OpenRaiser/Intern-Atlas huggingface dataset:
LEI: Reinforced Multi-Object Cache Admission
Authors: Hexuan Lv, Yuhai Zhao, Sice Wang
Venue: CIKM
Year: 2025

The Hot Object Cache (HOC) admission policy is one of the core technologies in Content Delivery Network (CDN) cache management and plays a critical role in the broader Computing Power Network (CPN) environment. It primarily employs heuristic- and threshold-based methods. These methods are simple and efficient but fail both to capture latent dependencies among requested objects and to support informed admission decisions. We propose a reinforcement learning approach called LEI for multi-object CDN admission (i.e., sequences of request objects across multiple time instants, which include multiple objects requested at each instant). LEI integrates three key components: a Multi-Object Time Encoding (MO-TE) mechanism, which uses density-based representations to model multi-object temporal sequences; a Buffer Mechanism, which addresses the reward-latency issue by buffering historical decision information; and a dual-head network architecture with a two-stage training strategy, which enhances the model's admission decision-making capability and long-term stability. Experiments on four public datasets demonstrate that LEI improves the HOC request hit rate by 5-12% and the HOC byte hit rate by 5-42% compared with state-of-the-art methods. It also reduces disk cache (DC) read and write rates.
LLM4IA: Index Advising Via Large Language Models
Authors: Xian Lyu, Junbiao Zhang, Yihang Zheng, Guoliang Li, Chen Lin
Venue: CIKM
Year: 2025

Recently, large-language models (LLMs) have demonstrated strong potential to solve database problems. However, LLMs still face two challenges in solving the index selection problem: (1) representing the workload in an LLM-friendly form and (2) finding the optimal index set. To solve these challenges, we propose LLM4IA, an LLM-based index selection method that can recommend indexes for any analytical workload directly on any database instance. LLM4IA proposes a concise description of natural language by extracting and sorting predicates and completely avoiding numerical input. LLM4IA adopts an iterative index selection process by repeatedly improving previous index candidates and summarizing effective candidates. Experiments on TPC-H and TPC-DS show that LLM4IA surpasses the near-optimal index advisor Extend by 5%-10%. Our demonstration highlights how LLM4IA recommends high-quality indexes for a new database instance without expensive retraining or fine-tuning.
Image Hashing Based on Hamming Ball Spacing
Authors: Zhaomeng Ma, Haoran Chang, Hailong Shen, Yanzhi Song
Venue: CIKM
Year: 2025

Image hashing has been widely used in large-scale image retrieval due to its enhancement of storage space and retrieval speed. Recently, methods based on hash centers have achieved impressive retrieval performance, aiming to assign mutually separated hash codes as center points for each category and learn compact binary codes by minimizing intra-class variance. However, current methods tend to generate codebooks in advance, which are then used as pre-defined hash centers, leading to compromised quality of hash centers in instance-level datasets with excessive categories. To address this problem, we designed a training strategy that learns hash centers by constraining the margin of category clusters in Hamming space, during which the Gilbert-Varshamov bound and the upper bound of Hamming ball packing from coding theory are utilized to determine the range of margins. Finally, we jointly optimize hash encoder and hash centers to improve retrieval performance. Extensive experiments on more challenging large-scale instance-level datasets demonstrate that our method effectively overcomes the limitation of retrieval performance imposed by the number of categories. Code is at: https://github.com/GrimmAI/Hamming-Ball-Hashing.git.
From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media
Authors: Tian Ma, Kaiyu Feng, Yu Rong, Kangfei Zhao
Venue: CIKM
Year: 2025

