OpenRaiser/Intern-Atlas huggingface dataset:
CoinCLIP: A Multimodal Framework for Assessing Viability in Web3 Memecoins
Authors: Hou-Wan Long, Hongyang Li, Wei Cai
Venue: CIKM
Year: 2025

The rapid growth of memecoins within the Web3 ecosystem, driven by platforms like Pump.fun, has made it easier for anyone to create tokens. However, this democratization has also led to an explosion of low-quality or bot-generated projects, often motivated by short-term financial gain. This overwhelming influx of speculative tokens creates a challenge in distinguishing viable memecoins from those that are unlikely to succeed. To address this issue, we introduce CoinVibe, a comprehensive multimodal dataset designed to evaluate the viability of memecoins. CoinVibe integrates textual descriptions, visual content (logos), and community data (user comments, timestamps, and number of likes) to provide a holistic view of a memecoin's potential. In addition, we present CoinCLIP, a novel framework that leverages the Contrastive Language-Image Pre-Training (CLIP) model, augmented with lightweight modules and community data integration, to improve classification accuracy. By combining visual and textual representations with community insights, CoinCLIP provides a robust, data-driven approach to filter out low-quality or bot-driven projects. This research aims to help creators and investors identify high-potential memecoins, while also offering valuable insights into the factors that contribute to their long-term success.
Harnessing Large Language Models for Group POI Recommendations
Authors: Jing Long, Liang Qu, Junliang Yu, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin
Venue: CIKM
Year: 2025

The rapid proliferation of Location-Based Social Networks (LBSNs) has underscored the importance of Point-of-Interest (POI) recommendation systems in enhancing user experiences. While individual POI recommendation methods leverage users' check-in histories to provide personalized suggestions, they struggle to address scenarios requiring group decision-making. Group POI recommendation systems aim to satisfy the collective preferences of multiple users, but existing approaches face two major challenges: diverse group preferences and extreme data sparsity in group check-in data. To overcome these challenges, we propose LLMGPR, a novel framework that leverages large language models (LLMs) for group POI recommendations. LLMGPR introduces semantic-enhanced POI tokens and incorporates rich contextual information to model the diverse and complex dynamics of group decision-making. To further enhance its capabilities, we developed a sequencing adapter using Quantized Low-Rank Adaptation (QLoRA), which aligns LLMs with group POI recommendation tasks. To address the issue of sparse group check-in data, LLMGPR employs an aggregation adapter that integrates individual representations into meaningful group representations. Additionally, a self-supervised learning (SSL) task is designed to predict the purposes of check-in sequences (e.g., business trips and family vacations), thereby enriching group representations with deeper semantic insights. Extensive experiments demonstrate the effectiveness of LLMGPR, showcasing its ability to significantly enhance the accuracy and robustness of group POI recommendations.
KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits
Authors: Hou-Wan Long, Yicheng Song, Zidong Wang, Tianshu Sun
Venue: CIKM
Year: 2025

Sponsored search advertising (SSA) requires advertisers to constantly adjust keyword strategies. While bid adjustment and keyword generation are well-studied, keyword pruning-refining keyword sets to enhance campaign performance-remains underexplored. This paper addresses critical inefficiencies in current practices as evidenced by a dataset containing 0.5 million SSA records from a pharmaceutical advertiser on search engine Meituan, China's largest delivery platform. We propose KP-Agent, an LLM agentic system with domain tool set and a memory module. By modeling keyword pruning within a contextual bandit framework, KP-Agent generates code snippets to refine keyword sets through reinforcement learning. Experiments show KP-Agent improves cumulative profit by up to 49.28% over baselines.
ParaStyleTTS: Toward Efficient and Robust Paralinguistic Style Control for Expressive Text-to-Speech Generation
Authors: Haowei Lou, Hye-young Paik, Wen Hu, Lina Yao
Venue: CIKM
Year: 2025

