertisers, and bidding environments. The full utilization of winning price under mixed censorship can effectively alleviate the FPA censorship problem and improve the performance of winning price prediction (aka. bid landscape forecasting). In this work, we propose a Multi-task Mixed Censorship Predictor (MMCP) that utilizes multi-task learning (MTL) to leverage the WP under SPA as supervised information for FPA. A Double-gate Mixture-of-Experts architecture has been proposed to alleviate the negative transfer problem of multi-task learning in our context. Furthermore, several auxiliary modules including the first-second mapping module and adaptive censorship loss function have been introduced to integrate MTL and winning price prediction. Extensive experiments on two real-world datasets demonstrate the superior performance of the proposed MMCP compared with other state-of-the-art FPA models under various performance metrics. The implementation of the code is available on github (https://github.com/Currycurrycurry/MMCP/).
Profiling Urban Mobility Patterns with High Spatial and Temporal Resolution: A Deep Dive into Cellphone Geo-position Data
Authors: José Ignacio Huertas, Luisa Fernanda Chaparro Sierra
Venue: WSDM
Year: 2024

Traditionally, urban traveling patterns have been obtained through origin-destination surveys. This method presents drawbacks such as high costs, limited representativeness of the surveyed population, and low spatial and temporal resolution of the results obtained. This study proposes deploying historical data on mobile device geolocalization to depict population displacement patterns with high spatial and temporal resolution levels. As an illustrative example, the traveling patterns were derived for a megacity in Latin America (the metropolitan area of Monterrey, Mexico) with a database of 0.7 million users being monitored during three months. The solutions formulated to tackle the challenges posed by this proposed method are described, as well as the use of the information gathered to obtain dynamic origin-destination matrices, quantify the average number of daily trips and kilometers traveled per inhabitant, attain population density per hour, and to identify the destinations attractor of most trips. We also suggest using this information to assess the impact of massive events such as concerts and sports gatherings on city mobility and air pollution.
Mitigating Factual Inconsistency and Hallucination in Large Language Models
Authors: Muneeswaran I, Advaith Shankar, Varun V, Saisubramaniam Gopalakrishnan, Vishal Vaddina
Venue: WSDM
Year: 2024

Large Language Models (LLMs) have demonstrated remarkable capabilities in various language-related tasks enabling applications in various fields such as healthcare, education, financial services etc. However, they are prone to producing factually incorrect responses or ''hallucinations'' which can have detrimental consequences such as loss of credibility, diminished customer trust etc. In this presentation, we showcase a solution that addresses the challenge of minimizing hallucinations. Our solution provides accurate responses and generates detailed explanations, thereby enabling the users to know how the model arrived at the final response. Additionally, it verifies if the explanations are factually correct and offers insights into whether the generated explanations are directly derived from the provided context or if they are inferred from it. We also systematically assess the quality of generated responses using an LLM-based evaluation technique. We present empirical results on benchmark datasets to demonstrate the effectiveness of our approach. Our presentation also examines the impact of individual components in the solution, enhancing the factual correctness of the final response. This research is vital for industries utilizing LLMs, as it provides a means to enhance the reliability of responses and mitigate the risks associated with factual hallucinations. Researchers and practitioners seeking to enhance the reliability of LLM responses will find valuable insights in this presentation.
Recent Advances in Refinement Recommendations
Authors: Akshay Jagatap, Sachin Farfade
Venue: WSDM
Year: 2024

Navigating vast e-commerce websites with extensive product cata- logs can be a daunting challenge for shoppers. To assist customers in finding the products they desire, e-commerce platforms provide product attribute filters, commonly referred to as "refinements." These refinements serve as a vital navigational aid, enabling cus- tomers to refine their search results based on specific product at- tributes such as material, color, size, brand, etc. However, on mobile devices refinements are not easily discoverable due to lack of screen space. To improve discoverability, contextually relevant refinements are suggested in-line on search page by refinement recommenda- tion systems. In the work, we discuss the evolution of refinement recommendations strategies i.e, a) search query-based classifica- tion approach, for a given search query we train a classification model, with the refinements as labels b) session-based classification approach, for the given sequence of session interactions we train a sequence classification model, with the refinements as labels and c) session-based generation approach, with the sequence of session interactions as input and output as the refinement name.
Bridging Text Data and Graph Data: Towards Semantics and Structure-aware Knowledge Discovery
Authors: Bowen Jin, Yu Zhang, Sha Li, Jiawei Han
Venue: WSDM
Year: 2024

Graphs and texts are two key modalities in data mining. In many cases, the data presents a mixture of the two modalities and the information is often complementary: in e-commerce data, the product-user graph and product descriptions capture different aspects of product features; in scientific literature, the citation graph, author metadata, and the paper content all contribute to modeling the paper impact.
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation
Authors: Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
Venue: WSDM
Year: 2024

Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions. Through our proposed framework REIGN, we take several steps to remedy this restricted learning setup. First, we systematically generate reformulations of training questions to increase robustness of models to surface form variations. This is a particularly challenging problem, given the incomplete nature of such questions. Second, we guide ConvQA models towards higher performance by feeding it only those reformulations that help improve their answering quality, using deep reinforcement learning. Third, we demonstrate the viability of training major model components on one benchmark and applying them zero-shot to another. Finally, for a rigorous evaluation of robustness for trained models, we use and release large numbers of diverse reformulations generated by prompting ChatGPT for benchmark test sets (resulting in 20x increase in sizes). Our findings show that ConvQA models with robust training via reformulations significantly outperform those with standard training from gold QA pairs only.
Foundation Models for Aerial Robotics
Authors: Ashish Kapoor
Venue: WSDM
Year: 2024

