OpenRaiser/Intern-Atlas huggingface dataset:
Spatio-Temporal Sequence Modeling for Traffic Signal Control
Authors: Qian Sun, Le Zhang, Jingbo Zhou, Rui Zha, Yu Mei, Chujie Tian, Hui Xiong
Venue: CIKM
Year: 2024

Traffic Signal Control(TSC), a pivotal and challenging research area in the transportation domain, aims to alleviate congestion at urban intersections by optimizing vehicular flows from different inflow directions. While large efforts have been focused on using Reinforcement Learning(RL) based methods to tackle the TSC problem, it possesses constraints such as unpredictable training duration and risks of online exploration, limiting its real-world deployment. Recently, offline RL has emerged as a new solution by transitioning from learning through online interactions to deriving policies from pre-collected datasets, which guarantees a safer and more efficient learning process. However, existing offline methods overlook the crucial temporal and spatial intricacy among data from different traffic signals at different timesteps, which leads to suboptimal performance. To this end, in this paper, we present an innovative formulation of the offline TSC problem by introducing a spatio-temporal graph to model the historical Markov Decision Process sequences across all traffic signals within the road network. Along this line, we propose STLight, a novel spatio-temporal sequence modeling approach to predict optimal actions for the signals from historical data, accounting for the inherent inter-dependencies among them. Specifically, we incorporate a spatio-temporal encoder to represent states, actions, and returns by capturing dynamic and spatially dependent information. The ordered space-time-aware representations are further fed to the Action Decoder to predict signal phase actions in an auto-regressive manner, accounting for the hidden dependencies between the actions and the reward and state tokens. Furthermore, to adaptively handle tasks with different levels of congestion scenarios, we incorporate space-aware return-based contrastive learning to automatically differentiate data samples with disparate traffic flow patterns. Finally, extensive experiments conducted on two public real-world traffic datasets clearly demonstrate the superior performance of the proposed model over both the state-of-the-art online and offline traffic signal control baselines.
Dynamic Graph-based Deep Reinforcement Learning with Long and Short-term Relation Modeling for Portfolio Optimization
Authors: Haoyu Sun, Yuxuan Bian, Li Han, Peng Zhu, Dawei Cheng, Yuqi Liang
Venue: CIKM
Year: 2024

Portfolio optimization is a significant concern in finance. Existing research on portfolio optimization fails to adequately learn from the long and short-term relationships among equities, which inevitably leads to suboptimal performance. In this paper, we propose a Dynamic Graph-based Deep Reinforcement Learning (DGDRL) for optimal portfolio decisions. We achieve this goal by devising two mechanisms for naturally modeling the financial market. Firstly, we utilize the static and dynamic graphs to represent the long and short-term relations, which are then naturally represented by the proposed multi-channel graph attention neural network. Secondly, compared with the traditional two-phase approach, forecasting equity's trend and then weighting them by combinatorial optimization, we naturally optimize the portfolio decisions, which could directly guide the model to converge to optimal rewards. Through extensive experiments on three real-world datasets, we have demonstrated that our method significantly outperforms state-of-the-art benchmark methods in portfolio management. Furthermore, the evaluation of the industrial trading system has shown the applicability of our model to real-world financial markets.
FABLE: Approximate Butterfly Counting in Bipartite Graph Stream with Duplicate Edges
Authors: Guozhang Sun, Yuhai Zhao, Yuan Li
Venue: CIKM
Year: 2024

Bipartite graph models the relationship between two different sets of entities. Such graph data become more dynamic and are organized as stream with duplicate edges in real-word applications such as customer-product in e-commerce. A butterfly, (2,2)-biclique, is the simplest cohesive substructure and of great importance in a bipartite graph. However, it is challenging to estimate the number of butterflies in large scale and high dynamic bipartite graph stream when given a limited memory. Besides, existing works for butterfly counting assume no duplicate edges in the bipartite graph stream, which cause less accuracy in bipartite graph stream with duplicate edges. In this paper, we propose FABLE, a Fixed-size memory Approximate Butterfly counting algorithm for dupLicate Edges in bipartite graph stream. In FABLE, we compute the number of distinct edges by maintaining an ordered list of edge priorities for replacement and sampling. We provide theoretical proof of unbiasedness and derive the variance of butterfly count. Our extensive experiments on 5 real-world datasets confirm that our approach has higher accuracy compared with the baseline method under the same memory usage.
Towards Making Effective Machine Learning Decisions Against Out-of-Distribution Data
Authors: Lakpa Dorje Tamang
Venue: CIKM
Year: 2024

