OpenRaiser/Intern-Atlas huggingface dataset:
Dual-level Intents Modeling for Knowledge-aware Recommendation
Authors: Jin Zeng, Nan Wang, Jinbao Li
Venue: CIKM
Year: 2024

Previous user-item interaction graphs have typically focused on simple interaction between users and items, failing to identify the important effects of user's intents in the interaction. While recent studies have ventured into exploring intent relationships between users and items for modeling, they predominantly emphasize user preferences manifesting in the interaction, overlooking knowledge-driven insight, thereby limiting the interpretability of intent. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel dual-level intents modeling framework called DIM. DIM aims to mine user's true intents, which usually include user popularity preference and personalized preference. Therefore, we extract both the popular and personalized user preferences from attribute tuples within the knowledge graph at the global and local levels, respectively. Experimental results on three datasets demonstrate the superiority of DIM over various state-of-the-art approaches.
Benchmarking Challenges for Temporal Knowledge Graph Alignment
Authors: Weixin Zeng, Jie Zhou, Xiang Zhao
Venue: CIKM
Year: 2024

Temporal knowledge graph alignment (TKGA) discovers the equivalent elements among heterogeneous temporal knowledge graphs (TKGs), and thus can increase the coverage of a given TKG. However, existing TKGA datasets fail to mirror the real-life challenges, and the oversimplified scenarios may even impede the fair comparison and development of the alignment solutions. To address the aforementioned issues, in this work, we propose to benchmark challenges for temporal knowledge graph alignment by establishing a new dataset, i.e., BETA, which features multi-granular temporal information, more realistic quadruple distribution, and new challenging alignment scenarios. Furthermore, we also offer a simple yet effective solution, MGTEA, to address the aforementioned challenges, which effectively models the complex structural and multi-granular temporal features to facilitate the alignment. Extensive experiments reveal that BETA indeed better mirrors the real-life challenges, and there is still room for developing more advanced solutions to address these difficulties, despite of the superior performance achieved by MGTEA.
M2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base
Authors: Zhiwei Zha, Jiaan Wang, Zhixu Li, Xiangru Zhu, Wei Song, Yanghua Xiao
Venue: CIKM
Year: 2024

Multimodal knowledge bases (MMKBs) provide cross-modal aligned knowledge crucial for multimodal tasks. However, the images in existing MMKBs are generally collected for entities in encyclopedia knowledge graphs. Therefore, detailed groundings of visual semantics with linguistic concepts are lacking, which are essential for the visual concept cognition ability of multimodal models. Addressing this gap, we introduce M2 ConceptBase, the first concept-centric MMKB. M2 ConceptBase models concepts as nodes with associated images and detailed textual descriptions. We propose a context-aware multimodal symbol grounding approach to align concept-image and concept-description pairs using context information from image-text datasets. Comprising 951K images and 152K concepts, M2 ConceptBase links each concept to an average of 6.27 images and a single description, ensuring comprehensive visual and textual semantics. Human studies confirm more than 95% alignment accuracy, underscoring its quality. Additionally, our experiments demonstrate that M2 ConceptBase significantly enhances VQA model performance on the OK-VQA task. M2 ConceptBase also substantially improves the fine-grained concept understanding capabilities of multimodal large language models through retrieval augmentation in two concept-related tasks, highlighting its value.
Learn From Mistakes: Guidance on Zero-shot Conversational Text-to-SQL
Authors: Wenshuo Zhai, Xiang Zhao, Jinzhi Liao, Ziyang Chen
Venue: CIKM
Year: 2024

Large language models (LLMs) possess powerful contextual comprehension capabilities and have demonstrated remarkable success in conversational tasks. However, existing works that apply LLMs to conversational text-to-SQL task have the problem of repetitive mistakes, which results in the failure to bring out the performance of LLMs. In this paper, we propose a novel approach that provides guidance through learning from mistakes. Specifically, the guidance offered by our approach includes tailored suggestions, corrective feedback, and personalized strategies aimed at improving learning outcomes. Furthermore, we employ chain-of-thought (CoT) to utilize guidance that is not suitable directly as prompts. Our method rigorously analyzes actual errors and strategizes on how to utilize the derived guidance effectively. Experimental results demonstrate that our approach improves the state-of-the-art (SOTA) performance metrics, increasing QEX performance from 66.3% to 70.9% (an absolute improvement of 4.6%) and IEX performance from 37.4% to 45.1% (an absolute improvement of 7.7%) on the CoSQL dataset.
Meta-Prompt Tuning Vision-Language Model for Multi-Label Few-Shot Image Recognition
Authors: Feng Zhang, Wei Chen, Fei Ding, Tengjiao Wang, Dawei Lu, Jiabin Zheng
Venue: CIKM
Year: 2024

