OpenRaiser/Intern-Atlas huggingface dataset:
Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs
Authors: Saiping Guan, Jiyao Wei, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
Venue: CIKM
Year: 2024

Sparse Knowledge Graphs (KGs), frequently encountered in real-world applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated KGs. The sparse KG completion task, which reasons answers for given queries in the form of (head entity, relation, ?) for sparse KGs, is particularly challenging due to the necessity of reasoning missing facts based on limited facts. Path-based models, known for excellent explainability, are often employed for this task. However, existing path-based models typically rely on external models to fill in missing facts and subsequently perform path reasoning. This approach introduces unexplainable factors or necessitates meticulous rule design. In light of this, this paper proposes an alternative approach by looking inward instead of seeking external assistance. We introduce a two-stage path reasoning model called LoGRe (Look Globally and Reason) over sparse KGs. LoGRe constructs a relation-path reasoning schema by globally analyzing the training data to alleviate the sparseness problem. Based on this schema, LoGRe then aggregates paths to reason out answers. Experimental results on five benchmark sparse KG datasets demonstrate the effectiveness of the proposed LoGRe model.
CancerKG.ORG - A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care
Authors: Michael N. Gubanov, Anna Pyayt, Aleksandra Karolak
Venue: CIKM
Year: 2024

Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center and Research Institute, which is one of the top Cancer centers in the U.S. and in the world. Our hybrid is remarkable as it serves the user needs better than just an LLM, KG or a search-engine in isolation. LLMs as is are known to exhibit hallucinations and catastrophic forgetting as well as are trained on outdated corpora. The state of the art KGs, such as PrimeKG, cBioPortal, ChEMBL, NCBI, and other require manual curation, hence are quickly getting stale. CancerKG is unsupervised and is capable of automatically ingesting and organizing the latest medical findings. To alleviate the LLMs shortcomings, the verified KG serves as a Retrieval Augmented Generation (RAG) guardrail. CancerKG exhibits 5 different advanced user interfaces, each tailored to serve different data modalities better and more convenient for the user. We evaluated CancerKG on real user queries and report a high NDCG score on a large-scale corpora of approximately 44K publications.
Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware Approach
Authors: Yuxiang Guo, Shuanghong Shen, Qi Liu, Zhenya Huang, Linbo Zhu, Yu Su, Enhong Chen
Venue: CIKM
Year: 2024

Knowledge Tracing (KT) is a crucial research task for dynamically monitoring students' knowledge states, particularly in online education systems. Recently, knowledge tracing has gained significant attention and in-depth research. Most existing methods rely on students' response data for question understanding and modeling, which helps better updating students' knowledge states. Meanwhile, question ID is utilized to indicate and represent questions. However, this presents a challenge when transitioning to new, cold-start questions that few students has answered before. Also, prior work has overlooked the semantic modeling of questions, which could better assist in modeling the transfer of students' knowledge states. In this paper, we explore leveraging the power of Large Language Models (LLMs) to help understand questions for knowledge tracing, which benefits mitigating cold-start and sparse problems and modeling the transfer of students' knowledge states in a sophisticated manner. Specifically, we first design an attribute estimation module to estimate the attribute of the questions (e.g., difficulty, ability requirements, expected response time) by prompting Large Language Models. Subsequently, we have developed a question embedding module that incorporates graph attention network to effectively utilizing these attributes. Extensive experiments on various datasets demonstrate that our model outperforms existing state-of-the-art models and effectively addresses the problems of cold-start and sparsity. In addition, due to the estimation of multiple attributes of the questions, our model exhibits superior interpretability.
A Bayesian Multi-Armed Bandit Algorithm for Bid Shading in Online Display Advertising
Authors: Mengzhuo Guo, Wuqi Zhang, Congde Yuan, Binfeng Jia, Guoqing Song, Hua Hua, Shuangyang Wang, Qingpeng Zhang
Venue: CIKM
Year: 2024