Conventional machine learning systems operate on the assumption of independent and identical distribution (i.i.d), where both the training and test data share a similar sample space, and no distribution shift exists between them. However, this assumption does not hold in practical deployment scenarios, making it crucial to develop methodologies that address the non-trivial task of data distribution shift. In our research, we aim to address this problem by developing ML algorithms that explicitly achieve promising performance when subjected to various types of out-of-distribution (OOD) data. Specifically, we approach the problem by categorizing the data distribution shifts into two types: covariate shifts and semantic shifts, and proposing effective methodologies to tackle each type independently and conjointly while validating them with different types of datasets. We aim to propose ideas that are compatible with existing deep neural networks to perform detection and/or generalization of the test instances that are shifted in semantic and covariate space, respectively.
Natural Language-Assisted Multi-modal Medication Recommendation
Authors: Jie Tan, Yu Rong, Kangfei Zhao, Tian Bian, Tingyang Xu, Junzhou Huang, Hong Cheng, Helen Meng
Venue: CIKM
Year: 2024

Combinatorial medication recommendation (CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extract meaningful information from electronic health records (EHRs) to facilitate combinatorial medication recommendations. Existing learning-based approaches further consider the chemical structures of medications, but ignore the textual medication descriptions in which the functionalities are clearly described. Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation (NLA-MMR), a multimodal alignment framework designed to learn knowledge from the patient view and medication view jointly. Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities. In this vein, we employ pretrained language models (PLMs) to extract in-domain knowledge regarding patients and medications, serving as the foundational representation for both modalities. In the medication modality, we exploit both chemical structures and textual descriptions to create medication representations. In the patient modality, we generate the patient representations based on textual descriptions of diagnosis, procedure, and symptom. Extensive experiments conducted on three publicly accessible datasets demonstrate that NLA-MMR achieves new state-of-the-art performance, with a notable average improvement of 4.72% in Jaccard score.
LAMRec: Label-aware Multi-view Drug Recommendation
Authors: Yunsen Tang, Ning Liu, Haitao Yuan, Yonghe Yan, Lei Liu, Weixing Tan, Lizhen Cui
Venue: CIKM
Year: 2024

The drug recommendation task aims to predict safe and effective drug prescriptions based on the patients' historical electronic health records (EHRs). However, existing drug recommendation models generally have two limitations. First, they neglect the inherent characteristics of multiple views existing in patients' clinical data (e.g., diagnoses and procedures), leading to fragmented and inconsistent patient representations. Second, they do not fully exploit drug label information. Most models do not explicitly establish a mapping relationship between drug labels and patients' historical visits. To address these two problems, we proposed a label-aware multi-view drug recommendation model named LAMRec. In particular, LAMRec uses a cross-attention module to fuse information from the diagnosis and procedure views, and increases the mutual information of patient multi-view representations through multi-view contrastive loss; the label-wise attention mechanism fully explores drug label information by constructing an adaptive mapping of drug-visit to generate personalized representations that are aware of the drug-related visit information. Experiments on three real world medical datasets demonstrated the superiority of LAMRec, with a relative reduction of 5.25% in DDI compared to the optimal baseline, a relative improvement of 4.20% in Jaccard similarity scores, and a relative improvement of 3.10% in F1 scores. We released the code online at: https://github.com/Tyunsen/LAMRec.
EasyST: A Simple Framework for Spatio-Temporal Prediction
Authors: Jiabin Tang, Wei Wei, Lianghao Xia, Chao Huang
Venue: CIKM
Year: 2024

Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, scalability and generalization challenges remain significant obstacles. Advanced models often rely on Graph Neural Networks to encode spatial and temporal correlations, but struggle with the increased complexity of large-scale datasets. The recursive GNN-based message passing schemes used in these models hinder their training and deployment in real-life urban sensing scenarios. Moreover, long-spanning large-scale spatio-temporal data introduce distribution shifts, necessitating improved generalization performance. To address these challenges, we propose a simple framework for spatio-temporal prediction - EasyST paradigm. It learns lightweight and robust Multi-Layer Perceptrons (MLPs) by effectively distilling knowledge from complex spatio-temporal GNNs. We ensure robust knowledge distillation by integrating the spatio-temporal information bottleneck with teacher-bounded regression loss, filtering out task-irrelevant noise and avoiding erroneous guidance. We further enhance the generalization ability of the student model by incorporating spatial and temporal prompts to provide downstream task contexts. Evaluation on three spatio-temporal datasets for urban computing tasks demonstrates that EasyST surpasses state-of-the-art approaches in terms of efficiency and accuracy. The implementation code is available at https://github.com/HKUDS/EasyST.
TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs by Learning Relative Preference
Authors: Zuoli Tang, Zhaoxin Huan, Zihao Li, Shirui Hu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li
Venue: CIKM
Year: 2024

