OpenRaiser/Intern-Atlas huggingface dataset:
MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
Authors: Dongcheng Zou, Senzhang Wang, Xuefeng Li, Hao Peng, Yuandong Wang, Chunyang Liu, Kehua Sheng, Bo Zhang
Venue: WSDM
Year: 2024

Traffic forecasting is a complex multivariate time-series regression task of paramount importance for traffic management and planning. However, existing approaches often struggle to model complex multi-range dependencies using local spatiotemporal features and road network hierarchical knowledge. To address this, we propose MultiSPANS. First, considering that an individual recording point cannot reflect critical spatiotemporal local patterns, we design multi-filter convolution modules for generating informative ST-token embeddings to facilitate attention computation. Then, based on ST-token and spatial-temporal position encoding, we employ the Transformers to capture long-range temporal and spatial dependencies. Furthermore, we introduce structural entropy theory to optimize the spatial attention mechanism. Specifically, The structural entropy minimization algorithm is used to generate optimal road network hierarchies, i.e., encoding trees. Based on this, we propose a relative structural entropy-based position encoding and a multi-head attention masking scheme based on multi-layer encoding trees. Extensive experiments demonstrate the superiority of the presented framework over several state-of-the-art methods in real-world traffic datasets, and the longer historical windows are effectively utilized. The code is available at https://github.com/SELGroup/MultiSPANS.
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation
Authors: Yue Ding, Yanbiao Ji, Xun Cai, Xin Xin, Yuxiang Lu, Suizhi Huang, Chang Liu, Xiaofeng Gao, Tsuyoshi Murata, Hongtao Lu
Venue: WSDM
Year: 2025

In modern recommender systems, especially in e-commerce, predicting multiple targets such as click-through rate (CTR) and post-view conversion rate (CTCVR) is common. Multi-task recommender systems are increasingly popular in both research and practice, as they leverage shared knowledge across diverse business scenarios to enhance performance. However, emerging real-world scenarios and data privacy concerns complicate the development of a unified multi-task recommendation model. In this paper, we propose PF-MSMTrec, a novel framework for personalized federated multi-scenario multi-task recommendation. In this framework, each scenario is assigned to a dedicated client utilizing the Multi-gate Mixture-of-Experts (MMoE) structure. To address the unique challenges of multiple optimization conflicts, we introduce a bottom-up joint learning mechanism. First, we design a parameter template to decouple the expert network parameters, distinguishing scenario-specific parameters as shared knowledge for federated parameter aggregation. Second, we implement personalized federated learning for each expert network during a federated communication round, using three modules: federated batch normalization, conflict coordination, and personalized aggregation. Finally, we conduct an additional round of personalized federated parameter aggregation on the task tower network to obtain prediction results for multiple tasks. Extensive experiments on two public datasets demonstrate that our proposed method outperforms state-of-the-art approaches. The source code and datasets will be released as open-source for public access.
Sequentially Diversified and Accurate Recommendations in Chronological Order for a Series of Users
Authors: Jongjin Kim, U Kang
Venue: WSDM
Year: 2025

When we sequentially recommend top-k items to users, how can we recommend them diversely while maintaining accuracy? Aggregate-level diversity is an important topic in recommender system since it is essential to maximize the potential profit of platforms by exposing a variety of items to users. However, previous studies do not consider the order of users receiving recommendations and assume that all users receive recommendations at once. In reality, users do not simultaneously receive recommendations so the preferences of the latter users are not given during recommending to the former users. In this work, we introduce the problem of sequentially diversified recommendation and propose SAPID, an accurate method to address the problem. SAPID removes the popularity bias from the model through a negative sampling mechanism based on temporal popularities. Then, SAPID collects candidate items to recommend based on the distribution of preference scores. Finally, SAPID decides which items to recommend immediately or later according to their estimated exposure opportunities. Extensive experiments show that SAPID shows the state-of-the-art performance in real-world datasets by achieving up to 61.0% increased diversity with 38.9% higher accuracy compared to the second-best competitor.
Dynamic Interaction-Driven Intent Evolver with Semantic Probability Distributions
Authors: Zelin Li, Cheng Zhang, Dawei Song
Venue: WSDM
Year: 2025

Accurately capturing a user's dynamic search intent based on her/his interactions with the system is crucial for improving the performance of session-based search. Existing methods often require the entire interaction sequence within a session to be recomputed continuously at each interaction step, and the token-level interactions are either captured within an overall transformer structure or simply ignored. As a consequence, the current approaches suffer from an increased computation burden and fall short of accurately capturing the dynamic evolution of user intent. In this paper, we propose a novel representation approach which treats both search intent and candidate documents as dimension-specific probability distributions of token embedding representations. Based on this representation, we propose an Dynamic Interaction-Driven intent Evolver (DIDE) for dynamically updating the user's search intent throughout a session with a lightweight similarity calculation method for document ranking. Comprehensive experimental results demonstrate that DIDE adeptly captures the dynamic nature of session-based search and significantly outperforms a range of strong baseline models across three different datasets.
Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
Authors: Yilong Wang, Tianxiang Zhao, Zongyu Wu, Suhang Wang
Venue: WSDM
Year: 2025

