OpenRaiser/Intern-Atlas huggingface dataset:
TESSM: Tree-based Selective State Space Models for Efficient Join Order Selection Learning
Authors: Yaohui Chu, Yizhe Liu, Yue Zhang, Xuan Hou, Longfei Yu, Zhaohui Peng
Venue: CIKM
Year: 2024

Join order optimization is pivotal in database query optimization, seeking the most efficient join sequence to reduce execution costs. As more tables join, the complexity surges, turning it into an NP-hard problem due to the exponential growth of possible orders. Deep reinforcement learning (DRL) has recently made significant strides, outperforming traditional algorithms by treating join selection as a Markov Decision Process to devise more effective strategies.Current methods struggle with integrating query semantics and plan structures, as well as encountering issues with complex joins where bottom-up learning can lead to information loss.To tackle these issues, we present the Tree-based Selective State Space Models for Efficient Join Order Selection Learning(TESSM). This framework uses the Tree Mamba architecture to integrate join pattern graphs with execution plan nodes, enhancing long-term dependency information flow. A tiered training strategy enhances the model's training precision and speed.Our approach has proven effective, as evidenced by JOB and TPC-H benchmark tests, showing TESSM's substantial improvements in query optimization efficiency and effectiveness.
Leveraging Knowledge Graphs and LLMs to Support and Monitor Legislative Systems
Authors: Andrea Colombo
Venue: CIKM
Year: 2024

Knowledge Graphs (KGs) have been used to organize large datasets into structured, interconnected information, enhancing data analytics across various fields. In the legislative context, one potential natural application of KGs is modeling the intricate set of interconnections that link laws and their articles with each other and the broader legislative context. At the same time, the rise of large language models (LLMs) such as GPT has opened new opportunities in legal applications, such as text generation and document drafting. Despite their potential, the use of LLMs in legislative contexts is critical since it requires the absence of hallucinations and reliance on up-to-date information, as new laws are published on a daily basis. This work investigates how Legislative Knowledge Graphs and LLMs can synergize and support legislative processes. We address three key questions: the benefits of using KGs for legislative systems, how LLM can support legislative activities by ensuring an accurate output, and how we can allow non-technical users to use such technologies in their activities. To this aim, we develop Legis AI Platform, an interactive platform focused on Italian legislation that enhances the possibility of conducting legislative analysis and that aims to support lawmaking activities.
International Workshop on Online and Adaptive Recommender Systems (OARS 2024)
Authors: Xiquan Cui, Vachik S. Dave, Yi Su, Khalifeh Al Jadda, Srijan Kumar, Julian J. McAuley, Tao Ye, Stephen D. Guo, Chip Huyen
Venue: CIKM
Year: 2024

Recommender system (RecSys) plays important roles in helping users navigate, discover, and consume massive and highly-dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. Therefore, it is important to investigate more on online and adaptive recommender system (OARS) that can adapt in real time to meet user needs and be robust against distribution shifts. Every year, the organizers survey the most important topics for OARS and propose a new workshop program. In light of the recent advancement of LLMs and foundation models in RecSys, in this new edition, we decide to formally add the new topic of foundation and LLM models in OARS. We will invite experts and papers in the field to facilitate its further advancement. Our workshop offers a focused discussion of the new study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia to discuss on new topics in the area, grow a community, and push the direction forward.
Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning
Authors: Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma
Venue: CIKM
Year: 2024

Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space. However, these methods often neglect the rationality of the augmented samples. Due to significant uncertainty, random augmentation can disrupt the semantic information and interest evolution patterns inherent in the original user sequences. Moreover, pulling semantically inconsistent sequences closer in the representation space can render the user sequence embeddings insensitive to variations in user preferences, which contradicts the primary objective of sequential recommendation. To address these limitations, we propose the Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. The core idea is to leverage context information to generate more reasonable augmented views. Specifically, CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. These generated items are aligned with their respective context information and can effectively replace the corresponding original items, thereby generating a positive view of the original sequence. By considering two different augmentations of the same user sequence, we can construct a pair of positive samples for contrastive learning. To ensure representation cohesion, we train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model. Extensive experiments on five benchmark datasets demonstrate the advantages of our proposed method over existing baselines.
UniEmbedding: Learning Universal Multi-Modal Multi-Domain Item Embeddings via User-View Contrastive Learning
Authors: Boqi Dai, Zhaocheng Du, Jieming Zhu, Jintao Xu, Deqing Zou, Quanyu Dai, Zhenhua Dong, Rui Zhang, Hai-Tao Zheng
Venue: CIKM
Year: 2024