Personality prediction from social media posts is a critical task that implies diverse applications in psychology and sociology. The Myers-Briggs Type Indicator (MBTI), a popular personality inventory, has been traditionally predicted by machine learning (ML) and deep learning (DL) techniques. Recently, the success of Large Language Models (LLMs) has revealed their huge potential in understanding and inferring personality traits from social media content. However, directly exploiting LLMs for MBTI prediction faces two key challenges: the hallucination problem inherent in LLMs and the naturally imbalanced distribution of MBTI types in the population. In this paper, we propose PostToPersonality (P2P), a novel LLM- based framework for MBTI prediction from social media posts of individuals. Specifically, P2P leverages Retrieval-Augmented Generation with in-context learning to mitigate hallucination in LLMs. Furthermore, we fine-tune a pre-trained LLM to improve model specification in MBTI understanding with synthetic minority oversampling, which balances the class imbalance by generating synthetic samples. Experiments conducted on a real-world social media dataset demonstrate that P2P achieves state-of-the-art performance compared with 10 ML/DL baselines.
SMTIR: Scenario-Aware Multi-Trigger Induction Network for CTR Prediction
Authors: Xuan Ma, Yu Shi, Hao Peng, Jia Duan, Zhanhao Ye, Kunyao Wang, Kai Yan, Long Chen, Zehua Zhang, Changping Peng, Zhangang Lin, Ching Law
Venue: CIKM
Year: 2025

Trigger-Induced Recommendation (TIR), which aims to predict user interest based on a trigger item, has gained considerable traction on e-commerce platforms. Current TIR methods typically analyze user intent by integrating explicit interest in the trigger item and implicit interest derived from user historical behaviors. However, these methods often overlook the contextual information and occurring scenarios related to the trigger, resulting in an undue emphasis on isolated trigger items and a consequently restrictive understanding of users' short-term intentions. To address these challenges, we propose a novel scenario-aware multi-trigger induction method featuring three key enhancements: (1) The Context Modeling Network learns contextual information associated with the trigger during the request, improving the understanding of users' real intentions regarding the trigger item; (2) The Multi-Trigger Learning Network introduces user latent triggers from various scenarios to uncover users' potential external preferences; (3) The Scenario Induction Network captures the characteristics of the scenarios in which triggers occur and performs induction to yield scenario-aware user intentions prediction. We validate our approach through experiments on multiple industrial datasets, demonstrating the model's effectiveness. Furthermore, we have integrated the model into an online advertising system, achieving a 5.46% improvement in Click-Through Rate (CTR).
Reconsidering the Performance of GAE in Link Prediction
Authors: Weishuo Ma, Yanbo Wang, Xiyuan Wang, Muhan Zhang
Venue: CIKM
Year: 2025

Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. However, reliance on outdated baselines may exaggerate the benefits of these new approaches. To tackle this issue, we systematically explore Graph Autoencoders (GAEs) by applying model-agnostic tricks in recent methods and tuning hyperparameters. We find that a well-tuned GAE can match the performance of recent sophisticated models while offering superior computational efficiency on widely used link prediction benchmarks. Our approach delivers substantial performance gains on datasets where structural information dominates and feature data is limited. Specifically, our GAE achieves a state-of-the-art (SOTA) Hits@100 score of 78.41% on the ogbl-ppa dataset. Furthermore, we examine the impact of various tricks to uncover the reasons behind our success and to guide the design of future methods. Our study emphasizes the critical need to update baselines for a more accurate assessment of progress in GNNs for link prediction. Our code is available at https://github.com/GraphPKU/Refined-GAE.
Multimodal Sentiment Analysis with Multi-Perspective Thinking via Large Multimodal Models
Authors: Juhao Ma, Shuai Xu, Yicong Li, Xiaoming Fu
Venue: CIKM
Year: 2025

