OpenRaiser/Intern-Atlas huggingface dataset:
Credibility assessment of textual claims on the web
Authors: Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, Gerhard Weikum
Venue: Conference CIKM 2016
Year: 2016

There is an increasing amount of false claims in news, social media, and other web sources. While prior work on truth discovery has focused on the case of checking factual statements, this paper addresses the novel task of assessing the credibility of arbitrary claims made in natural-language text - in an open-domain setting without any assumptions about the structure of the claim, or the community where it is made. Our solution is based on automatically finding sources in news and social media, and feeding these into a distantly supervised classifier for assessing the credibility of a claim (i.e., true or fake). For inference, our method leverages the joint interaction between the language of articles about the claim and the reliability of the underlying web sources. Experiments with claims from the popular website snopes.com and from reported cases of Wikipedia hoaxes demonstrate the viability of our methods and their superior accuracy over various baselines.
Fairness in machine learning for healthcare
Authors: Muhammad Aurangzeb Ahmad, Arpit Patel, Carly Eckert, Vikas Kumar, Ankur Teredesai
Venue: Conference KDD 2020
Year: 2020

The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.
Face-keg: Fact checking explained using knowledge graphs
Authors: Nikhita Vedula, Srinivasan Parthasarathy
Venue: Conference WSDM 2021
Year: 2021

In recent years, a plethora of fact checking and fact verification techniques have been developed to detect the veracity or factuality of online information text for various applications. However, limited efforts have been undertaken to understand the interpretability of such veracity detection, i.e. explaining why a particular piece of text is factually correct or incorrect. In this work, we seek to bridge this gap by proposing a technique, FACE-KEG, to automatically perform explainable fact checking. Given an input fact or claim, our proposed model constructs a relevant knowledge graph for it from a large-scale structured knowledge base. This graph is encoded via a novel graph transforming encoder. Our model also simultaneously retrieves and encodes relevant textual context about the input text from the knowledge base. FACE-KEG then jointly exploits both the concept-relationship structure of the knowledge graph as well as semantic contextual cues in order to (i) detect the veracity of an input fact, and (ii) generate a human-comprehensible natural language explanation justifying the fact's veracity. We conduct extensive experiments on three large-scale datasets, and demonstrate the effectiveness of FACE-KEG while performing fact checking. Automatic and human evaluations further show that FACE-KEG significantly outperforms competitive baselines in learning concise, coherent and informative explanations for the input facts.
Multimodal banking dataset: Understanding client needs through event sequences
Authors: Dzhambulat Mollaev, Ivan Kireev, Mikhail Orlov, Alexander Kostin, Ivan Karpukhin, Maria Postnova, Gleb Gusev, Andrey Savchenko
Venue: Conference CIKM 2025
Year: 2025

Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent practical need, developing deep learning techniques suitable to handle such data is limited by the absence of large open-source multi-source real-world datasets of event sequences. To fill this gap, which is mainly caused by security reasons, we present the first industrial-scale publicly available multimodal banking dataset, MBD, that contains information on more than 2M corporate clients of a large bank. Clients are represented by several data sources: 950M bank transactions, 1B geo position events, 5M embeddings of dialogues with technical support, and monthly aggregated purchases of four bank products. All entries are properly anonymized from real proprietary bank data, and the experiments confirm that our anonymization still saves all significant information for introduced downstream tasks. Moreover, we introduce a novel multimodal benchmark suggesting several important practical tasks, such as future purchase prediction and modality matching. The benchmark incorporates our MBD and two public financial datasets. We provide numerical results for the state-of-the-art event sequence modeling techniques including large language models and demonstrate the superiority of fusion baselines over single-modal techniques for each task. Thus, MBD provides a valuable resource for future research in financial applications of multimodal event sequence analysis. HuggingFace Link: https://huggingface.co/datasets/ai-lab/MBD Github Link: https://github.com/Dzhambo/MBD
Internal logical induction for pixel-symbolic reinforcement learning
Authors: Jiacheng Xu 0003, Chao Chen 0028, Fuxiang Zhang, Lei Yuan 0005, Zongzhang Zhang, Yang Yu 0001
Venue: Conference KDD 2023
Year: 2023