Multi-label few-shot image recognition aims to identify multiple unseen objects using only a handful of examples. Recent methods typically tune pre-trained vision-language models with shared or class-specific prompts. However, they still have drawbacks. Tuning a shared prompt is insufficient for all samples especially when the tasks are complex and tuning specific prompts for each class is inevitable to lose generalization ability, thus failing to capture diverse visual knowledge. To address these issues, we propose to meta-tune a generalized prompt pool, enabling each prompt to act as an expert for multi-label few-shot image recognition. Specifically, we first construct a diverse prompt pool to handle complex samples and tasks effectively. Then, the meta-tuning strategy is designed to learn meta-knowledge and transfer it from source tasks to target tasks, enhancing the generalization of prompts. Extensive experimental results on two widely used multi-label image recognition datasets demonstrate the effectiveness of our method.
Revisit Orthogonality in Graph-Regularized MLPs
Authors: Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu
Venue: CIKM
Year: 2024

This paper introduces OrthoReg, a simple yet effective Graph-regularized MLP model for semi-supervised node representation learning. We first demonstrate, through empirical observations and theoretical analysis, that node embeddings learned from conventional GR-MLPs suffer from the over-correlation issue. This issue arises when a few dominant singular values overwhelm the embedding space, leading to the limited expressive power of the learned node representations. To mitigate this problem, we propose a novel GR-MLP model called OrthoReg. By incorporating a soft regularization loss on the correlation matrix of node embeddings, OrthoReg explicitly encourages orthogonal node representations, effectively avoiding over-correlated representations. Compared to the currently popular GNN models, our OrthoReg possesses two distinct advantages: 1) Much faster inference speed, particularly for large-scale graphs. 2) Significantly superior performance in inductive cold-start settings. Experiments on semi-supervised node classification tasks, together with the extensive ablation studies, have demonstrated the effectiveness of the proposed designs.
DPCAG: A Community Affiliation Graph Generation Model for Preserving Group Relationships
Authors: Xinjian Zhang, Bo Ning, Chengfei Liu
Venue: CIKM
Year: 2024

Graph data has been widely applied due to its powerful expressive capabilities. The release of raw graph data without preprocessing may lead to privacy information leakage. Thus, generating privacy-protected graphs is necessary for data analysis. Current privacy protection methods in graphs focus on securing attributes like degree distribution, triangle counts, and node information, but they often overlook the need to protect user group relationships. Additionally, some privacy-preserving graph publishing methods introduce significant noise due to the chosen graph generation techniques and the points at which noise is added. This paper aims to propose an effective graph synthesis algorithm by using differential privacy named DPCAG (Differentially Private Community Affiliation Graph Generation Model) for protecting user group relationships. Firstly, it is observed that there are numerous small probabilities in the adjacency matrix D generated by the affiliation matrix F, directly utilizing it to construct graph G would result in the generation of a substantial number of redundant edges. Therefore, we introduce a generating threshold theta to filter out unnecessary edges. Secondly, to achieve a better balance between data availability and the level of privacy protection, two budget allocation schemes are designed based on the introduction of k-truss to describe the tightness of group relationships. Lastly, we demonstrate the proposed model satisfies differential privacy mathematically and the effectiveness of DPCAG is validated using four real graph datasets.
VIER: Visual Imagination Enhanced Retrieval in Sponsored Search
Authors: Yadong Zhang, Yuqing Song, Siyu Lu, Qiang Liu, Xingxing Wang
Venue: CIKM
Year: 2024

Embedding-based Retrieval (EBR) has been a fundamental component in sponsored-search systems, which retrieves high-quality products for the user's search query by encoding the information of the query, user and product into dense embeddings. However, due to the characteristic of location-based service, the user input queries suffer from two extremes: overly brief queries with vague intentions and lengthy queries with substantial noise, both of which make it challenging to discern the exact user search intent. In fact, the e-consumers typically have a mental imagery of the product they intend to search for, reflecting their specific purchasing intentions. In this paper, we propose a Visual Imagination Enhanced Retrieval model (VIER) to explore the implicit imagery of users. Specifically, we design a visual imagination network to reconstruct the imagery embeddings that capture both coarse-grained query commonalities and fine-grained user personalities. These pseudo-image representations are integrated with the query and user behavior to enhance the understanding of user search intentions for improved retrieval. According to online A/B tests on Meituan sponsored-search system, our method significantly outperforms baselines in terms of revenue, clicks and click-through rate.
Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
Authors: Hanqi Zhang, Chong Chen, Lang Mei, Qi Liu, Jiaxin Mao
Venue: CIKM
Year: 2024