Graph neural networks (GNNs) have shown great ability for node classification on graphs. However, the success of GNNs relies on abundant labeled data, while obtaining high-quality labels is costly and challenging, especially for newly emerging domains. Hence, unsupervised domain adaptation (UDA), which trains a classifier on the labeled source graph and adapts it to the unlabeled target graph, is attracting increasing attention. Various approaches have been proposed to alleviate the distribution shift between the source and target graphs to facilitate the classifier adaptation. However, most of them simply adopt existing UDA techniques developed for independent and identically distributed data to gain domain-invariant node embeddings for graphs, which do not fully consider the graph structure and message-passing mechanism of GNNs during the adaptation and will fail when label distribution shift exists among domains. In this paper, we proposed a novel framework that adopts link prediction to connect nodes between source and target graphs, which can facilitate message-passing between the source and target graphs and augment the target nodes to have "in-distribution'' neighborhoods with the source domain. This strategy modified the target graph on the input level to reduce its deviation from the source domain in the embedding space and is insensitive to disproportional label distributions across domains. To prevent the loss of discriminative information in the target graph, we further design a novel identity-preserving learning objective, which guides the learning of the edge insertion module together with reconstruction and adaptation losses. Experimental results on real-world datasets demonstrate the effectiveness of our framework.
Towards Secure and Robust Recommender Systems: A Data-Centric Perspective
Authors: Zongwei Wang, Junliang Yu, Tong Chen, Hongzhi Yin, Shazia Sadiq, Min Gao
Venue: WSDM
Year: 2025

As recommender systems (RS) continue to evolve, the field has seen a pivotal shift from model-centric to data-centric paradigms, where the quality, integrity, and security of data are increasingly becoming the key drivers of system performance and personalization. This transformation has unlocked new avenues for more precise recommendations, yet it also introduces significant challenges. As reliance on data intensifies, RS face mounting threats that can compromise both their effectiveness and user trust. These challenges include (1) Malicious Data Manipulation, where adversaries corrupt or tamper with datasets, distorting recommendation outcomes and undermining system reliability; (2) Data Privacy Leakage, where adversarial actors exploit system outputs to infer sensitive user information, leading to serious privacy concerns; and (3) Erroneous Data Noise, where inaccuracies, inconsistencies, and redundant data obscure the true user preferences, degrading recommendation quality and user satisfaction. By focusing on these critical data-centric challenges, this tutorial aims to equip participants with the knowledge to build RS that are secure, privacy-preserving, and resilient to data-driven threats, ensuring reliable and trustworthy performance in real-world environments. In addition, attendees will gain hands-on experience with our newly released toolkit for RS-based attacks and defenses, providing them with practical, actionable insights into safeguarding RS against emerging vulnerabilities.
MoKGNN: Boosting Graph Neural Networks via Mixture of Generic and Task-Specific Language Models
Authors: Hao Yan, Chaozhuo Li, Jun Yin, Weihao Han, Hao Sun, Senzhang Wang, Jian Zhang, Jianxin Wang
Venue: WSDM
Year: 2025

Text-Attributed Graphs (TAGs) are prevalent in various real-world scenarios, where each node is associated with a text attribute. Representation learning on TAGs relies on a comprehensive understanding of both the textual attributes associated with nodes and the topological connections between nodes. Recent works have tried to enhance graph neural networks (GNNs) with pre-trained language models (PLMs) for textual attribute learning, which has achieved promising result compared with shallow text representation learning models such as BoW. With the advent of more powerful LLMs, it is worth to further exploring how to effectively and efficiently incorporate the generic knowledge in LLMs to enhance GNNs. To this aim, we for the first time investigate whether the mainstream generative LLMs can provide high-quality generic text embedding for GNNs. To simultaneously use the generic knowledge in the LLM and the task-specific knowledge in the tuned PLM (TLM), we propose a dual channel knowledge fusion framework, named MoKGNN, to enhance the representation learning on TAGs. Specifically, we first propose a dual channel feature extraction stage to derive text embeddings that contain generic knowledge and task-specific knowledge separately. Next, we introduce a Knowledge Alignment Network to align the higher-dimensional General Embedding with the lower-dimensional Task-Specific Embedding. Finally, we adopt a mix-up operation to integrate the generic and task-specific textual attribute representation to enhance downstream GNNs. Extensive experiments on four TAG datasets demonstrate the effectiveness of our approach in improving the performance of GNNs in the tasks of node classification and link prediction.
RSM: Reinforced Subgraph Matching Framework with Fine-grained Operation based Search Plan
Authors: Ziming Li, Yuequn Dou, Youhuan Li, Xinhuan Chen, Chuxu Zhang
Venue: WSDM
Year: 2025