Multimodal sentiment analysis (MSA) is attracting increasing attention from researchers. Existing studies on MSA typically rely on surface-level feature extraction and fusion that can be directly obtained from multimodal data, which may often ignore the underlying semantic connection between images and texts. Recent progress in large multimodal models (LMMs) has demonstrated their impressive reasoning abilities, which can be leveraged to improve traditional MSA approaches by providing a deeper understanding of the sematic connection of the modalities. Toward this issue, in this paper, we propose a novel framework called MPT that combines traditional MSA approaches with Multi-Perspective Thinking from LMMs to improve prediction outcomes. Specifically, MPT instructs the traditional multimodal deep learning models to understand multiple-perspective rationales for different sentiment polarities, augmenting its knowledge base and enhancing its ability to make more accurate predictions. Extensive experiments on four refined datasets show that MPT can not only deliver better performance compared with existing methods, but also demonstrate good cross-modal understanding ability for recognizing user sentiment. The codes and datasets can be accessed here: https://github.com/RMJHQwQ/MPT.
kNNBE: Incorporating Labeled Sentences in Bi-encoder Inference for Fast and Accurate Skill Mapping
Authors: Takuya Makino
Venue: CIKM
Year: 2025

Skill mapping is a key task in the Human Resources domain. It consists in identifying ontology-defined skills in job texts. Among the most successful approaches applied to skill mapping, bi-encoders offer efficient inference but struggle with fine-grained skill distinctions, particularly under limited supervision. While accurate, cross-encoder and LLM-based reranking approaches are computationally expensive and usually not feasible to be adopted in real case scenarios. We propose kNNBE, a hybrid inference method that augments bi-encoder similarity scores with k-nearest labeled sentences drawn from a synthetic memory bank. kNNBE improves both prediction accuracy and generalization to unseen skills while retaining high throughput. Extensive experiments on three benchmark datasets show that kNNBE rivals state-of-the-art rerankers in accuracy while being orders of magnitude faster.
IclForge: Enhancing In-Context Learning with Evolutionary Algorithms under Budgeted Annotation
Authors: Vijit Malik, Atul Pande, Anirban Majumder
Venue: CIKM
Year: 2025

In-context learning (ICL) has emerged as a powerful paradigm for adapting Large Language Models (LLMs) to specific tasks without parameter updates. While various strategies exist for selecting relevant ICL exemplars from a labeled pool, the fundamental challenge of constructing this high-quality pool remains largely unexplored, especially for new tasks or domains with limited labeled data. We present IclForge, a novel active learning framework that efficiently selects informative examples from unlabeled datasets to be annotated and included in the ICL pool. Unlike traditional active learning methods that optimize for individual example informativeness, IclForge explicitly considers the interdependence of examples within the ICL context. Through extensive experiments across diverse datasets and LLM architectures, we show that IclForge outperforms standard active learning baselines by +180-450 basis points while requiring 50% fewer annotations. Our framework is complementary to existing ICL selection strategies and extends naturally to generative applications, which we demonstrate through experiments on Math Word Problem (MWP) tasks. These results highlight IclForge's effectiveness in constructing high-quality ICL exemplar pools in resource-constrained scenarios.
Multi-Task Learning through Hierarchical Information Sharing and Transfer
Authors: Yufan Mao, Jingran Xu, Liang Zhang, Xiyue Hou, Yongbo Jin, Yingming Li, Linjian Mo
Venue: CIKM
Year: 2025

In this work, we propose a novel Hierarchical Information Sharing and Transfer (HIST) framework for multi-task learning, which employs implicit shared-bottom pattern and explicit sequential transfer at tower-level simultaneously. In particular, a multi-level gating mixture-of-experts is presented for efficient bottom-level sharing. Further, self-attention mechanism is adopted for information transfer between task-specific towers. Such hierarchical task interaction scheme leads to a remarkable enhancement in multi-task learning settings. Extensive experiments on four subsets of AliExpress dataset unequivocally demonstrate that HIST outperforms the current state-of-the-art methods consistently.
Leveraging Intra-Modal Consistency for Cross-Modal Alignment and Retrieval
Authors: Fengyang Mao, Xiaodong Yue, Yufei Chen, Jiaqing Ma, Zheran Zhang, Jie Shi
Venue: CIKM
Year: 2025