In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. It is important for DR models to balance both efficiency and effectiveness. Pre-trained language models (PLMs), especially Transformer-based PLMs, have been proven to be effective encoders of DR models. However, the self-attention component in Transformer-based PLM results in a computational complexity that grows quadratically with sequence length, and thus exhibits a slow inference speed for long-text retrieval. Some recently proposed non-Transformer PLMs, especially the Mamba architecture PLMs, have demonstrated not only comparable effectiveness to Transformer-based PLMs on generative language tasks but also better efficiency due to linear time scaling in sequence length. This paper implements the Mamba Retriever to explore whether Mamba can serve as an effective and efficient encoder of DR model for IR tasks. We fine-tune the Mamba Retriever on the classic short-text MS MARCO passage ranking dataset and the long-text LoCoV0 dataset. Experimental results show that (1) on the MS MARCO passage ranking dataset and BEIR, the Mamba Retriever achieves comparable or better effectiveness compared to Transformer-based retrieval models, and the effectiveness grows with the size of the Mamba model; (2) on the long-text LoCoV0 dataset, the Mamba Retriever can extend to longer text length than its pre-trained length after fine-tuning on retrieval task, and it has comparable or better effectiveness compared to other long-text retrieval models; (3) the Mamba Retriever has superior inference speed for long-text retrieval. In conclusion, Mamba Retriever is both effective and efficient, making it a practical model, especially for long-text retrieval.
Multi-view Temporal Knowledge Graph Reasoning
Authors: Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang, Zhiqiang Zhang, Jun Zhou, Deqing Wang
Venue: CIKM
Year: 2024

Temporal Knowledge Graph (TKG) reasoning is a crucial task that aims to predict future facts based on historical information. In the process of reasoning over TKGs, we identify two types of facts that need to be predicted: 1) recurring facts and 2) unknown facts. While existing models emphasize reasoning about recurring facts, they inadvertently overlook the importance of unknown facts. To make better predictions on both facts, we introduce a novel TKG reasoning model, named Multi-view Recurrent Network (MV-NET), which generates different views to capture reasoning patterns for both recurring and unknown facts. Specifically, MV-NET comprises three views: a recurring history view that captures repetitive features, an exploring history view that focuses on exploring new information for unknown facts, and a full history view that assimilates historical information comprehensively. Then, the historical information of each view is encoded by a multi-view recurrent network. To better integrate the embeddings of three views, we employ an adaptive scoring module, which consists of a query-aware attentive fusion mechanism to incorporate the predicted scores from three views, thus obtaining fused scores for prediction. Extensive experiments on three commonly used datasets demonstrate the superiority of MV-NET compared to many state-of-the-art baselines.
Cost-Effective Framework with Optimized Task Decomposition and Batch Prompting for Medical Dialogue Summary
Authors: Chi Zhang, Tao Chen, Jiehao Chen, Hao Wang, Jiyun Shi, Zhaojing Luo, Meihui Zhang
Venue: CIKM
Year: 2024

The generation of medical dialogue notes is essential in healthcare, providing a structured recapitalization of patient-provider interactions. Medical notes are rigorously organized into various sections, including Chief Complaint, History of Present Illness and more. Each section serves a specific purpose to record detailed medical content. Traditionally, this task is labor-intensive, requiring physicians to manually create notes, a process prone to errors. With advancements in AI, it is now feasible to automate the generation of medical notes. There are mainly two categories of methods for automatic medical note generation. Pre-trained language models (PLMs) struggle with unstructured outputs, limited datasets, and inadequate medical terminology. In-context learning (ICL) methods improve accuracy and reduce data requirements but still produce unstructured notes and require high time and cost. To tackle the above challenges, we propose a three-module framework, called CE-DEPT, for accurate, efficient and cost-effective medical note generation. Specifically, the Task Decomposition Module breaks down complete medical dialogues into section-specific dialogues to ensure relevance and accuracy. The Batch Combination Module groups these sections into batches based on disease similarity to reduce costs and improve efficiency. The Note Generation Module employs batch prompting with ICL to generate each section note, followed by combining them into a structured, comprehensive medical note. Experiments on benchmark datasets demonstrated the effectiveness of Task Decomposition and Batch Prompting. Our method, CE-DEPT outperforms the best method by 5% on the ROUGE-1 score, 3% on the Bertscore-F1, a cost-effectiveness improvement of 15%, and a reduction in time consumption of 25% at peak accuracy.
Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph Embeddings
Authors: Bin Zhang, Jiayin Li, Yuanfei Dai
Venue: CIKM
Year: 2024