Recently, the item textual information has been exploited with pre-trained language models (PLMs) to enrich the representations of tail items. The underlying idea is to align the hot items and tail items in terms of the external semantic knowledge covered by the PLM. However, it is non-trivial to eliminate the popularity bias by exploiting the textual semantics. One major obstacle is that the model supervision still counts on the sparse yet binary user behaviors. In the preliminary investigation, we discover that text-based recommendations also suffer from the popularity bias. To this end, we propose a novel self-distillation framework based on a pre-trained language model, named Staple. The proposed Staple consists of two main components: ranker model and recommender model, which are both instantiated as a PLM towards exploiting the item textual semantics. Motivated by the recent success of reinforcement learning with human feedback (RLHF), the proposed Staple aims to recover the relative preference by learning a fair ranker model that can successfully distinguish the preference levels for uninteracted items. Specifically, analogous to the training of large language models (LLMs), we introduce a pre-training and a fair supervised fine-tuning with a decoupled layer to build the ranker model. Then, similar to RLHF for LLM training, we utilize the relative preference information estimated by the ranker over candidate items to complement the learning of the recommender model. We show that this RLHF process can be reformed as an efficient distillation learning process. We conduct extensive experiments on three real-world datasets. In addition to the performance metrics, we employ two additional metrics to measure fairness and debiased performance. The experiments show that our method can significantly improve the item exposure fairness of recommendation and mitigate popularity bias, while also improving the recommendation performance. The source code is available at https://github.com/WHUIR/STAPLE.
Embedding Knowledge Graphs in Function Spaces
Authors: Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
Venue: CIKM
Year: 2024

We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Initially employing polynomial functions to compute embeddings, we progress to more intricate representations using neural networks with varying layer complexities. We argue that employing functions for embedding computation enhances expressiveness and allows for more degrees of freedom, enabling operations such as composition, derivatives and primitive of entities representation. Additionally, we meticulously outline the step-by-step construction of our approach and provide code for reproducibility, thereby facilitating further exploration and application in the field.
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Authors: Phan Nguyen Minh Thao, Cong-Tinh Dao, Chenwei Wu, Jian-Zhe Wang, Shun Liu, Jun-En Ding, David S. Restrepo, Feng Liu, Fang-Ming Hung, Wen-Chih Peng
Venue: CIKM
Year: 2024

Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use complementary multimodal EHR data sources to get a clearer picture of patients' health and support clinical decision-making. However, most EHR predictive models do not reflect these procedures, as they either focus on a single modality or overlook the inter-modality interactions/redundancy. In this work, we propose MEDFuse, a Multimodal EHR Data Fusion framework that incorporates masked lab-test modeling and large language models (LLMs) to effectively integrate structured and unstructured medical data. MEDFuse leverages multimodal embeddings extracted from two sources: LLMs fine-tuned on free clinical text and masked tabular transformers trained on structured lab test results. We design a disentangled transformer module, optimized by a mutual information loss to 1) decouple modality-specific and modality-shared information and 2) extract useful joint representation from the noise and redundancy present in clinical notes. Through comprehensive validation on the public MIMIC-III dataset and the in-house FEMH dataset, MEDFuse demonstrates great potential in advancing clinical predictions, achieving over 90% F1 score in the 10-disease multi-label classification task.
MERLIN: Multimodal & Multilingual Embedding for Recommendations at Large-scale via Item Associations
Authors: Sambeet Tiady, Arihant Jain, Dween Rabius Sanny, Khushi Gupta, Srinivas Virinchi, Swapnil Gupta, Anoop Saladi, Deepak Gupta
Venue: CIKM
Year: 2024

Product recommendations incentivize customers to make multi-unit purchases by surfacing relevant products, leading to lower cost per unit for e-commerce stores and lower prices for their customers. However, the humongous scale of products, implicit co-purchase asymmetry and variation in co-purchase behavior across different categories, are orthogonal problems to solve. To address these problems, we propose MERLIN (Multimodal & Multilingual Embedding for Recommendations at Large-scale via Item associations), a Graph Neural Network that generates product recommendations from a heterogeneous and directed product graph. We mine category associations to remove noisy product co-purchase associations, leading to higher quality recommendations. Leveraging product co-view relationships, we finetune SentenceBERT model for textual representation, and train a self-supervised knowledge distillation model to learn visual representation, which allows us to learn product representations which are multi-lingual and multi-modal in nature. We selectively align node embeddings leveraging co-viewed products. MERLIN model can handle node asymmetry by learning dual embeddings for each product, and can generate recommendations for cold-start products by employing catalog metadata such as title, category and image. Extensive offline experiments on internal and external datasets show that MERLIN model outperforms state-of-the-art baselines for node recommendation and link prediction task. We conduct ablations to quantify the impact of our model components and choices. Further, MERLIN model delivers significant improvement in sales measured through an A/B experiment.
STAR: Sparse Text Approach for Recommendation
Authors: Anna Tigunova, Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Venue: CIKM
Year: 2024

