OpenRaiser/Intern-Atlas huggingface dataset:
Converted Data is All You Need for Causal Optimization of e-Commerce Promotions
Authors: Dmitri Goldenberg, Hugo Manuel Proença, Amit Livne, Felipe Moraes, Javier Albert, Bracha Shapira
Venue: CIKM
Year: 2025

Promotional campaigns are essential drivers of customer engagement and revenue in e-commerce. Maintaining these campaigns within budget constraints requires targeted allocation, traditionally achieved through causal uplift models that rely on vast datasets of user interactions, including non-converted sessions, which introduce challenges such as noisy data, attribution complexity and imbalanced outcomes. We propose a novel approach using converted-only data, which reduces training data size, simplifies attribution, improves efficiency, and mitigates the impact of non-converted interactions. We present a generalized framework for budget constrained promotion allocation with converted-only data and validate it through a benchmarking study and multiple large-scale deployments at Booking.com, positively impacting the experience of millions of customers worldwide. Our results demonstrate that the proposed method is competitive with standard modeling approaches and, in some cases, significantly outperforms them.
PathLens: Structurally Enhancing Heterophilic Graphs for GNNs
Authors: Karan Goyal, Saankhya Samanta, Vikram Goyal, Mukesh K. Mohania
Venue: CIKM
Year: 2025

The notion that standard GNNs perform better on graphs with high homophily, led to the development of specialised algorithms for heterophilic datasets in recent years. In this work, we both examine and leverage this notion. Rather than creating new algorithms, we emphasise the importance of understanding and enriching the data. We introduce a novel data engineering technique, PathLens, that enhances the performance of both heterophilic and non-heterophilic GNNs on heterophilic datasets. Our method structurally augments a given heterophilic graph by adding supernodes, thereby creating a network of pathways connecting spectral clusters in the graph. It facilitates additional paths to bring similar nodes (intraclass) closer than dissimilar ones (interclass) by reducing the average shortest path lengths. We draw both intuitive and empirical connections between the relative decreases in intraclass and interclass average shortest path lengths and shifts in the graph's homophily levels, providing a novel perspective that extends beyond traditional homophily measures. We conduct extensive experiments on seven diverse heterophilic datasets using various GNN architectures and also compare with several data-centric techniques, demonstrating significant improvements as high as 37% in node classification performance. Furthermore, our empirical findings highlight the strong sensitivity of several recent GNNs with respect to the random seed used for data splitting, underscoring this often-overlooked factor in GNN evaluation. The code will be available at https://github.com/goyalkaraniit/PathLens.
A Large-Scale Web Search Dataset for Federated Online Learning to Rank
Authors: Marcel Gregoriadis, Jingwei Kang, Johan Pouwelse
Venue: CIKM
Year: 2025

The centralized collection of search interaction logs for training ranking models raises significant privacy concerns. Federated Online Learning to Rank (FOLTR) offers a privacy-preserving alternative by enabling collaborative model training without sharing raw user data. However, benchmarks in FOLTR are largely based on random partitioning of classical learning-to-rank datasets, simulated user clicks, and the assumption of synchronous client participation. This oversimplifies real-world dynamics and undermines the realism of experimental results. We present AOL4FOLTR, a large-scale web search dataset with ≈ 2.6 million queries from 10,000 users. Our dataset addresses key limitations of existing benchmarks by including user identifiers, real click data, and query timestamps, enabling realistic user partitioning, behavior modeling, and asynchronous federated learning scenarios.
SSH-T3 : A Hierarchical Pre-training Framework for Multi-Scenario Financial Risk Assessment
Authors: Zehao Gu, Yateng Tang, Jiarong Xu, Siwei Zhang, Xuehao Zheng, Xi Chen, Yun Xiong
Venue: CIKM
Year: 2025

Efficiently modeling user behavior on online payment platforms is crucial for accurately identifying potential financial risks. With the rapid growth of online payment platforms, the volume of user transaction data has significantly increased. Moreover, users' payment behaviors often encompass diverse activities and interactions across multiple scenarios. Based on observations from online payment platforms, we identify three key challenges: scarce labels and poor representation robustness, long user payment behavior sequences, and complex and heterogeneous amount-aware scenarios. To address these challenges, we propose a novel Self-Supervised Hierarchical Two-Tower Transformer SSH-T3, specifically designed for multi-scenario financial risk assessments. We introduce a masked modeling pre-training task to reconstruct multi-scenario day-level transaction amount distributions, effectively mitigating behavior-level noise and enhancing representation robustness. Additionally, we propose a hierarchical Multi-Scenario Payment Behavior Sequence (MS-PBS) modeling approach tailored to business needs, which significantly reduces complexity while capturing user behavior patterns more effectively through day-level representations. Furthermore, we highlight the critical importance of correlating multi-scenario data in MS-PBS modeling to better identify defaulter patterns. To this end, we design a Two-Tower Transformer equipped with a specialized attention mechanism that captures intricate user patterns across scenarios. Extensive experiments conducted on both offline and online real-world business datasets demonstrate the effectiveness and applicability of SSH-T3.
A Large-Scale Dataset of Interactions Between Weibo Users and Platform-Empowered LLM Agent
Authors: Shaokui Gu, Yongjie Yin, Qingyuan Gong, Fenghua Tong, Yipeng Zhou, Qiang Duan, Yang Chen
Venue: CIKM
Year: 2025