In real-time bidding systems, ad exchanges and supply-side platforms (SSP) are switching from the second-price auction (SPA) to the first-price auction (FPA), where the advertisers should pay what they bid if they win the auction. To avoid overpaying, advertisers are motivated to conceal their truthful evaluations of impression opportunities through bid shading methods. However, advertisers are consistently facing a trade-off between the probability and cost-saving of winning, due to the information asymmetry, where advertisers lack knowledge about their competitors' bids in the market. To address this challenge, we propose a Bayes ian Multi-Armed Bandit (BayesMAB) algorithm for bid shading when the winning price is unknown to advertisers who lose the impression opportunity. BayesMAB incorporates the mechanism of FPA to infer each price interval's winning rate by progressively updating the market price hidden by SSP. In this way, BayesMAB better approximates the winning rates of price intervals and thus is able to derive the optimal shaded bid that balances the trade-off between the probability and cost-saving of winning the impression opportunity. We conducted large-scale A/B tests on Tencent's online display advertising platform. The cost-per-mile (CPM) and cost-per-action (CPA) decreased by 13.06% and 11.90%, respectively, whereas the return on investment (ROI) increased by 12.31% with only 2.7% sacrifice of the winning rate. We also validated BayesMAB's superior performance in an offline semi-simulated experiment with SPA data sets. BayesMAB has been deployed online and is impacting billions of traffic every day. Codes are available at https://github.com/BayesMAB/BayesMAB.
Graph Local Homophily Network for Anomaly Detection
Authors: Ronghui Guo, Minghui Zou, Sai Zhang, Xiaowang Zhang, Zhizhi Yu, Zhiyong Feng
Venue: CIKM
Year: 2024

In graph anomaly detection (GAD), the fact that anomalous nodes usually exhibit high heterophily, while most Graph Neural Networks (GNNs) have homophily assumptions, leads to poor performance. Many studies have attempted to solve this problem by employing a set of graph filters covering various frequencies. Their ultimate goal is to design the most appropriate spectral filter to capture the complex signals generated by normals and anomalies. The critical aspect lies in the fusion of information from filters with different frequency response functions. However, existing methods lack a clear indicator to guide the fusion of information at different frequencies. In this paper, we find that local homophily is a valuable metric for assessing the weights of high- and low-frequency information at the node level, and explicitly point out that the accuracy of local homophily is positively correlated with the accuracy of anomaly detection. Moreover, we unveil the phenomenon of camouflage in anomalous, wherein these nodes disguise themselves by making their features resemble those of surrounding normals. Based on this investigation, we propose the Graph Local Homophily Network for Anomaly Detection (GLHAD). Specifically, we first identify the local homophily of the nodes in the graph under the supervision of the labeled nodes, where two contrasting paradigms are employed to resist the camouflage of anomalies. Then, the local homophily-based combination module combines low- and high-frequency signals based on the predicted local homophily. Eventually, the node representations of different layers are aggregated to make finally predictions.Comprehensive experiments on four anomaly detection datasets show that GLHAD outperforms other state-of-the-art baselines.
Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment Classification
Authors: Shashank Gupta, Mohamed Reda Bouadjenek, Antonio Robles-Kelly, Tsz-Kwan Lee, Thanh Thi Nguyen, Asef Nazari, Dhananjay R. Thiruvady
Venue: CIKM
Year: 2024

For the sentence-level sentiment classification task, learning Contrastive Discourse Relations (CDRs) like a-but-b is difficult for Deep Neural Networks (DNNs) via purely data-driven training. Several methods exist in the literature for dissemination of CDR information with DNNs, but there is no dedicated dataset available to effectively test their dissemination performance. In this paper, we propose a new large-scale dataset for this purpose called Covid19-twitter, which contains around 100k tweets symmetrically divided into various categories. Instead of manual annotation, we used a combination of an Emoji analysis and a lexicon-based tool called Valence Aware Dictionary and sEntiment Reasoner (VADER) to perform automatic labelling of the tweets, while also ensuring high accuracy of the annotation process through some quality checks. We also provide benchmark performances of several baselines on our dataset for both the sentiment classification and CDR dissemination tasks. We believe that this dataset will be valuable for discourse analysis research in sentiment classification.
GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
Authors: Vipul Gupta, Xin Chen, Ruoyun Huang, Fanlong Meng, Jianjun Chen, Yujun Yan
Venue: CIKM
Year: 2024

Graph Neural Networks (GNNs) have emerged as powerful tools for supervised machine learning over graph-structured data, while sampling-based node representation learning is widely utilized in unsupervised learning. However, scalability remains a major challenge in both supervised and unsupervised learning for large graphs (e.g., those with over 1 billion nodes). The scalability bottleneck largely stems from the mini-batch sampling phase in GNNs and the random walk sampling phase in unsupervised methods. These processes often require storing features or embeddings in memory. In the context of distributed training, they require frequent, inefficient random access to data stored across different workers. Such repeated inter-worker communication for each mini-batch leads to high communication overhead and computational inefficiency. We propose GraphScale, a unified framework for both supervised and unsupervised learning to store and process large graph data distributedly. The key insight in our design is the separation of workers who store data and those who perform the training. This separation allows us to decouple computing and storage in graph training, thus effectively building a pipeline where data fetching and data computation can overlap asynchronously. Our experiments show that GraphScale outperforms state-of-the-art methods for distributed training of both GNNs and node embeddings. We evaluate GraphScale both on public and proprietary graph datasets and observe a reduction of at least 40% in end-to-end training times compared to popular distributed frameworks, without any loss in performance. While most existing methods don't support billion-node graphs for training node embeddings, GraphScale is currently deployed in production at TikTok enabling efficient learning over such large graphs.
RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders
Authors: Danil Gusak, Gleb Mezentsev, Ivan V. Oseledets, Evgeny Frolov
Venue: CIKM
Year: 2024