Cross-modal retrieval aims to match videos and texts by mapping them into a shared feature space. Most existing approaches achieve alignment through contrastive learning based on one-to-one supervised pairs. However, these methods rely too much on supervised signals and do not fully use the unsupervised semantic relationships within each modality. As a result, samples that are semantically similar may be spread in the shared space, which hurts retrieval performance. To solve this problem, we propose a method called Leveraging Intra-Modal Consistency for Cross-Modal Alignment and Retrieval (LICA). Our method introduces a consistency constraint between intra-modal similarities and cross-modal similarity distributions. In this way, samples that are close in meaning stay closer together in the shared space. Experiments on standard text-video retrieval benchmarks show that LICA helps optimize the distribution of the cross-modal feature space and improves retrieval accuracy.
As Good as It KAN Get: High-Fidelity Audio Representation
Authors: Patryk Marszalek, Maciej Rut, Piotr Kawa, Przemyslaw Spurek, Piotr Syga
Venue: CIKM
Year: 2025

Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perceptual performance over previous INRs, achieving the lowest Log-Spectral Distance of 1.29 and the highest Perceptual Evaluation of Speech Quality of 3.57 for 1.5~s audio. To extend KAN's utility, we propose FewSound, a hypernetwork-based architecture that enhances INR parameter updates. FewSound outperforms the state-of-the-art HyperSound, with a 33.3% improvement in MSE and 60.87% in SI-SNR. These results show KAN as a robust and adaptable audio representation with the potential for scalability and integration into various hypernetwork frameworks.
Real until proven fake - Source-Level Audio Deepfake Detection (with PIPNet)
Authors: Alicja Martinek
Venue: CIKM
Year: 2025

The rapid development of synthetic speech technologies poses significant challenges to digital security and authenticity verification. This work investigates the use of prototype-based neural networks for detecting and classifying audio deepfakes by tracing them back to their generative source. Using time-frequency representations of speech (spectrograms, mel-spectrograms, MFCC), we evaluated model performance across multilingual and monolingual setups. Our results demonstrate that PIPNet reliably distinguishes between real and synthetic speech and effectively identifies the source TTS generator, making it a strong candidate for source-level attribution in audio deepfake detection.
Tight Bounds for Jensen's Gap with Applications to Variational Inference
Authors: Marcin Mazur, Tadeusz Dziarmaga, Piotr Koscielniak, Lukasz Struski
Venue: CIKM
Year: 2025

Since its original formulation, Jensen's inequality has played a fundamental role across mathematics, statistics, and machine learning, with its probabilistic version highlighting the nonnegativity of the so-called Jensen's gap, i.e., the difference between the expectation of a convex function and the function at the expectation. Of particular importance is the case when the function is logarithmic, as this setting underpins many applications in variational inference, where the term variational gap is often used interchangeably. Recent research has focused on estimating the size of Jensen's gap and establishing tight lower and upper bounds under various assumptions on the underlying function and distribution, driven by practical challenges such as the intractability of log-likelihood in graphical models like variational autoencoders (VAEs). In this paper, we propose new, general bounds for Jensen's gap that accommodate a broad range of assumptions on both the function and the random variable, with special attention to exponential and logarithmic cases. We provide both analytical and empirical evidence for the performance of our method. Furthermore, we relate our bounds to the PAC-Bayes framework, providing new insights into generalization performance in probabilistic models.
Dense Retrieval for Aggregated Search
Authors: Lang Mei, Sijie Liu, Ziyuan Zhao, Rolan Yan, Jiaxin Mao, Jirong Wen
Venue: CIKM
Year: 2025