We release a large-scale dataset that captures interactions between human users and CommentRobert, an LLM-based social media agent on Weibo. The dataset contains Weibo posts in which users actively mention the LLM agent account @CommentRobert, indicating that the users are interested in interacting with the platform-empowered LLM agent. The dataset contains 557,645 interactions from 304,400 unique users over 17 months. We detail our data collection methodology, user attributes, and content characteristics, underscoring the dataset's value in examining real-world human-LLM agent interactions. Our analysis offers insights into the demographic and behavioral traits of users interested in the selected LLM agent, interaction dynamics between humans and the agent, and linguistic patterns in comments. These interactions provide a unique lens through which to explore how humans perceive, trust, and communicate with LLMs. This dataset enables further research into modeling human intent understanding, improving LLM agent design, and studying the evolution of human-LLM agent relationships. Potential applications also include long-term user engagement prediction and AI-generated comment detection on social platforms. This constructed dataset is available at https://zenodo.org/records/16921462.
PERC: A Prior-Guided Framework for Classifying Long-Content Educational Resources with Imbalanced Category Distributions
Authors: Quanlong Guan, Xiuliang Duan, Zhi Chen, Xingyu Zhu, Jianbo Huang, Xinzhong Liu, Zonglin Liu, Liangda Fang
Venue: CIKM
Year: 2025

With the rapid growth of online education, the types and volumes of educational resources have increased significantly. Efficient classification of these resources can substantially reduce manual workload and enhance management effectiveness. However, existing models often struggle to accurately classify long-content educational content, particularly under imbalanced category distributions. To address these challenges, we propose PERC, a prior-guided framework for classifying long educational resources with imbalanced categories. To the best of our knowledge, PERC is the first framework to incorporate the foundational cognitive dimensions of Bloom's Taxonomy into educational resource classification. First, PERC leverages standardized pedagogical classification guidelines and maps the original label space into a semantically structured prior category space using a Structured State-Space Learning framework. Second, to handle the length and high information density of educational texts, we introduce a Dynamic Sliding Window Attention mechanism that captures both local and partial global dependencies, enabling the extraction of compact, semantically rich representations. Finally, a category-aware classifier integrates the prior representation of each category with the semantic representation of the resource to produce a category-aware embedding for final prediction. To evaluate PERC, we constructed two datasets: EduMix-24 and EduMath-24, comprising 18,799 educational resources manually annotated across 9 lesson types, 15 teaching modes, and 9 activity elements. Classification experiments on all three tasks consistently demonstrate that PERC outperforms state-of-the-art baselines.
PersonaGen: A Persona-Driven Open-Ended Machine-Generated Text Dataset
Authors: Carmelo Gugliotta, Lucio La Cava, Andrea Tagarelli
Venue: CIKM
Year: 2025

We present PersonaGen, a novel dataset for investigating persona-driven machine-generated text (MGT) produced by Open Large Language Models (OLLMS). PersonaGen is specifically designed to investigate how synthetic persona profiles affect, guide, or manifest in MGT. We built PersonaGen by pairing curated persona-profiles (i.e., description of characteristics, background, and goals) across eight thematic domains (e.g., Physics, Education, Medicine) with prompts covering various narrative or opinion-style content (e.g., stories, commonsense). Open-ended generations were produced by six representative OLLMs, yielding a total of 1.44 million persona-driven generations. PersonaGen supports multiple research tasks, such as machine-generated text attribution, persona category detection, and persona profile identification, thus providing a valuable resource for studying LLM controllability and role-playing behavior, as well as the impact of persona profile conditioning in downstream tasks. We have released PersonaGen on the Hugging Face platform at https://doi.org/10.57967/hf/5805.
When Variety Seeking Meets Multi-Sided Recommendation Fairness: A Consistent and Personalized Multi-Objective Optimization Framework
Authors: Jiayi Guo, Jiangning He, Chenyan Wang, Xinran Wu
Venue: CIKM
Year: 2025