Learning high-quality item embeddings is crucial for recommendation tasks such as matching and ranking. However, existing methods often rely on ID-based item embeddings learned end-to-end with downstream recommendation models, which may suffer from overfitting and limited generalizability. In this paper, we aim to learn universal item embeddings (dubbed UniEmbedding) that capture multi-modal semantics, generalize across multiple domains, and serve different downstream tasks. To achieve this goal, we introduce the UniEmbedding pretraining framework, which includes three modules: a domain-aware multi-modal adapter, a user-view projection module, and contrastive learning objectives across domains. Compared to naive ID embeddings, UniEmbedding provides rich semantic information that generalizes more effectively across domains. Unlike multi-modal embeddings directly extracted from off-the-shelf pretrained models, UniEmbedding achieves better alignment between content semantics and behaviors. We evaluated UniEmbedding on both public and industrial datasets, demonstrating its effectiveness in matching and ranking tasks. Furthermore, UniEmbedding has been deployed in multiple recommendation applications at Huawei, resulting in significant gains in user engagement metrics.
MSG-Chart: Multimodal Scene Graph for ChartQA
Authors: Yue Dai, Soyeon Caren Han, Wei Liu
Venue: CIKM
Year: 2024

Automatic Chart Question Answering (ChartQA) is challenging due to the complex distribution of chart elements with patterns of the underlying data not explicitly displayed in charts. To address this challenge, we design a joint multimodal scene graph for charts to explicitly represent the relationships between chart elements and their patterns. Our proposed multimodal scene graph includes a visual graph and a textual graph to jointly capture the structural and semantical knowledge from the chart. This graph module can be easily integrated with different vision transformers as inductive bias. Our experiments demonstrate that incorporating the proposed graph module enhances the understanding of charts' elements' structure and semantics, thereby improving performance on publicly available benchmarks, ChartQA and OpenCQA.
Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement Learning
Authors: Aijun Dai, Zhenyu Zhu, Haiqing Hu, Guoyu Tang, Lin Liu, Sulong Xu
Venue: CIKM
Year: 2024

In the domain of e-commerce, query rewriting is a potent strategy for bridging the lexical gap between search queries and product descriptions, thereby enhancing the recall rate of search engines. This research introduces a query rewriting framework predicated on large language models (LLM), encompassing three phases of training: domain-specific pre-training, supervised fine-tuning (SFT) and reinforcement learning (RL) for objective alignment. To detail, the process initiates with domain-specific pre-training using consumer behavior data and product descriptions from JD.com. Subsequently, we filter and utilize high-quality query-rewrite pairs for SFT. The final stage employs RL to refine the model's objective alignment, utilizing an offline search system as the simulation environment. The RL's training reward is derived from the recall rate, aiming to optimize the number of relevant products the rewrites retrieve. Through offline evaluations, our method has demonstrated its capacity to substantially enhance the efficacy of LLMs for e-commerce query rewriting. Moreover, online A/B testing has corroborated that our approach significantly boosts the number of purchases made per user (UCVR). Since December 2023, our approach has been successfully implemented on JD.com, one of China's most frequented online shopping platforms.
ByGCN: Spatial Temporal Byroad-Aware Graph Convolution Network for Traffic Flow Prediction in Road Networks
Authors: Tangpeng Dan, Xiao Pan, Bolong Zheng, Xiaofeng Meng
Venue: CIKM
Year: 2024

As a fundamental technology in intelligent transportation systems (ITS), accurate traffic flow prediction has emerged as a critical challenge in real-time applications. How to fully utilize the traffic data, and capture the spatial temporal correlation are keys to improve the model's prediction ability. Numerous neural networks have been proposed to address this issue. However, most of these existing methods have the following two problems: 1) Lack of byroads information. Meaning that the existing methods do not consider the byroads in real-life traffic environments; 2) Lack of potential learning ability. Meaning that the existing methods suffer the non-similar forgetting and hard to gain the multi-hop correlation. To overcome these problems, we propose a novel Spatial Temporal Byroad-Aware Graph Convolution Network (ByGCN) in this paper. ByGCN consists of byroad identification and spatial temporal learning modules. In the first module, we design spatial temporal decoupling and graph diffusion blocks to identify the byroads and reconstruct them into the flow data. In the second module, with the help of spatial temporal attention and GCN, our module can capture the complex spatial temporal correlation. Experiments on four real-world traffic datasets demonstrate that ByGCN outperforms the state-of-the-art methods.
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning
Authors: Dillon Davis, Huiji Gao, Thomas Legrand, Malay Haldar, Alex Deng, Han Zhao, Liwei He, Sanjeev Katariya
Venue: CIKM
Year: 2024