Controlling speaking style in text-to-speech (TTS) systems has become a growing focus in both academia and industry. While many existing approaches rely on reference audio to guide style generation, such methods are often impractical due to privacy concerns and limited accessibility. More recently, large language models (LLMs) have been used to control speaking style through natural language prompts; however, their high computational cost, lack of interpretability, and sensitivity to prompt phrasing limit their applicability in real-time and resource-constrained environments. In this work, we propose ParaStyleTTS, a lightweight and interpretable TTS framework that enables expressive style control from text prompts alone. ParaStyleTTS features a novel two-level style adaptation architecture that separates prosodic and paralinguistic speech style modeling. It allows fine-grained and robust control over factors such as emotion, gender, and age. Unlike LLM-based methods, ParaStyleTTS maintains consistent style realization across varied prompt formulations and is well-suited for real-world applications, including on-device and low-resource deployment. Experimental results show that ParaStyleTTS generates high-quality speech with performance comparable to state-of-the-art LLM-based systems while being 30x faster, using 8x fewer parameters, and requiring 2.5x less CUDA memory. Moreover, ParaStyleTTS exhibits superior robustness and controllability over paralinguistic speaking styles, providing a practical and efficient solution for style-controllable text-to-speech generation. Demo can be found at https://parastyletts.github.io/ParaStyleTTS_Demo/. Code can be found at https://github.com/haoweilou/ParaStyleTTS.
Achoio: A Skill-Aware Evaluation Management System for Text-To-Speech Research
Authors: Haowei Lou, Hye-Young Paik, Basem Suleiman, Wen Hu, Lina Yao
Venue: CIKM
Year: 2025

Human subjective evaluation plays a crucial role in evaluating speech-related generative tasks such as text-to-speech (TTS) generation. However, current practices are often constrained by limited scalability, fragmented workflows, and inconsistent rating reliability. Researchers frequently rely on manual methods or general-purpose crowdsourcing systems, where recruiting appropriately skilled listeners is challenging, and result analysis is labor-intensive. In this work, we introduce Achoio, a dedicated end-to-end online system designed to streamline and scale human evaluation for the TTS research community. Achoio allows researchers to create and manage evaluation projects, upload synthesized speech samples, and automatically match them with qualified listeners based on linguistic proficiency and domain knowledge. The system provides built-in tools for project status tracking, result aggregation and visualization. In this demonstration, we will walk through the core features of Achoio, including intuitive project setup, skill-based listener matching algorithm, and automated analytics. By addressing the limitations of existing workflows, Achoio offers a scalable, domain-aware, and analysis-ready solution for conducting high-quality subjective TTS evaluations. Our system is live and can be found at https://www.achoio.com. Demo is available on YouTube at https://youtu.be/Ugjj3_YooSM.
Dual Denoising Diffusion Model for Session-based Social Recommendation
Authors: Mengying Lu, Hai-Tao Zheng, Lan Zhou, Qi Li, Jinxiao Shan, Zhixing Li, Hong-Gee Kim
Venue: CIKM
Year: 2025