Recommendation research has evolved from solely improving accuracy to addressing ethical and fairness concerns. While prior works focus on optimizing fairness from either the user or product perspective, recent research emphasizes the importance of multi-sided fairness. This issue is inherently challenging due to the competing goals of different stakeholders. To tackle this challenge, we propose a Consistent and Personalized Fairness Recommendation framework with Multi-Objective Integer Programming (CPFR-MOIP). Our framework introduces two key innovations. First, we develop a novel similarity-based individual fairness metric for the user side and formulate a consistent product-side fairness metric, ensuring that the generated recommendation list aligns with the user preference distribution and the expected product exposure distribution. Second, we incorporate users' variety-seeking levels as a moderating factor to adjust fairness trade-offs and introduce personalized weights to balance user-side and product-side fairness. To effectively solve this optimization problem, we devise an alternating algorithm with theoretical guarantee and demonstrate the Pareto optimality of the obtained solutions. Extensive experiments on two real-world datasets demonstrate that our CPFR-MOIP achieves superior multi-sided fairness while maintaining competitive recommendation accuracy. Furthermore, ablation analysis highlights the advantages of incorporating user variety-seeking levels for personalizing fairness trade-offs. Our work paves the way for more ethical and personalized recommendation systems. The implementation code is available at: https://github.com/P0ise-Wang/CPFR-MOIP.
Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions
Authors: Kehan Guo, Zhen Liu, Zhichun Guo, Bozhao Nan, Olexandr Isayev, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
Venue: CIKM
Year: 2025

Reaction yield prediction underpins computer-aided synthesis prediction (CASP). Formulated as a regression problem that takes both reactants and products as input, this task has been extensively studied using machine learning methods, based on handcrafted fingerprint features, SMILES encoded by Transformers, and molecular graphs encoded by Graph Neural Networks. However, a major limitation of these methods is their inability to effectively capture and model the underlying uncertainties, arising both from the inherently stochastic nature of chemical reaction processes and from inconsistencies or noise in how yields are measured and reported. What makes this seemingly simple regression problem even more challenging is the lack of any principled way to account for the underlying uncertainties, due to missing or unrecorded experimental process (commonly happens in chemical labs). Given these challenges, we propose a new formulation for yield prediction. Rather than assuming a single deterministic yield value for a given reaction, we model the outcome as a probabilistic distribution over three discrete yield regimes: high, medium, and low, reflecting the inherent uncertainty in the reaction process, which is often only partially observed. Accordingly, we propose Proto-Yield, an encoder-agnostic prototype network that models reactions as occurring in one of three yield regimes: high, medium, or low. Without access to full reaction processes, Proto-Yield learns to infer latent regimes and their associated yield distributions from noisy, incomplete training data. During inference, Proto-Yield outputs both a calibrated probability distribution over the yield regimes and the predicted yield conditioned on each regime. Extensive experiments on a 41,000-reaction patent corpus and two high-throughput benchmarks show that Proto-Yield improves R2 by up to 15% and reduces RMSE/MAE by 13% compared to baseline methods.
MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation
Authors: Chengcheng Guo, Junda She, Kuo Cai, Shiyao Wang, Qigen Hu, Qiang Luo, Guorui Zhou, Kun Gai
Venue: CIKM
Year: 2025

Large-scale industrial recommendation systems typically employ a two-stage paradigm of retrieval and ranking to handle huge amounts of information. Recent research focuses on improving the performance of retrieval model. A promising way is to introduce extensive information about users and items. On one hand, lifelong sequential behavior is valuable. Existing lifelong behavior modeling methods in ranking stage focus on the interaction of lifelong behavior and candidate items from retrieval stage. In retrieval stage, it is difficult to utilize lifelong behavior because of a large corpus of candidate items. On the other hand, existing retrieval methods mostly relay on interaction information, potentially disregarding valuable multi-modal information. To solve these problems, we represent the pioneering exploration of leveraging multi-modal information and lifelong sequence model within the advanced tree-based retrieval model. We propose Multi-modal Indexing and Searching with lifelong Sequence (MISS), which contains a multi-modal index tree and a multi-modal lifelong sequence modeling module. Specifically, for better index structure, we propose multi-modal index tree, which is built using the multi-modal embedding to precisely represent item similarity. To precisely capture diverse user interests in user lifelong sequence, we propose collaborative general search unit (Co-GSU) and multi-modal general search unit (MM-GSU) for multi-perspective interests searching.
Time-Period-Aware Embedding Regeneration for Session-Based Recommendation
Authors: Cheng Guo, Rui Xue, Jeff Zhang
Venue: CIKM
Year: 2025