Subgraph matching is one of the fundamental problems in graph analytics. Existing methods generate matching orders to guide their search, which consists of a series of extensions. Each time, they extend smaller partial matches into larger ones until all complete answers are obtained. However, these methods have two significant drawbacks. Firstly, their matching order generations are usually heuristic and challenging to be effective for different queries. Secondly, each extension, serving as its computation unit, is coarse-grained and may hinder performance. This granularity issue stems from merging generation and expansion operations into a single computation unit. To address these challenges, we introduce a pioneering framework for Reinforced Subgraph Matching (RSM) that features a fine-grained operation-based search plan. Initially, RSM proposes a fresh paradigm for search, referred to as operation-level search, where each computation unit is defined as an operation that either generates or expands a candidate set under a query vertex. To deal with the second problem and fully exploit the potential of this novel search paradigm, RSM implements a reinforcement learning strategy to generate operation-level search plans. RSM's reinforcement learning approach for constructing operation-based search plans encompasses three modules. In the first module, we employ graph neural networks to extract query vertex representation from graphs. Then, the other two modules leverage multilayer perceptron and are designed to create the generation and expansion operations, respectively. Extensive experiments on real-world graph datasets validate that RSM cuts down query processing time, outperforming existing algorithms by up to 1 to 2 orders of magnitude.
Hawkes Point Process-enhanced Dynamic Graph Neural Network
Authors: Zhiqiang Wang, Baijing Hu, Kaixuan Yao, Jiye Liang
Venue: WSDM
Year: 2025

Dynamic graph representation learning aims to capture the evolution of graph structures and obtain accurate node embeddings, a crucial task in graph machine learning. The Hawkes point process, a mathematical framework effective for modeling the influence of historical events on future occurrences, has been validated as a powerful tool for capturing the dynamics of graph evolution in dynamic graph representation learning. However, existing dynamic graph representation learning methods based on the Hawkes point process primarily model excitation at the individual node level, failing to adequately account for structural influences during graph evolution. This limitation restricts their ability to comprehensively capture network evolution patterns. To address this limitation, we propose a Hawkes Point Process-enhanced Dynamic Graph Neural Network (HP-DGNN) model. This model leverages the Hawkes point process to model both individual node histories and structural histories, capturing their respective influences on future node interactions. By integrating individual and structural influences in computing Hawkes conditional intensity, the model comprehensively captures the impacts of both layers on future node interactions. We evaluate our proposed model on two downstream tasks of dynamic graph representation learning: dynamic link prediction and future node degree prediction. Compared to 12 state-of-the-art methods, our model consistently demonstrates superior performance, underscoring its effectiveness in capturing the complexities of graph evolution.
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
Authors: Kexin Zhang, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Kaize Ding, Xiuqiang He, Xue Liu
Venue: WSDM
Year: 2025

The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature interactions. However, there has been less focus on improving fusion design. Instead, two naive solutions, stacked and parallel fusion, are commonly used. Both solutions rely on pre-determined fusion connections and fixed fusion operations. It has been repetitively observed that changes in fusion design may result in different performances, highlighting the critical role that fusion plays in CTR models. While there have been attempts to refine these basic fusion strategies, these efforts have often been constrained to specific settings or dependent on specific components. Neural architecture search has also been introduced to partially deal with fusion design, but it comes with limitations. The complexity of the search space can lead to inefficient and ineffective results. To bridge this gap, we introduce OptFusion, a method that automates the learning of fusion, encompassing both the connection learning and the operation selection. We have proposed a one-shot learning algorithm tackling these tasks concurrently. Our experiments are conducted over three large-scale datasets. Extensive experiments prove both the effectiveness and efficiency of OptFusion in improving CTR model performance. Our code implementation is available here https://github.com/kexin-kxzhang/OptFusion.
Advancing Voice AI for E-commerce: Tracking ASR Model Performance at Scale
Authors: Dhruv Agarwal, Nupur Neti, Federica Cerina
Venue: WSDM
Year: 2025

Traditionally, automatic speech recognition (ASR) systems rely on human transcriptions to calculate word error rate (WER) by comparing ASR outputs to manual transcriptions. Recently, Amazon's mobile voice shopping platform stopped storing audio from incoming requests to enhance customer privacy, making offline, human-based evaluation unfeasible. This presentation introduces a multitask Speech LLM-based system that processes real-time audio, extracting key features to track ASR performance and detect traffic shifts-all without storing audio or requiring human annotations. Additionally, we demonstrate how combining these features with a synthetic audio generation model (TTS) enables accurate detection of ASR performance degradation, ensuring continuous optimization of the customer voice experience.
Large Language Model driven Policy Exploration for Recommender Systems
Authors: Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
Venue: WSDM
Year: 2025

Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP). However, offline RL policies trained on static user data are vulnerable to distribution shift when deployed in dynamic online environments. Additionally, excessive focus on exploiting short-term relevant items can hinder exploration, leading to sub-optimal recommendations and negatively impacting long-term user gains. Online RL-based RS also face challenges in production deployment, due to the risks of exposing users to untrained or unstable policies. Large Language Models (LLMs) offer a promising solution to mimic user objectives and preferences for pre-training policies offline to enhance the initial recommendations in online settings. Effectively managing distribution shift and balancing exploration are crucial for improving RL-based RS, especially when leveraging LLM-based pre-training. To address these challenges, we propose an Interaction-Augmented Learned Policy (iALP) that utilizes user preferences distilled from an LLM. Our approach involves prompting the LLM with user states to extract item preferences, learning rewards based on feedback, and updating the RL policy using an actor-critic framework. Furthermore, to deploy iALP in an online scenario, we introduce an adaptive variant, A-iALP, that implements a simple fine-tuning strategy (A-iALPft), and an adaptive approach (A-iALPap) designed to mitigate issues with compromised policies and limited exploration. Experiments across three simulated environments demonstrate that A-iALP introduces substantial performance improvements.
SAGESSE: A System for Argument Generation, Extraction and Structuring of Social Exchanges
Authors: Nicolas Almerge, Matteo Santelmo, Ilker Gül, Amin Asadi Sarijalou, Rémi Lebret, Léo Laugier, Karl Aberer
Venue: WSDM
Year: 2025

Online debates provide critical insights into public opinion and societal trends, yet the unstructured nature of these discussions presents significant challenges for analysis. In this paper, we present SAGESSE, a novel argumentation parsing pipeline tailored to Reddit debates, leveraging the capabilities of large language models to structure and interpret complex online arguments. SAGESSE generates detailed argument maps that organize debates systematically, offering a clearer understanding of discourse dynamics. We have developed a web application where users can select controversial Reddit topics and visualize the corresponding argument maps generated from user comments. This tool has the potential to aid analysts, policymakers, and researchers in tracking debate progress, gauging public sentiment, and identifying influential arguments. The web application is available at https://modemos.epfl.ch/sagesse/ .
The Initial Screening Order Problem
Authors: José M. Álvarez, Antonio Mastropietro, Salvatore Ruggieri
Venue: WSDM
Year: 2025

We investigate the role of the initial screening order (ISO) in candidate screening. The ISO refers to the order in which the screener searches the candidate pool when selecting k candidates. Today, it is common for the ISO to be the product of an information access system, such as an online platform or a database query. The ISO has been largely overlooked in the literature, despite its impact on the optimality and fairness of the selected k candidates, especially under a human screener. We define two problem formulations describing the search behavior of the screener given an ISO: the best-k, where it selects the top k candidates; and the good-k, where it selects the first good-enough k candidates. To study the impact of the ISO, we introduce a human-like screener and compare it to its algorithmic counterpart, where the human-like screener is conceived to be inconsistent over time. Our analysis, in particular, shows that the ISO, under a human-like screener solving for the good-k problem, hinders individual fairness despite meeting group fairness, and hampers the optimality of the selected k candidates. This is due to position bias, where a candidate's evaluation is affected by its position within the ISO. We report extensive simulated experiments exploring the parameters of the best-k and good-k problems for both screeners. Our simulation framework is flexible enough to account for multiple candidate screening tasks, being an alternative to running real-world procedures.
Mind Over Machine: Evaluating Theory of Mind Reasoning in LLMs and Humans
Authors: Maryam Amirizaniani
Venue: WSDM
Year: 2025

As Large Language Models (LLMs) become increasingly integrated into applications demanding human-like understanding-such as mental health support, education, and social robotics-their capacity to exhibit Theory of Mind (ToM) reasoning is essential. Although previous research has evaluated LLMs' capability in ToM tasks, a critical gap remains as few studies have systematically investigated how LLMs' ToM reasoning diverges from human reasoning and the extent of these differences. This study introduces a reinforcement learning-based framework designed to bridge this gap. This approach seeks to enhance LLM alignment with human ToM reasoning, effectively narrowing the differences in their reasoning processes. Finally, future research directions to advance this field will be discussed, including strategies for developing LLMs that can better approximate human social cognition. This work lays a foundation for responsible LLM deployment, offering guidelines for applications in sensitive contexts where accurate ToM understanding is crucial.