Session-based Social Recommendation (SSR) enhances item recommendations by incorporating both session interactions and social network data. Despite recent progress, existing SSR methods-primarily based on Graph Neural Networks-are highly susceptible to session noise (irrelevant or unintentional interactions) and social noise (misleading signals from connected users). Prior denoising strategies often rely on heuristic resampling or reweighting techniques, which lack generalizability and robustness across diverse datasets. In this work, we explore a novel direction by introducing diffusion models for denoising in SSR. However, applying diffusion to SSR presents unique challenges due to heterogeneous data modalities, incompatible noise patterns, and the absence of semantic guidance during the reverse process. To overcome these challenges, we propose D3MRec, a Dual Denoising Diffusion Model specifically designed for SSR. D3MRec employs a dual-branch architecture that independently models session sequences and social graphs, applying denoising diffusion in their respective hidden representation spaces. This decoupled design preserves the structural integrity of each modality while enabling modality-specific denoising. Moreover, we introduce cross-modal guidance by leveraging collaborative signals from the other branch during the reverse diffusion process, enhancing alignment between session intents and social preferences. The dual denoising processes not only mitigate noise within each modality but also serve as mutual priors, facilitating robust and consistent representation learning across modalities. Extensive experiments on multiple benchmarks show that D3MRec significantly outperforms state-of-the-art models, particularly under noisy conditions, demonstrating its effectiveness and robustness.
Prompt Tuning as User Inherent Profile Inference Machine
Authors: Yusheng Lu, Zhaocheng Du, Xiangyang Li, Pengyue Jia, Yejing Wang, Weiwen Liu, Yichao Wang, Huifeng Guo, Ruiming Tang, Zhenhua Dong, Yongrui Duan, Xiangyu Zhao
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capabilities. However, LLMs face challenges like unstable instruction compliance, modality gaps, and high inference latency, leading to textual noise and limiting their effectiveness in recommender systems. To address these challenges, we propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. It integrates the causal relationship between user profiles and behavior sequences into LLMs' prompts. It employs Expectation Maximization (EM) to infer the embedded latent profile, minimizing textual noise by fixing the prompt template. Furthermore, a profile quantization codebook bridges the modality gap by categorizing profile embeddings into collaborative IDs pre-stored for online deployment. This improves time efficiency and reduces memory usage. Experiments show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms. An industry application confirms its effectiveness, robustness, and transferability. The presented solution has been deployed in Huawei AppGallery's Explore page since May 2025, serving 2 million daily active users, delivering significant improvements in real-world recommendation scenarios. The code is publicly available for replication at https://github.com/Applied-Machine-Learning-Lab/UserIP-Tuning.
Anchor-based Pairwise Comparison via Large Language Model for Recommendation Reranking
Authors: Qin Luo, Erjia Chen, Zhao Shi, Bang Wang
Venue: CIKM
Year: 2025

In recommender systems, reranking is an important post-processing technique to reorder the items in a recommendation list. Recently, some LLM-based reranking approaches have been proposed to enjoy the semantic reasoning capability of a large language model. However, they are sensitive to the order of the input list and often incur large computational overheads. To address their limitations, we propose the APCR, an Anchor-based Pairwise Comparison for recommendation Reranking in this paper. It first leverages an LLM to conduct pairwise comparisons between those recommended items and an anchor and computes a kind of preference scores for producing a new LLM suggested list. We next propose a position-aware list reranking technique to reorder the items in the recommendation list by considering their positions in the LLM suggested list to output the final list. Experiments on real-world datasets show that our APCR outperforms the state-of-the-art LLM-based reranking techniques in terms of better list ranking performance.
Using Large Language Models to Improve Product Information in E-commerce Catalogs
Authors: Gang Luo, Julien Han, Hayreddin Çeker, Karim Bouyarmane
Venue: CIKM
Year: 2025

To give customers good experience, an e-commerce retailer needs high-quality product information in its catalog. Yet, the raw product information often lacks sufficient quality. For a large catalog that can contain billions of products, manually fixing this information is highly labor-intensive. To address this issue, we propose using the tool use functionality of large language models to automatically improve product information. In this talk, we show why existing data cleaning methods are not well suited for this task and how we designed our automated system to improve product information. When evaluated on a random sample of products from an e-commerce catalog, our system improved product information completeness by 78% with no major drop in information accuracy.
ROI Scan: LLM-powered Object-level Similarity Search for Google Ads Content Moderation
Authors: Enming Luo, Yintao Liu, Dongjin Kwon, Rich Munoz, Wei Qiao, Nic Trieu, Eric Xiao, Jimin Li, Laurel Graham, Ariel Fuxman
Venue: CIKM
Year: 2025