Reinforcement Learning (RL) has experienced rapid advancements in recent years. The widely studied RL algorithms mainly focus on a single input form, such as pixel-based image input or symbolic vector input. These two forms have different characteristics and, in many scenarios, will appear together, while few RL algorithms have studied the problems with mixed input types. Specifically, in the scenario where both pixel and symbolic inputs are available, symbolic input usually offers abstract features with specific semantics, which is more conducive to the agent's focus. Conversely, pixel input provides more comprehensive information, enabling the agent to make well-informed decisions. Tailoring the processing approach based on the properties of these two input types can contribute to solving the problem more effectively. To tackle the above issue, we propose an Internal Logical Induction (ILI) framework that integrates deep RL and rule learning into one system. ILI utilizes the deep RL algorithm to process the pixel input and the rule learning algorithm to induce propositional logic knowledge from symbolic input. To efficiently combine these two mechanisms, we further adopt a reward shaping technique by treating valuable knowledge as intrinsic rewards for the RL procedure. Experimental results demonstrate that the ILI framework outperforms baseline approaches in RL problems with pixel-symbolic input, and its inductive knowledge exhibits transferability advantages when pixel input semantics change.
Improving web search results using affinity graph
Authors: Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, Wei-Ying Ma
Venue: Conference SIGIR 2005
Year: 2005

In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
Causality-driven user modeling for sequential recommendations over time
Authors: Xingming Chen, Qing Li
Venue: Conference WWW 2024
Year: 2024

Contemporary sequential recommendation systems predominantly leverage statistical correlations derived from user interaction histories to predict future preferences. However, these correlations often mask implicit challenges. On the one hand, user data is frequently plagued by implicit, noisy feedback, misdirecting users towards items that fail to align with their actual interests, which is magnified in sequential recommendation contexts. On the other hand, prevalent methods tend to over-rely on similarity-based attention mechanisms across item pairs, which are prone to utilizing heuristic shortcuts, thereby leading to suboptimal recommendation. To tackle these issues, we put forward a causality-driven user modeling approach for sequential recommendation, which pivots towards a causal perspective. Specifically, we involves the application of a causal graph to identify confounding factors that give rise to spurious correlations and to isolate conceptual variables that causally encapsulate user preferences. By learning the representation of these disentangled causal variables at the conceptual level, we can distinguish between causal and non-causal associations while preserving the inherent sequential nature of user behaviors. This enables us to ascertain which elements are critical and which may induce unintended biases. The framework of our method can be compatible with various mainstream sequential models, which offers a robust foundation for reconstructing more accurate and meaningful user and item representations driven by causality.
Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
Authors: Hyunsik Jeon, Jong-eun Lee, Jeongin Yun, U Kang
Venue: Conference WWW 2024
Year: 2024

How can we recommend cold-start bundles to users? The cold-start problem in bundle recommendation is crucial because new bundles are continuously created on the Web for various marketing purposes. Despite its importance, existing methods for cold-start item recommendation are not readily applicable to bundles. They depend overly on historical information, even for less popular bundles, failing to address the primary challenge of the highly skewed distribution of bundle interactions. In this work, we propose CoHeat (Popularity-based Coalescence and Curriculum Heating), an accurate approach for cold-start bundle recommendation. CoHeat first represents users and bundles through graph-based views, capturing collaborative information effectively. To estimate the user-bundle relationship more accurately, CoHeat addresses the highly skewed distribution of bundle interactions through a popularity-based coalescence approach, which incorporates historical and affiliation information based on the bundle's popularity. Furthermore, it effectively learns latent representations by exploiting curriculum learning and contrastive learning. CoHeat demonstrates superior performance in cold-start bundle recommendation, achieving up to 193% higher nDCG@20 compared to the best competitor.
Sinkhorn collaborative filtering
Authors: Xiucheng Li, Jin Yao Chin, Yile Chen, Gao Cong
Venue: Conference WWW 2021
Year: 2021