The Airbnb search system grapples with many unique challenges as it continues to evolve. We oversee a marketplace that is nuanced by geography, diversity of homes, and guests with a variety of preferences. Crafting an efficient search system that can accommodate diverse guest needs, while showcasing relevant homes lies at the heart of Airbnb's success. Airbnb search has many challenges that parallel other recommendation and search systems but it has a unique information retrieval problem, upstream of ranking, called location retrieval. It requires defining a topological map area that is relevant to the searched query for homes listing retrieval. The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. Despite the lack of suitable, prevalent machine learning based approaches, we tackle cold start, generalization, differentiation and algorithmic bias. We detail the efficacy of heuristics, statistics, machine learning, and reinforcement learning approaches to solve these challenges, particularly for systems that are often unexplored by current literature.
Automated Nanoparticle Image Processing Pipeline for AI-Driven Materials Characterization
Authors: Alexandra L. Day, Carolin B. Wahl, Roberto dos Reis, Wei-keng Liao, Vinayak P. Dravid, Alok N. Choudhary, Ankit Agrawal
Venue: CIKM
Year: 2024

Recent innovations have made it possible to produce millions of distinct nanoparticles on a chip. These vast volumes of data are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image preprocessing step before model training can produce significantly higher-performing models in a fraction of the time and make the model more robust to different image noise levels and microscope acquisition settings. The proposed image processing pipeline effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures, resulting in higher performance and significant cost savings. Experiments demonstrate superior performance relative to our baseline, including a 15% improvement in recall and more than a 10% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives in our application, and our best-performing model obtains a precision of 97.3% and weighted F-score of 95.9% on an unseen test set. Additionally, model training time is reduced from 15.5 hours to 32 seconds. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will offer a considerable speedup in the laboratory, allowing researchers to rapidly and accurately analyze much greater volumes of data and accelerate materials discovery.
PROSPECT: Learn MLPs on Graphs Robust against Adversarial Structure Attacks
Authors: Bowen Deng, Jialong Chen, Yanming Hu, Zhiyong Xu, Chuan Chen, Tao Zhang
Venue: CIKM
Year: 2024

Current adversarial defense methods for GNNs exhibit critical limitations obstructing real-world application: 1) inadequate adaptability to graph heterophily, 2) absent generalizability to early GNNs like GraphSAGE used downstream, and 3) low inference scalability unacceptable for resource-constrained scenarios. To simultaneously address these challenges, we propose the first online GNN-MLP distillation framework PROSPECT, which merges the complementary knowledge of MLP and GNN and can thus learn GNN and MLP robust against adversarial structure attacks on both homophilic and heterophilic graphs. PROSPECT integrates seamlessly into GraphSAGE and achieves inference scalability exponentially higher than conventional GNNs. To mitigate potential convergence failure caused by inductive bias conflicts between the heterogeneous MLP and GNN, we propose the Quasi-Alternating Cosine Annealing (QACA) learning rate scheduler, inspired by our convergence analysis of the involved MLP. Experiments on homophilic and heterophilic graphs demonstrate the advantages of PROSPECT over current defenses and offline GNN-MLP distillation methods in terms of adversarial robustness and clean accuracy, the inference scalability of PROSPECT orders of magnitude higher than existing defenses, and the effectiveness of QACA.
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining
Authors: Audrey Der, Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Zhongfang Zhuang, Vivian Lai, Junpeng Wang, Liang Wang, Wei Zhang, Eamonn J. Keogh
Venue: CIKM
Year: 2024

Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-supervised time series PTMs were surpassed by simple supervised models. We hypothesize this undesired phenomenon may be caused by data scarcity. Our results indicate that replacing a real-data pretraining set with a greater volume of only generated samples produces noticeable improvement.
A Supervised BERT Model for Identifying Core-Intent Bearing Phrases in e-Commerce Queries
Authors: Abhishek Sudhakar Deshmukh, Arnab Dutta
Venue: CIKM
Year: 2024