To satisfy users' diverse information needs, the aggregated search systems need to integrate heterogeneous results, with rich but different structural information, from a variety of verticals, such as news search, video search, and product search. A key challenge in aggregated search is to effectively and efficiently retrieve the most relevant results among a large number of heterogeneous information from different verticals. With the development of deep learning and pre-trained language models (PLMs), many researchers resort to Dense Retrieval (DR) models for a unified, efficient embedding-based retrieval and a better retrieval performance. However, existing dense retrieval models have limitations in: 1) capturing the structural information of search results ; and 2) generalizing across different vertical domains where the search results have different or even unseen structures. In this paper, we aim to tackle these limitations, and propose an effective and efficient dense retrieval model for aggregated search. Specifically, we utilize a deep prompt-tuning technique to make the pre-training model easily applied to downstream vertical search tasks. To capture the structural knowledge, we design a Graph Neural Network (GNN)-based structure prompt, to prompt how text segments are organized in the vanilla semi-structured data. We further incorporate a distributional prompt to model the theme of each domain, and enhance cross-domain generalization. Extensive experiments on the real-world data collected from the WeChat Search demonstrate that for aggregated search tasks, our models can achieve better performance over existing retrieval models, and have the superior ability to generalize to the various or even unseen vertical search tasks.
FinD3: A Dual 3D State Space Model with Dynamic Hypergraph for Financial Stock Prediction
Authors: Jieyuan Mei, Jindong Tian, Ronghui Xu, Hanyue Wei, Chenjuan Guo, Bin Yang
Venue: CIKM
Year: 2025

The financial market plays a crucial role in the modern economy by influencing capital allocation, corporate valuation, and investor behavior. However, its complex dependencies and non-stationary dynamics present significant challenges for financial stock prediction. Previous predictive approaches are typically categorized into Univariate Time Series (UTS) and Multivariate Time Series (MTS) paradigms. UTS methods overlook both cross-feature and cross-stock influences, while MTS methods can only capture one of these simultaneously. Although some recent approaches claim to model 3D Multivariate Time Series (3D-MTS) dependencies, they often discard substantial information and fail to capture the dynamics of the stock market. To address these limitations, we propose FinD3, a Financial 3D model using Dual cubic state spaces and Dynamic hypergraphs. To extract the inherent complex relationships in 3D-MTS, we propose a novel Dual Cubic State Space Model (DCSSM) to capture both cross-feature and cross-stock patterns. Furthermore, to more accurately reflect the dynamics of the stock market, we present an Evolving Hypergraph Attention (EHA) module, which captures dynamic changes in financial markets and updates the hypergraph based on a priori hypergraph. Experimental results demonstrate that FinD3 achieves state-of-the-art performance in quantitative trading performance on two real-world stock market datasets, offering a promising solution to practical quantitative trading challenges. The code is available at: https://github.com/decisionintelligence/FinD3.
Usefulness and Diminishing Returns: Evaluating Social Information in Recommender Systems
Authors: Qing Meng, Huiyu Min, Ming Shan Hee, Roy Ka-Wei Lee, Bing Tian Dai, Shuai Xu
Venue: CIKM
Year: 2025

Social recommendation, which leverages users' social information to predict users' preferences, is a popular branch of recommender systems. Many existing studies have attempted to advance the performance of collaborative filtering methods by leveraging the user-user matrix to enhance user embedding learning with user's social connections. While the existing social recommender systems have demonstrated good performance in various recommendation tasks, the extent of social information usefulness in recommender systems remains unclear. This paper addresses the research gap by designing experiments to answer three research questions: (i) How useful is social information in varying user-item data sparsity? (ii) How much social information do the existing social recommendation models use? (iii) How valuable is social information for cold-start situations? Working towards answering the research questions, we introduce evaluation metrics to estimate the utilization of social information in the existing social recommendation models. We conducted experiments on three publicly available social recommendation datasets, and our results showed that there are diminishing returns when applying social information in recommender systems.
Semantic Filter Recommendation for eCommerce Search
Authors: Kilian Merkelbach, Antonino Freno
Venue: CIKM
Year: 2025

We present an application of encoder-only Transformers to the task of recommending filters to eCommerce search queries. In particular, we operate in a dynamic setup where new recommendations are computed online whenever the user selects one or more filters, conditioned on the search query and the filters selected so far. Our method leverages the world knowledge imparted into a pretrained model, setting it apart from purely memory-based or statistical models, which we use as baselines for evaluation. We review experimental results on offline benchmarks using data generated from eBay search logs, comparing the performance of the proposed model to the baselines. The results show a significant increase in filter recommendation accuracy, as measured by NDCG.