Recommender systems play a vital role in modern web services. In a typical recommender system, we are given a set of observed user-item interaction records and seek to uncover the hidden behavioral patterns of users from these historical interactions. By exploiting these hidden patterns, we aim to discover users’ personalized tastes and recommend them new items. Among various types of recommendation methods, the latent factor collaborative filtering models have dominated the field. In this paper, we develop a unified view for the existing latent factor models from a probabilistic perspective. The unified framework enables us to discern the underlying connections of different latent factor models and deepen our understandings of their advantages and limitations. In particular, we observe that the loss functions adopted by the existing models are oblivious to the geometry induced by the item-similarity. To address this, we propose a novel model—SinkhornCF—based on Sinkhorn divergence. To address the challenge of the expensive computational cost of Sinkhorn divergence, we also propose new techniques to enable the resulting model to be able to scale to large datasets. Its effectiveness is verified on two real-world recommendation datasets.
Deepstyle: Learning user preferences for visual recommendation
Authors: Qiang Liu, Shu Wu, Liang Wang
Venue: Conference SIGIR 2017
Year: 2017

Visual information is an important factor in recommender systems. Some studies have been done to model user preferences for visual recommendation. Usually, an item consists of two fundamental components: style and category. Conventional methods model items in a common visual feature space. In these methods, visual representations always can only capture the categorical information but fail in capturing the styles of items. Style information indicates the preferences of users and has significant effect in visual recommendation. Accordingly, we propose a DeepStyle method for learning style features of items and sensing preferences of users. Experiments conducted on two real-world datasets illustrate the effectiveness of DeepStyle for visual recommendation.
Exploiting variational domain-invariant user embedding for partially overlapped cross domain recommendation
Authors: Weiming Liu, Xiaolin Zheng, Jiajie Su, Mengling Hu, Yanchao Tan, Chaochao Chen
Venue: Conference SIGIR 2022
Year: 2022

Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer. However, only few proportion of users simultaneously activate on both the source and target domains in practical CDR tasks. In this paper, we focus on the Partially Overlapped Cross-Domain Recommendation (POCDR) problem, that is, how to leverage the information of both the overlapped and non-overlapped users to improve recommendation performance. Existing approaches cannot fully utilize the useful knowledge behind the non-overlapped users across domains, which limits the model performance when the majority of users turn out to be non-overlapped. To address this issue, we propose an end-to-end Dual-autoencoder with Variational Domain-invariant Embedding Alignment (VDEA) model, a cross-domain recommendation framework for the POCDR problem, which utilizes dual variational autoencoders with both local and global embedding alignment for exploiting domain-invariant user embedding. VDEA first adopts variational inference to capture collaborative user preferences, and then utilizes Gromov-Wasserstein distribution co-clustering optimal transport to cluster the users with similar rating interaction behaviors. Our empirical studies on Douban and Amazon datasets demonstrate that VDEA significantly outperforms the state-of-the-art models, especially under the POCDR setting.
Bootstrap Latent Representations for Multi-Modal Recommendation
Authors: Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang
Venue: Conference WWW 2023
Year: 2023

This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e.g., user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the representations of users and items with a simple dropout augmentation. It then jointly optimizes three multi-modal objectives to learn the representations of users and items by reconstructing the user-item interaction graph and aligning modality features under both inter- and intra-modality perspectives. BM3 alleviates both the need for contrasting with negative examples and the complex graph augmentation from an additional target network for contrastive view generation. We show BM3 outperforms prior recommendation models on three datasets with number of nodes ranging from 20K to 200K, while achieving a 2-9 × reduction in training time. Code implementation is located at: https://github.com/enoche/BM3.
On the convergence of the Shapley value in parametric Bayesian learning games
Authors: Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
Venue: Conference ICML 2022
Year: 2022

Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergence property of the Shapley value in parametric Bayesian learning games where players perform a Bayesian inference using their combined data, and the posterior-prior KL divergence is used as the characteristic function. We show that for any two players, under some regularity conditions, their difference in Shapley value converges in probability to the difference in Shapley value of a limiting game whose characteristic function is proportional to the log-determinant of the joint Fisher information. As an application, we present an online collaborative learning framework that is asymptotically Shapley-fair. Our result enables this to be achieved without any costly computations of posterior-prior KL divergences. Only a consistent estimator of the Fisher information is needed. The effectiveness of our framework is demonstrated with experiments using real-world data.
Fast causal discovery by approximate kernel-based generalized score functions with linear computational complexity
Authors: Yixin Ren, Haocheng Zhang, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou
Venue: Conference KDD 2025
Year: 2025

Score-based causal discovery methods can effectively identify causal relationships by evaluating candidate graphs and selecting the one with the highest score. One popular class of scores is kernel-based generalized score functions, which can adapt to a wide range of scenarios and work well in practice because they circumvent assumptions about causal mechanisms and data distributions. Despite these advantages, kernel-based generalized score functions pose serious computational challenges in time and space, with a time complexity of $\mathcal{O}(n^3)$ and a memory complexity of $\mathcal{O}(n^2)$, where $n$ is the sample size. In this paper, we propose an approximate kernel-based generalized score function with $\mathcal{O}(n)$ time and space complexities by using low-rank technique and designing a set of rules to handle the complex composite matrix operations required to calculate the score, as well as developing sampling algorithms for different data types to benefit the handling of diverse data types efficiently. Our extensive causal discovery experiments on both synthetic and real-world data demonstrate that compared to the state-of-the-art method, our method can not only significantly reduce computational costs, but also achieve comparable accuracy, especially for large datasets.
CASA: clustered federated learning with asynchronous clients
Authors: Boyi Liu 0002, Yiming Ma 0005, Zimu Zhou, Yexuan Shi, Shuyuan Li, Yongxin Tong
Venue: Conference KDD 2024
Year: 2024

Clustered Federated Learning (CFL) is an emerging paradigm to extract insights from data on IoT devices. Through iterative client clustering and model aggregation, CFL adeptly manages data heterogeneity, ensures privacy, and delivers personalized models to heterogeneous devices. Traditional CFL approaches, which operate synchronously, suffer from prolonged latency for waiting slow devices during clustering and aggregation. This paper advocates a shift to asynchronous CFL, allowing the server to process client updates as they arrive. This shift enhances training efficiency yet introduces complexities to the iterative training cycle. To this end, we present CASA, a novel CFL scheme for Clustering-Aggregation Synergy under Asynchrony. Built upon a holistic theoretical understanding of asynchrony's impact on CFL, CASA adopts a bi-level asynchronous aggregation method and a buffer-aided dynamic clustering strategy to harmonize between clustering and aggregation. Extensive evaluations on standard benchmarks show that CASA outperforms representative baselines in model accuracy and achieves 2.28-6.49× higher convergence speed.
Off-policy learning in two-stage recommender systems
Authors: Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi
Venue: Conference WWW 2020
Year: 2020

Many real-world recommender systems need to be highly scalable: matching millions of items with billions of users, with milliseconds latency. The scalability requirement has led to widely used two-stage recommender systems, consisting of efficient candidate generation model(s) in the first stage and a more powerful ranking model in the second stage. Logged user feedback, e.g., user clicks or dwell time, are often used to build both candidate generation and ranking models for recommender systems. While it’s easy to collect large amount of such data, they are inherently biased because the feedback can only be observed on items recommended by the previous systems. Recently, off-policy correction on such biases have attracted increasing interest in the field of recommender system research. However, most existing work either assumed that the recommender system is a single-stage system or only studied how to apply off-policy correction to the candidate generation stage of the system without explicitly considering the interactions between the two stages. In this work, we propose a two-stage off-policy policy gradient method, and showcase that ignoring the interaction between the two stages leads to a sub-optimal policy in two-stage recommender systems. The proposed method explicitly takes into account the ranking model when training the candidate generation model, which helps improve the performance of the whole system. We conduct experiments on real-world datasets with large item space and demonstrate the effectiveness of our proposed method.