OpenRaiser/Intern-Atlas huggingface dataset:
A System for Time Series Feature Extraction in Federated Learning
Authors: Siqi Wang, Jiashu Li, Mian Lu, Zhao Zheng, Yuqiang Chen, Bingsheng He
Venue: CIKM
Year: 2022

Federated learning (FL), which enables collaborative learning without revealing raw data, is an emerging topic in privacy-preserving machine learning. Based on our experiences in thousands of real-world applications, time-series feature extraction plays a significant role in improving model quality. In this work, we propose a system automatically integrating time series feature extraction for training FL models. Our experiments show that by adopting time series feature extraction, the model accuracy (AUC) is improved by 3% on average, and recall is increased by 10% in recommender systems. We have open-sourced the project https://github.com/4paradigm/tsfe and provided a step by step demonstration on how audiences can use our system to create their own FL pipeline that extracts time series features. Demonstration video at: https://youtu.be/UW27dWT-ays
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding
Authors: Bingning Wang, Feiyang Lv, Ting Yao, Jin Ma, Yu Luo, Haijin Liang
Venue: CIKM
Year: 2022

Visual question answering is an important task in both natural language and vision understanding. However, in most of the public visual question answering datasets such as VQA, CLEVR, the questions are human generated that specific to the given image, such as 'What color are her eyes?'. The human generated crowdsourcing questions are relatively simple and sometimes have the bias toward certain entities or attributes [1, 55]. In this paper, we introduce a new question answering dataset based on image-ChiQA. It contains the real-world queries issued by internet users, combined with several related open-domain images. The system should determine whether the image could answer the question or not. Different from previous VQA datasets, the questions are real-world image-independent queries that are more various and unbiased. Compared with previous image-retrieval or image-caption datasets, the ChiQA not only measures the relatedness but also measures the answerability, which demands more fine-grained vision and language reasoning. ChiQA contains more than 40K questions and more than 200K question-images pairs. A three-level 2/1/0 label is assigned to each pair indicating perfect answer, partially answer and irrelevant. Data analysis shows ChiQA requires a deep understanding of both language and vision, including grounding, comparisons, and reading. We evaluate several state-of-the-art visual-language models such as ALBEF, demonstrating that there is still a large room for improvements on ChiQA.
CRUX: Crowdsourced Materials Science Resource and Workflow Exploration
Authors: Mengying Wang, Hanchao Ma, Abhishek Daundkar, Sheng Guan, Yiyang Bian, Alp Sehirlioglu, Yinghui Wu
Venue: CIKM
Year: 2022

Modern multidisciplinary materials science routinely processes scientific workflows that integrate different data resources (e.g., X-ray data, scripts, analytical results). Most of such data resources are isolated in research labs, created ad-hocly, and remain underutilized. We demonstrate CRUX, a Crowdsourced platform for materials data ResoUrces and workflow eXploration. CRUX is empowered by coherent data-workflow modeling, knowledge-based resource assembly for workflow search, and data provenance to support workflow exploration. CRUX allows users to declare parameterized workflows as graph patterns, and automatically recommends crowdsourced resources with quality guarantees. We demonstrate the ease-of-use and the performance of CRUX with three categories of queries: data search, workflow recommendation, and resource exploration. We make case of CRUX for peak finding in X-ray Diffraction (XRD) data, a cornerstone task in materials research. We show that CRUX enables new interactive paradigms to explore and design workflows for data analysts in general.
Target Interest Distillation for Multi-Interest Recommendation
Authors: Chenyang Wang, Zhefan Wang, Yankai Liu, Yang Ge, Weizhi Ma, Min Zhang, Yiqun Liu, Junlan Feng, Chao Deng, Shaoping Ma
Venue: CIKM
Year: 2022