Scalability is a major challenge in modern recommender systems. In sequential recommendations, full Cross-Entropy (CE) loss achieves state-of-the-art recommendation quality but consumes excessive GPU memory with large item catalogs, limiting its practicality. Using a GPU-efficient locality-sensitive hashing-like algorithm for approximating large tensor of logits, this paper introduces a novel RECE (REduced Cross-Entropy) loss. RECE significantly reduces memory consumption while allowing one to enjoy the state-of-the-art performance of full CE loss. Experimental results on various datasets show that RECE cuts training peak memory usage by up to 12 times compared to existing methods while retaining or exceeding performance metrics of CE loss. The approach also opens up new possibilities for large-scale applications in other domains.
Planes, Trains and Automobiles: Leverage Multimodal In-Mission Signals for Shopping Journeys
Authors: Viet Ha-Thuc, Shasha Li, Arnau Ramisa, Xinliang Zhu
Venue: CIKM
Year: 2024

Modern search systems offer multiple ways for expressing information needs, including image, voice, and text. Consequently, an increasing number of users seamlessly transition between these modalities to convey their intents. This emerging trend presents new opportunities for utilizing queries in different modalities to help users complete their search journeys efficiently. In this proposal, we introduce an approach to segmenting a multimodal query stream into missions, demonstrate how these in-mission queries can enhance search ranking, and outline key areas for future research.
Quality Prediction in Arc Welding: Leveraging Transformer Models and Discrete Representations from Vector Quantised-VAE
Authors: Yannik Hahn, Robert F. Maack, Hasan Tercan, Tobias Meisen, Marion Purrio, Guido Buchholz, Matthias Angerhausen
Venue: CIKM
Year: 2024

Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW), which efficiently converts electrical energy into thermal energy to join metals. Despite decades of research and extensive application in the automotive and aerospace sectors, weld quality assessment in the GMAW process remains a major challenge. This paper presents a novel learning-based approach relying on a vector quantised variational autoencoder (VQ-VAE) for data representation. In addition, we are the first to provide a time series dataset to the research community that combines labeled and unlabeled time series data from the GMAW domain, thereby enabling further research. The core idea of our approach consists of two stages: In the first stage, we use a learned automatic extraction of local features of the input signal using a VQ-VAE architecture. Based on this, in the second stage, we use a transformer model that processes the discretized features and performs weld quality prediction and classification. Our approach addresses real-world scenarios and improves the prediction of quality and fill existing data gaps by providing a reliable approach for quality assessment during manufacturing based on sensor data.
Fragment Allocations for Partially Replicated Databases Considering Data Modifications and Changing Workloads
Authors: Stefan Halfpap, Rainer Schlosser
Venue: CIKM
Year: 2024

Columnar database systems can process complex mixed workloads on a single node. In case of increasing and peak analytical processing demand, we can offload read-only queries to replicas. Partial replication, i.e., duplicating only data subsets to additional nodes, is more cost-efficient than full replication for two primary reasons: (i) Partial replicas require less storage and can be set up faster. (ii) Partial replicas must synchronize only stored data subsets, allowing better scalability. However, determining which queries to offload is challenging for larger workloads because queries access overlapping data subsets and cause synchronization costs. This paper shows how to calculate optimized replica configurations that consider reallocation and data modification costs using integer linear programming (ILP) techniques. While ILP is effective for solving assignment problems, it does not scale well. For larger problems, users often fall back to simple heuristics, which can lose optimization potential. This paper demonstrates that scalable heuristics can be built on ILP, preserving its strengths. The three proposed approaches for reducing the calculation time allow trading solution quality flexibly. Our evaluations using TPC-H, TPC-DS, and a large real-world accounting workload show that our approach outperforms state-of-the-art solutions, often reducing reallocated data by more than 80% and halving modification costs. At the same time, the new allocations reduce the storage consumption by over 30%, with solutions computed in just a few seconds.
Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks
Authors: Jinkun Han, Wei Li, Yingshu Li, Zhipeng Cai
Venue: CIKM
Year: 2024

Current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brain-computer interfaces, it is time to explore next-generation recommenders that show users' real-time thoughts without delay. Electroencephalography (EEG) is a promising method of collecting brain signals because of its convenience and mobility. Currently, there is only few research on EEG-based recommendations due to the complexity of learning human brain activity. To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition Theory and Graph Convolutional Networks for accurate item recommendations. Compared with the state-of-the-art recommendation models, the superiority of QUARK is confirmed via extensive experiments.
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR framework
Authors: Ruidong Han, Qianzhong Li, He Jiang, Rui Li, Yurou Zhao, Xiang Li, Wei Lin
Venue: CIKM
Year: 2024