Developing machine intelligence abilities in robots and autonomous systems is an expensive and time-consuming process. Existing solutions are tailored to specific applications and are harder to generalize. Furthermore, scarcity of training data adds a layer of complexity in deploying deep machine learning models. We present a new platform for General Robot Intelligence Development (GRID) to address both of these issues. The platform enables robots to learn, compose and adapt skills to their physical capabilities, environmental constraints and goals. One of the components of GRID is a state-of-the-art simulation system that models robot physics and the machine intelligence processes. This system in turn is tightly coupled with a multitude of Foundation Models that enables rapid prototyping, design, debugging and refinement of robot AI models. GRID is designed from the ground up to be extensible to accommodate new types of robots, vehicles, hardware platforms and software protocols. In addition, the modular design enables various deep ML components and existing foundation models to be easily usable in a wider variety of robot-centric problems. We demonstrate the platform in various aerial robotics scenarios and demonstrate how the platform dramatically accelerates development of machine intelligent robots.
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
Authors: Yungi Kim, Taeri Kim, Won-Yong Shin, Sang-Wook Kim
Venue: WSDM
Year: 2024

In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia recommender systems: (L1) although multimodal features of interacted items by a user can reveal her preferences on items, existing methods utilize GCN designed to focus only on capturing collaborative signals, resulting in insufficient reflection of the multimodal features in the final user/item embeddings; (L2) although a user decides whether to prefer the target item by considering its multimodal features, existing methods represent her as only a single embedding regardless of the target item's multimodal features and then utilize her embedding to predict her preference for the target item. To address the above issues, we propose a novel multimedia recommender system, named MONET, composed of following two core ideas: modality-embracing GCN (MeGCN) and target-aware attention. Through extensive experiments using four real-world datasets, we demonstrate i) the significant superiority of MONET over seven state-of-the-art competitors (up to 30.32% higher accuracy in terms of recall@20, compared to the best competitor) and ii) the effectiveness of the two core ideas in MONET. All MONET codes are available at https://github.com/Kimyungi/MONET.
C²DR: Robust Cross-Domain Recommendation based on Causal Disentanglement
Authors: Menglin Kong, Jia Wang, Yushan Pan, Haiyang Zhang, Muzhou Hou
Venue: WSDM
Year: 2024

Cross-domain recommendation aims to leverage heterogeneous information to transfers knowledge from a data-sufficient domain (source domain) to a data-scarce domain (target domain). Existing approaches mainly focus on learning single-domain user preferences and then employ a transferring module to obtain cross-domain user preferences, but ignore the modeling of users' domain specific preferences on items. We argue that incorporating domain-specific preferences from the source domain will introduce irrelevant information that fails to the target domain. Additionally, directly combining domain-shared and domain-specific information may hinder the target domain's performance. To this end, we propose C^2DR, a novel approach that disentangles domain-shared and domain-specific preferences from a causal perspective. Specifically, we formulate a causal graph to capture the critical causal relationships based on the underlying recommendation process, explicitly identifying domain-shared and domain-specific information as causal irrelevant variables. Then, we introduce disentanglement regularization terms to learn distinct representations of the causal variables that obey the independence constraints in the causal graph. Remarkably, our proposed method enables effective intervention and transfer of domain-shared information, thereby improving the robustness of the recommendation model. We evaluate the efficacy of C^2DR through extensive experiments on three real-world datasets, demonstrating significant improvements over state-of-the-art baselines.
Towards Trustworthy Large Language Models
Authors: Sanmi Koyejo, Bo Li
Venue: WSDM
Year: 2024

Large Language models are among the most exciting technologies developed in the last few years. While the model's capabilities continue to improve, researchers, practitioners, and the general public are increasingly aware of some of its shortcomings. What will it take to build trustworthy large language models? This tutorial will present a range of recent findings, discussions, questions, and partial answers in the space of trustworthiness in large language models. While this tutorial will not attempt a comprehensive overview of this rich area, we aim to provide the participants with some tools and insights and to understand both the conceptual foundations of trustworthiness and a broad range of ongoing research efforts. We will tackle some of the hard questions that you may have about trustworthy large language models and hopefully address some misconceptions that have become pervasive.
WSDM 2024 Workshop on Representation Learning & Clustering
Authors: Lazhar Labiod, Mohamed Nadif, Aghiles Salah
Venue: WSDM
Year: 2024

Data clustering and representation learning play an indispensable role in data science. They are very useful to explore massive data in many fields, including information retrieval, natural language processing, bioinformatics, recommender systems, and computer vision. Despite their success, most existing clustering methods are severely challenged by the data generated by modern applications, which are typically high dimensional, noisy, heterogeneous, and sparse or even collected from multiple sources or represented by multiple views where each describes a perspective of the data. This has driven many researchers to investigate new effective clustering models to overcome these difficulties. One promising category of such models relies on representation learning. Indeed, learning a good data representation is crucial for clustering algorithms, and combining the two tasks is a common way of exploring this type of data. The idea is to embed the original data into a low dimensional latent space and then perform clustering on this new space. However, both tasks can be carried out sequentially or jointly. Many clustering algorithms, including deep learning versions, are based on these two modes of combining the two tasks.