In this work we propose to adapt Learned Sparse Retrieval, an emerging approach in IR, to text-centric content-based recommendations, leveraging the strengths of transformer models for an efficient and interpretable user-item matching. We conduct extensive experiments, showing that our LSR-based recommender, dubbed STAR, outperforms existing dense bi-encoder baselines on three recommendation domains. The obtained word-level representations of users and items are easy to examine and result in over 10x more compact indexes.
Re-evaluating the Command-and-Control Paradigm in Conversational Search Interactions
Authors: Johanne R. Trippas, Luke Gallagher, Joel Mackenzie
Venue: CIKM
Year: 2024

Conversational assistants are becoming prevalent among the wider population due to their simplicity and increasing utility. However, the shortcomings of these tools are as renowned as their benefits. In this work, we present a "first look" at an extensive collection of conversational queries, aiming to identify limitations and improvement opportunities specifically related to information access (i.e., search interactions). We explore over 600,000 Google Assistant interactions from 173 unique users, examining usage trends and the resulting deficiencies and strengths of these assistants. We aim to provide a balanced assessment, highlighting the assistant's shortcomings in supporting users and delivering relevant information to user needs and areas where it demonstrates a reasonable response to user inputs. Our analysis shows that, although most users conduct information-seeking tasks, there is little evidence of complex information-seeking behaviour, with most interactions consisting of simple, imperative instructions. Finally, we find that conversational devices allow users to benefit from increased naturalistic interactions and the ability to apply acquired information in situ, a novel observation for conversational information seeking.
LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting
Authors: Hubert Truchan, Christian Kalfar, Zahra Ahmadi
Venue: CIKM
Year: 2024

The progress of deep-learning-based forecasting architectures is evident through their expanding parameter configurations. However, the need for rapid online decision making in practical scenarios calls for an alternative strategy, highlighting the necessity for networks that are not only adaptive but also efficient in real-time operations. This shift is critical as we confront three principal challenges in deep-learning-based forecasting frameworks: (i) the inherent limitations of transformers, which, despite their attempts to preserve ordering information, the temporal information loss due to the permutation-invariant nature of self-attention mechanisms is inevitable, (ii) the inefficacy of linear models in capturing the dynamic interactions within swiftly evolving signals; and (iii) the incapacity of tree-based approaches to extrapolating beyond values present in the training set. In response to these challenges, we introduce LTBoost, an innovative boosted hybrid of linear and tree-based ensemble gradient algorithms tailored for long-term time series forecasting (LTSF) tasks, scalable to high data dimensions. LTBoost employs a dual strategy, beginning with a linear regression model to capture trends and extrapolate beyond known data, complemented by a robust nonlinear tree-based model that focuses on the residuals. This boosted hybrid approach not only addresses the challenges posed by existing models but also significantly improves forecast accuracy. The effectiveness of LTBoost is validated through empirical experiments conducted on nine well-established benchmark datasets, demonstrating superior performance and achieving state-of-the-art results in 32 out of 36 cases, measured by mean absolute error (MAE). Our findings also explore the impact of lag features and signal normalization techniques, demonstrating further improvements in predictive accuracy. This hybrid and highly effective approach highlights LTBoost's innovation and its resolution of specific forecasting challenges, setting the stage for its contribution to the field of time series forecasting, paving the way for its application in diverse real-world scenarios.
Multi-Graph Explorer: A Framework for Advanced Multi-Graph Analysis and Method Development
Authors: Yorgos Tsitsikas, Evangelos E. Papalexakis
Venue: CIKM
Year: 2024

In recent years, multi-graphs have been gaining increasing popularity due to their ability to better capture the multi-faceted information of real-world graphs. This, in turn, has consistently provided superior insights and performance in related machine learning tasks. However, the analysis of real-world multi-graphs and the development of multi-graph methods is currently stifled by a few limitations. On one side, researchers often struggle to properly evaluate the performance of multi-graph methods they design due to a lack of high-quality benchmarks, but also a lack of tools that allow for efficient and seamless experimentation. On the other side, practitioners aiming to analyze real-world multi-graphs often struggle obtaining robust insights due to a lack of high-quality multi-graph methods. To this end, we present Multi-Graph Explorer: a MATLAB software designed to offer a user-friendly yet comprehensive, flexible, and extensible workflow for multi-graph analysis, aiming to break these barriers and accelerate progress in machine learning tasks involving multi-graphs.