In sequential recommendation, pre-training from user historical behaviors through self-supervised learning can better comprehend user dynamic preferences, presenting the potential for direct integration with Click-Through Rate (CTR) prediction tasks. Previous methods have integrated pre-trained models into downstream tasks with the sole purpose of extracting semantic information or well-represented user features, which are then incorporated as new features. However, these approaches tend to ignore the additional inference costs and do not consider how to transfer the effective information from the pre-trained models for specific estimated items in CTR prediction. In this paper, we propose a Sequential Recommendation Pre-training framework for CTR prediction (SRP4CTR) to tackle the above problems. Initially, we discuss the impact of introducing pre-trained models on inference costs. Subsequently, we introduced a pre-trained method to encode sequence side information concurrently. During the fine-tuning process, we incorporate a cross-attention block to establish a bridge between estimated items and the pre-trained model at a low cost. Moreover, we develop a querying transformer technique to facilitate the knowledge transfer from the pre-trained model. Offline and online experiments show that our method outperforms previous baseline models.
Multi-Modal Sarcasm Detection via Graph Convolutional Network and Dynamic Network
Authors: Jiaqi Hao, Junfeng Zhao, Zhigang Wang
Venue: CIKM
Year: 2024

Sarcasm is a form of language used to convey implicit information contradicting the literal meaning of words, often observed on online social media platforms. Accurately detecting satirical or ironic expressions could significantly enhance sentiment analysis and opinion mining. For multi-modal data, capturing both inter- and intra-modal incongruities is crucial for this task. Recently, graph-based approaches to modeling incongruous features bet-ween image and text have made significant progress in this task. However, these methods rely on static networks to capture incongruous features, which makes them inflexible in adapting to diverse groups of text and image, or neglect important information due to inadequate use of text and image. To address these limitations, we propose a multi-modal sarcasm detection model based on the combination of Graph Convolutional Network and Dynamic Network. The graph convolutional network learns the incongruity of the three modal graphs and makes full use of the object-level information. The dynamic network dynamically captures the incongruity between the global-level image and the text and can flexibly adapt to different image and related text. At the same time, we generate augmented text to better utilize the text information. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches.
A General Strategy Graph Collaborative Filtering for Recommendation Unlearning
Authors: Yongjing Hao, Fuzhen Zhuang, Deqing Wang, Guanfeng Liu, Victor S. Sheng, Pengpeng Zhao
Venue: CIKM
Year: 2024

Recommender systems play a crucial role in delivering personalized services to users, but the increasing volume of user data raises significant concerns about privacy, security, and utility. However, existing machine unlearning methods cannot be directly applied to recommendation systems as they overlook the collaborative information shared across users and items. More recently, a method known as RecEraser was introduced, offering partitioning and aggregation-based approaches. Nevertheless, these approaches have limitations due to their inadequate handling of additional overhead costs. In this paper, we propose A General Strategy Graph Collaborative Filtering for Recommendation Unlearning (GSGCF-RU), which is a novel model-agnostic learnable delete operator that optimizes unlearning edge consistency and feature representation consistency. Specifically, the GSGCF-RU model utilizes unlearning edge consistency to eliminate the influence of deleted elements, followed by feature representation consistency to retain knowledge after deletion. Lastly, experimental results on three real-world public benchmarks demonstrate that GSGCF-RU not only achieves efficient recommendation unlearning but also surpasses state-of-the-art methods in terms of model utility. The source code can be found at https://github.com/YongjingHao/GSGCF-RU.
Frontiers of Large Language Model-Based Agentic Systems - Construction, Efficacy and Safety
Authors: Jia He, Reshmi Ghosh, Kabir Walia, Jieqiu Chen, Tushar Dhadiwal, April Hazel, Chandra Inguva
Venue: CIKM
Year: 2024

"The previous era was about information at your fingertips; I think of the AI era as expertise at your fingertips." - Satya Nadella, CNBC This tutorial explores Large Language Model (LLM)-based autonomous agents, addressing the lack of comprehensive guides on the topic. It systematically examines key components such as profiling, perception, memory, planning, and action, using an established taxonomy. The tutorial also extends the discussion to multi-agent frameworks, offering insights into collaborative intelligence. Additionally, it compares popular open-source frameworks for LLM-based agent development and discusses evaluation methodologies, focusing on efficiency and safety. The tutorial aims to catalyze dialogue and partnership among practitioners, propelling forward the integration of robust and effective LLM agent systems into the production environment.