Session-based recommender systems typically focus on intra-session user behavior but often overlook the macro-level temporal evolution of items themselves. To address this gap, we introduce a model that explicitly captures item dynamics by regenerating time-period-aware embeddings. Our approach partitions the data timeline into several distinct periods and employs a simple yet effective module, TEG, which uses a GRU and a causal attention layer to recurrently learn how item representations evolve from one period to the next. This allows our model to capture global, long-term trends while its gating mechanism naturally accommodates both dynamic and static items. Extensive experiments on three real-world datasets show that our method achieves highly competitive performance against complex state-of-the-art models. More importantly, it demonstrates the significant and complementary value of modeling global item evolution, providing a new dimension for improving session-based recommendation.
Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services
Authors: Hongcheng Guo, Zheyong Xie, Shaosheng Cao, Boyang Wang, Weiting Liu, Zheyu Ye, Zhoujun Li, Zuozhu Liu, Wei Lu
Venue: CIKM
Year: 2025

As interest in using Large Language Models for interactive and emotionally rich experiences grows, virtual pet companionship emerges as a novel yet underexplored application. Existing approaches focus on basic pet role-playing interactions without systematically benchmarking LLMs for comprehensive companionship. In this paper, we introduce PET-BENCH, a dedicated benchmark that evaluates LLMs across both self-interaction and human-interaction dimensions. Unlike prior work, PET-BENCH emphasizes self-evolution and developmental behaviors alongside interactive engagement, offering a more realistic reflection of pet companionship. It features diverse tasks such as intelligent scheduling, memory-based dialogues, and psychological conversations, with over 7,500 interaction instances designed to simulate pet behaviors. Evaluation of 28 LLMs reveals significant performance variations linked to model size and inherent capabilities, underscoring the need for specialized optimization in this domain. PET-BENCH serves as a foundational resource for benchmarking pet-related LLM abilities and advancing emotionally immersive human-pet interactions.
Twin-Flow Generative Ranking Network for Recommendation
Authors: Hao Guo, Erpeng Xue, Lei Huang, Shichao Wang, Xiaolei Wang, Lei Wang, Jinpeng Wang, Zeshun Li, Sheng Chen
Venue: CIKM
Year: 2025

Deep Learning Recommendation Models (DLRMs) often rely on extensive manual feature engineering to improve accuracy and user experience, which increases system complexity and limits scalability of model performance with respect to computational resources. Recently, Meta introduced a generative ranking paradigm based on HSTU block that enables end-to-end learning from raw user behavior sequences and demonstrates scaling law on large datasets that can be regarded as the state-of-the-art (SOTA). However, splitting user behaviors into interleaved item and action information significantly increases the input sequence length, which adversely affects both training and inference efficiency. To address this issue, we propose the Twin-Flow Generative Ranking Network (TFGR), that employs a Twin-flow mechanism to optimize interaction modeling, ensuring efficient training and inference through end-to-end token processing. TFGR duplicates the original user behavior sequence into a real flow and a fake flow based on the authenticity of the action information, and then defines a novel interaction method between the real flow and the fake flow within the QKV module of the self-attention mechanism. This design reduces computational overhead and improves both training efficiency and inference performance compared to Meta's HSTU-based model. Experiments on both open-source and real industrial datasets show that TFGR outperforms DLRM, which serves as the industrial online baseline with extensive feature engineering, as well as Meta's HSTU and other common recommendation models such as DIN, DCN, DIEN, and DeepFM. Furthermore, we investigate optimal parameter allocation strategies under computational constraints, establishing TFGR as an efficient and effective next-generation generative ranking paradigm.
Pseudo-Inverse Prefix Tuning for Effective Unlearning in LLMs
Authors: Preethi Gurumurthy, P. K. Srijith
Venue: CIKM
Year: 2025

Large Language Models (LLMs) are widely used in many real-world applications, but their deployment raises concerns about data privacy and compliance with regulations such as the right to be forgotten. To address these challenges, we explore the problem of machine unlearning, selectively removing the influence of specific training data from a model. While many existing approaches require retraining the entire model and access to both forget data and retain data, we propose Pseudo-Inverse Prefix Tuning (PI-Prefix), a parameter-efficient fine-tuning method that enables targeted forgetting with minimal overhead. PI-Prefix learns a small set of prefix parameters on the data to be forgotten and then applies pseudo-inverse transformation to unlearn the forget data while maintaining performance on retain data. Our experiments on two sentiment classification tasks (SST-2 and Yelp) demonstrate that PI-Prefix achieves effective and interpretable forgetting, with forget-set performance approaching random prediction. It preserves a strong generalization on the retain set even without requiring it during unlearning. These results highlight PI-Prefix as a promising direction for scalable and compliant unlearning in data removal contexts.