Lower-dimensional temporal knowledge graph embedding (TKGE) models are crucial for practical applications and resource-limited scenarios, although existing models employ higher-dimensional embeddings in training. In this paper, we propose a new framework for distilling TKGE models via an easy to hard pedagogical principle. The framework utilizes a learnable curriculum temperature (CT) module to optimize and guide the knowledge distillation process dynamically, ensuring that the entire procedure adheres to the principle. It also employs a self-adaptive attention mechanism to endeavor to achieve efficient transfer of knowledge from higher-dimensional models to lower-dimensional ones. Evaluation on various TKGE models and datasets demonstrates the proposed approach significantly reduces the model's parameters without noticeably affecting its performance.
Data Imputation from the Perspective of Graph Dirichlet Energy
Authors: Weiqi Zhang, Guanlue Li, Jianheng Tang, Jia Li, Fugee Tsung
Venue: CIKM
Year: 2024

Data imputation is a crucial task due to the widespread occurrence of missing data. Many methods adopt a two-step approach: initially crafting a preliminary imputation (the "draft") and then refining it to produce the final missing data imputation result, commonly referred to as "draft-then-refine". In our study, we examine this prevalent strategy through the lens of graph Dirichlet energy. We observe that a basic "draft" imputation tends to decrease the Dirichlet energy. Therefore, a subsequent "refine" step is necessary to restore the overall energy balance. Existing refinement techniques, such as the Graph Convolutional Network (GCN), often result in further energy reduction. To address this, we introduce a new framework, the Graph Laplacian Pyramid Network (GLPN). GLPN incorporates a U-shaped autoencoder and residual networks to capture both global and local details effectively. Through extensive experiments on multiple real-world datasets, GLPN consistently outperforms state-of-the-art methods across three different missing data mechanisms. The code is available at https://github.com/liguanlue/GLPN.
AI Agent for Information Retrieval: Generating and Ranking
Authors: Yongfeng Zhang, Zhiwei Liu, Qingsong Wen, Linsey Pang, Wei Liu, Philip S. Yu
Venue: CIKM
Year: 2024

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.
MSKR: Advancing Multi-modal Structured Knowledge Representation with Synergistic Hard Negative Samples
Authors: Shuili Zhang, Hongzhang Mu, Tingwen Liu, Qianqian Tong, Jiawei Sheng
Venue: CIKM
Year: 2024

Despite the notable progress achieved by large-scale vision-language pre-training models in a wide range of multi-modal tasks, their performance often falls short in image-text matching challenges that require an in-depth understanding of structured representations. For instance, when distinguishing between texts or images that are generally similar but have distinct structured knowledge (such as entities and relationships in text, or objects and object attributes in images), the model's capabilities are limited. In this paper, we propose a advancing Multi-modal Structured Knowledge Representation with synergistic hard negative samples (MSKR), thereby significantly improving the model's matching capability for such data. Specifically, our model comprises a structured knowledge-enhanced encoder designed to bolster the structured knowledge inherent in textual data, such as entities, their attributes, and the relationships among these entities as well as structured knowledge within images, focusing on elements like objects and their attributes. To further refine the model's learning process, we produce both image and text challenging negative samples. Extensive experimental evaluations on the Winoground, InpaintCOCO, and MSCOCO benchmark reveal that MSKR significantly outperforms the baseline model, showcasing marked improvements 2.66% on average in structured representation learning compared to the baseline. Moreover, general representation results illustrate that our model not only excels in structured representation learning but also maintains its proficiency in general representation learning.
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems
Authors: Yi Zhang, Ruihong Qiu, Jiajun Liu, Sen Wang
Venue: CIKM
Year: 2024

Offline reinforcement learning (RL) is an effective tool for real-world recommender systems with its capacity to model the dynamic interest of users and its interactive nature. Most existing offline RL recommender systems focus on model-based RL through learning a world model from offline data and building the recommendation policy by interacting with this model. Although these methods have made progress in the recommendation performance, the effectiveness of model-based offline RL methods is often constrained by the accuracy of the estimation of the reward model and the model uncertainties, primarily due to the extreme discrepancy between offline logged data and real-world data in user interactions with online platforms. To fill this gap, a more accurate reward model and uncertainty estimation are needed for the model-based RL methods. In this paper, a novel model-based Reward Shaping in Offline Reinforcement Learning for Recommender Systems, ROLeR, is proposed for reward and uncertainty estimation in recommendation systems. Specifically, a non-parametric reward shaping method is designed to refine the reward model. In addition, a flexible and more representative uncertainty penalty is designed to fit the needs of recommendation systems. Extensive experiments conducted on four benchmark datasets showcase that ROLeR achieves state-of-the-art performance compared with existing baselines. Source code can be downloaded at this address.