In the realm of e-Commerce, a fundamental problem is accurate interpretation of users' core intent. The intent is often subtly expressed implicitly or stated explicitly with the usage of verbose tokens or key phrases in a user query. In this work, we focus on the later class of problems where we identify a subset of query tokens which are the primary intent bearing phrases that convey explicit intents. We did not solve this as an intent detection problem but rather an immutable component detection problem because we believe that discovering the immutable phrases in a query entails that those are the intent bearing phrases. Furthermore, identifying a certain set of query tokens as immutable ensures better downstream processing in terms of unprecedented token handling, query category detection or query rewrites. We have developed a BERT based supervised learned model which can identify core-intent tokens, thereby improving F1 score over the baseline by over 35%. Furthermore, we integrated our proposed approach for a query recovery strategy which produces approximately 11.9% improvement in offline relevance scores compared to the production model.
A Skill Proficiency Framework for Workforce Learning and Development
Authors: Rebecca Dew, Mingzhao Li, Sandya Baratha Raj
Venue: CIKM
Year: 2024

Understanding the skills and proficiency levels required for various roles is crucial for effective workforce planning, learning and development. In this paper, we propose a robust skill proficiency modeling framework that offers a structured method to help describe, assess and develop proficiency in key skills, facilitating individuals' career pathways and aiding organizations in talent management and adaptability. We first design a skill proficiency description pipeline, which generates statements describing the requirements at each proficiency level of a skill. Following this, we build a skill proficiency by occupation model using large-scale job ad data to help organizations and individuals understand the skill proficiency requirements for different roles. Finally,we design a visual analytics system, based on a real-world career pathway scenario, to demonstrate the practical usefulness and effectiveness of our framework. A demo video is available at www.dropbox.com/scl/fi/nd0f3vi03n12g4y0sluaw/cikm24_demo.mp4?rlkey=55vya144q5ftai1uqqaubr5u5.
Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs
Authors: Sanjay Bhargav Dharavath, Tanmoy Dam, Supriyo Chakraborty, Prithwiraj Roy, Aniruddha Maiti
Venue: CIKM
Year: 2024

The field of autonomous vehicles (AVs) predominantly leverages multi-modal integration of LiDAR and camera data to achieve better performance compared to using a single modality. However, the fusion process encounters challenges in detecting distant objects due to the disparity between the high resolution of cameras and the sparse data from LiDAR. Insufficient integration of global perspectives with local-level details results in sub-optimal fusion performance.To address this issue, we have developed an innovative two-stage fusion process called Quantum Inverse Contextual Vision Transformers (Q-ICVT). This approach leverages adiabatic computing in quantum concepts to create a novel reversible vision transformer known as the Global Adiabatic Transformer (GAT). GAT aggregates sparse LiDAR features with semantic features in dense images for cross-modal integration in a global form. Additionally, the Sparse Expert of Local Fusion (SELF) module maps the sparse LiDAR 3D proposals and encodes position information of the raw point cloud onto the dense camera feature space using a gating point fusion approach. Our experiments show that Q-ICVT achieves an mAPH of 82.54 for L2 difficulties on the Waymo dataset, improving by 1.88% over current state-of-the-art fusion methods. We also analyze GAT and SELF in ablation studies to highlight the impact of Q-ICVT. Our code is available at https://github.com/sanjay-810/Qicvt
DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting
Authors: Ruixin Ding, Yuqi Chen, Yu-Ting Lan, Wei Zhang
Venue: CIKM
Year: 2024

Long-term time series forecasting (LTSF) has been widely applied in finance, traffic prediction, and other domains. Recently, patch-based transformers have emerged as a promising approach, segmenting data into sub-level patches that serve as input tokens. However, existing methods mostly rely on predetermined patch lengths, necessitating expert knowledge and posing challenges in capturing diverse characteristics across various scales. Moreover, time series data exhibit diverse variations and fluctuations across different temporal scales, which traditional approaches struggle to model effectively. In this paper, we propose a dynamic tokenizer with a dynamic sparse learning algorithm to capture diverse receptive fields and sparse patterns of time series data. In order to build hierarchical receptive fields, we develop a multi-scale Transformer model, coupled with multi-scale sequence extraction, capable of capturing multi-resolution features. Additionally, we introduce a group-aware rotary position encoding technique to enhance intra- and inter-group position awareness among representations across different temporal scales. Our proposed model, named DRFormer, is evaluated on various real-world datasets, and experimental results demonstrate its superiority compared to existing methods. Our code is available at: https://github.com/ruixindingECNU/DRFormer.