Google's ad platform reviews a massive volume of ads daily, aiming to maintain user trust and platform integrity. However, malicious advertisers often bypass the existing detection by subtly manipulating suspended ads, typically by placing a policy-violating region of interest (ROI) into diverse images with varied backgrounds and sizes. To counter this, we introduce ROI Scan, a novel two-stage object-level similarity search approach. Stage 1 precisely identifies and extracts the problematic ROIs from escalated ad images using LLM-powered suggestions, refined by human expert review. Stage 2 then matches these ROIs against a massive database of billions of objects extracted from production images. Our experiments on the Online Gambling policy demonstrate ROI Scan's effectiveness, achieving 89.1% relative recall and 83.8% incremental coverage over the full-image search baseline, with nearly 100% precision. In production, ROI Scan prevents hundreds of millions of policy violation impressions and blocks hundreds of thousands of bad ads weekly.
Bridging the Gap between Knowledge Graphs and LLMs for Multi-hop Question Answering
Authors: Shijie Luo, Xinyuan Lu, Qinpei Zhao, Weixiong Rao
Venue: CIKM
Year: 2025

To achieve multi-hop question answering over knowledge graphs (KGQA), many studies have explored converting retrieved subgraphs into textual form and feeding them into large language models (LLMs) to leverage their reasoning capabilities. However, due to the linear and discrete nature of text sequences, model performance may degrade when handling complex questions. To this end, we propose a novel structure-text knowledge synergistic method, BrikQA, which bridges the knowledge gap between knowledge graphs (KGs) and LLMs for multi-hop KGQA. LLMs and KGs complement each other by leveraging explicit topological patterns and implicit knowledge mining to enhance knowledge understanding and address sparsity issues. Experimental results on various datasets demonstrate that BrikQA outperforms state-of-the-art baselines. Our source code is available at https://github.com/shijielaw/BrikQA.
Collaborative Interest Mining Network for Knowledge Graph-based Recommendation
Authors: Jie Luo, Ying Pan, Guoliang Huang
Venue: CIKM
Year: 2025

Knowledge graphs contain rich semantic information and have been widely applied in recommender systems. However, most existing knowledge graph-based recommendation methods primarily focus on modeling item-side semantics and overlook the critical role of knowledge graphs in enhancing user representations. In this work, we propose a novel recommendation model called Collaborative Interest Mining Network for Knowledge Graph-based Recommendation (CIMNK), which leverages the knowledge graph to mine collaborative interest similarity (i.e., the similarity between users who share the same interests), thereby enhancing the quality of user embeddings. Specifically, CIMNK first constructs a user-interest graph by performing fine-grained filtering over entities and distinguishing between different relation types, to explicitly represent direct associations between users and interest entities. Subsequently, CIMNK introduces the Relation-aware Collaborative Interest Mining Module (RCIM), which conducts graph representation learning on the user-interest graph to mine and integrate collaborative interest information across different relation types. Finally, we design an interest-aware loss function to supervise the learning of collaborative interest similarity. Extensive experiments on three public benchmark datasets demonstrate that CIMNK outperforms state-of-the-art methods. The implementations are available at: https://github.com/JieLuoRoger/CIMNK-Pytorch.
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
Authors: Weiqing Luo, Chonggang Song, Lingling Yi, Gong Cheng
Venue: CIKM
Year: 2025

Combining semantic information with behavioral data is a crucial research area in recommender systems. A promising approach involves leveraging external knowledge to enrich behavioral-based recommender systems with abundant semantic information. However, this approach faces two primary challenges: (1) denoising raw external knowledge and (2) adapting semantic representations. To address these challenges, we propose exTernal knowledge-enhanced RecommendAtion With LLM assistance (TRAWL). This method utilizes large language models to extract relevant recommendation knowledge from raw external data and employs a contrastive learning strategy for adapter training. Experiments on public datasets and real-world online recommender systems validate the effectiveness of our approach.
ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling
Authors: Huishi Luo, Fuzhen Zhuang, Yongchun Zhu, Yiqing Wu, Bo Kang, Ruobing Xie, Feng Xia, Deqing Wang, Jin Dong
Venue: CIKM
Year: 2025

Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task learning is widely adopted to jointly optimize DT and CTR, we observe that multi-task models systematically collapse their DT predictions to the shortest and longest bins, under-predicting the moderate durations. We attribute this moderate-duration bin under-representation to over-reliance on the CTR-DT spurious correlation, and propose ORCA to address it with causal-decoupling. Specifically, ORCA explicitly models and subtracts CTR's negative transfer while preserving its positive transfer. We further introduce (i) feature-level counterfactual intervention, and (ii) a task-interaction module with instance inverse-weighting, weakening CTR-mediated effect and restoring direct DT semantics. ORCA is model-agnostic and easy to deploy. Experiments show an average 10.6% lift in DT metrics without harming CTR. Code is available at https://github.com/Chrissie-Law/ORCA-Mitigating-Over-Reliance-for-Multi-Task-Dwell-Time-Prediction-with-Causal-Decoupling.
MARM: Unlocking the Recommendation Cache Scaling-Law through Memory Augmentation and Scalable Complexity
Authors: Xiao Lv, Jiangxia Cao, Shijie Guan, Xiaoyou Zhou, Zhiguang Qi, Yaqiang Zang, Ben Wang, Guorui Zhou
Venue: CIKM
Year: 2025

Scaling-law has guided the language model design for past years, e.g., GPTs, enabling the estimation of expected model performance with respect to the size of learnable parameters and the scale of training samples. It is worth noting that the scaling laws of NLP cannot be directly applied to recommendation systems due to the following reasons: (1) The amount of training samples and model parameters is typically not the bottleneck for the model. Our recommendation system can generate over 50 billion user samples daily, and such a massive amount of training data can easily allow our model parameters to exceed 200 billion, surpassing many LLMs (about 100B). (2) It is essential to control FLOPs carefully in recommendation system. In training, we need to process a vast number of recommendation samples every day. During online inference, we must respond within milliseconds (LLMs usually take a few seconds). Considering the above differences with LLM, we can conclude that: for a RecSys model, compared to model parameters, the FLOPs is a more expensive factor that requires careful control. In this paper, we propose our milestone work, MARM (Memory Augmented Recommendation Model), which explores a new cache scaling-law successfully. By caching part of complex module calculation results, our MARM extends the single-layer attention-based sequences interests modeling module to a multi-layer setting with minor inference FLOPs cost (i.e, module time complexity O(n2*d) -> O(n*d)). Equipped with the cache idea, our MARM solution significantly overcomes computational bottlenecks and can seamlessly empower all interest extraction modules for user sequences, and even other models. To support our MARM, we construct a 60TB cache storage center for offline training and online serving. Comprehensive experiment results show that our MARM brings offline 0.43% GAUC improvements and online 2.079% play-time per user gains. Our MARM has been deployed on a real-world short-video platform, serving tens of millions of users daily.
MetaCAN: Improving Generalizability of Few-shot Anomaly Detection with Meta-learning
Authors: Zhisheng Lv, Jianfeng Zhang, Songlei Jian, Chenlin Huang, Hongguang Zhang, Guansong Pang, Zhong Liu
Venue: CIKM
Year: 2025

Few-shot Anomaly Detection (AD) for images aims to detect anomalies with few-shot normal samples from the target dataset. It is a crucial task when only few samples can be obtained, and it is challenging since it needs to be generalized to different domains. Existing methods try to enhance the generalizability of AD by incorporating large vision-language models (LVLMs).However, how to transform category semantic information in LVLMs into anomaly information to improve the generalizability of AD remains a challenge facing existing methods.To address the challenge, we propose a few-shot AD method called MetaCAN, a novel category-to-anomaly network trained with AD meta-learning scheme based on an LVLM. Specifically, MetaCAN constructs the auxiliary training data and multiple tasks based on different categories to perform AD meta-learning, which ensures that the optimization toward the achievement of optimal anomaly detection across all categories. Moreover, MetaCAN introduces an image-image anomaly discriminator and an image-text anomaly detector to fully exploit the powerful multimodal semantic representations during auxiliary training. Once trained on auxiliary datasets, MetaCAN can be applied directly to other target datasets without retraining. Extensive experiments on six real-world datasets demonstrate that MetaCAN achieves state-of-the-art performance on cross-domain and cross-category anomaly detection tasks compared with existing methods.