Sequential recommendation aims at predicting the next item that the user may be interested in given the historical interaction sequence. Typical neural models derive a single history embedding to represent the user's interests. Moving one step forward, recent studies point out that multiple sequence embeddings can help to better capture multi-faceted user interests. However, when ranking candidate items, these methods usually adopt the greedy inference strategy. This approach uses the best matching interest for each candidate item to calculate the ranking score, neglecting the target interest distribution in different contexts, which might lead to incompatibility with the current user intent. In this paper, we propose to enhance multi-interest recommendation by predicting the target user interest with a separate interest predictor and a specifically designed distillation loss. The proposed framework consists of two modules: the 1) multi-interest extractor to generate multiple embeddings regarding different user interests; and the 2) target-interest predictor to predict the interest distribution in the current context, which will be further utilized to dynamically aggregate multi-interest embeddings. To provide explicit supervision signals to the target-interest predictor, we devise a target-interest distillation loss that uses the similarity between the target item and multi-interest embeddings as the soft label of the target interest. This helps the target-interest predictor to accurately predict the user interest at the inference stage and enhances its generalization ability. Extensive experiments on three real-world datasets show the effectiveness and flexibility of the proposed framework.
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
Authors: Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang
Venue: CIKM
Year: 2022

Training graph neural networks (GNNs) with good generalizability on large-scale graphs is a challenging problem. Existing methods mainly divide the input graph into multiple subgraphs and train them in different batches to improve training scalability. However, the local batches obtained by such a strategy could contain topological bias compared with the complete graph structure. It has been studied that the topological bias results in more significant gaps between training and testing performances, or worse generalization robustness. A straightforward solution is to utilize contrastive learning, and train node embeddings to be robust and invariant among the augmented imperfect graphs. However, most of the existing work are inefficient by contrasting extensive node pairs at the large-scale graph. With random data augmentation, they may deteriorate the embedding process by transforming well-sampled batches into meaningless graph structures. To bridge the gap between large-scale graph training and contrastive learning, we propose adaptive subgraph contrastive learning (AdaGCL). Given a batch of sampled subgraphs, we propose subgraph-granularity contrastive loss to compare the anchor node with a limited number of subgraphs, which reduces the computation cost. AdaGCL tailors two key components for batch training: (1) Batch-aware view generation to keep the intrinsic individual subgraph structures of batch to learn the informative node embeddings; (2) Batch-aware pair sampling to construct the positive and negative contrasting subgraphs based on anchor node label. Experiments show that AdaGCL can scale up to graphs with millions of nodes, and delivers the consistent improvement than the existing methods on various benchmark datasets. Furthermore, AdaGCL has comparable running time with the state-of-the-art contrastive learning methods that focus on improving efficiency. Finally, ablation studies of the two components of AdaGCL demonstrate their effectiveness to generalize the batch training. The code is in: https://github.com/YL-wang/CIKM_AdaGCL/.
Dynamic Hypergraph Learning for Collaborative Filtering
Authors: Chunyu Wei, Jian Liang, Bing Bai, Di Liu
Venue: CIKM
Year: 2022

Hypergraph-based collaborative filtering for recommendations has emerged as an important research topic due to its ability to model complex relations among users and items. However, most existing methods typically construct the hypergraph structures using heuristics (e.g., motifs and jump connections) based on existing graphs (e.g., user-item bipartite graphs and social networks). From a learning perspective, we argue that the fixed heuristic topology of hypergraph may become a limitation and thus potentially compromise the recommendation performance. To tackle this issue, we propose a novel dynamic hypergraph learning framework for collaborative filtering (DHLCF), which learns hypergraph structures and makes recommendations collectively in a unified framework. In the hypergraph learning process, we solve two main challenges, i.e., 1) optimization issue and 2) regularization issue. Firstly, we propose a differentiable hypergraph learner to adaptively learn the optimized hypergraph structures dynamically for the hypergraph convolutions during the training process. Secondly, to better regularize dynamic hypergraph learning, we introduce a novel hypergraph learning objective, which forces the learned hypergraphs to retain the original graph topology. Extensive experiments on public datasets from different domains are provided to show that our proposed model significantly outperforms strong baselines.
Dynamic Transfer Gaussian Process Regression
Authors: Pengfei Wei, Xinghua Qu, Wen Song, Zejun Ma
Venue: CIKM
Year: 2022

In this paper, we work on a challenging dynamic transfer regression problem where domains come in a streaming manner. At each time stage, a new domain emerges and is taken as the target domain while all the domains in previous time stages are taken as source domains. We propose a transfer Gaussian process model GPdk with a novel dynamic transfer kernel DyTK to handle the dynamic transfer regression problem. Specifically, DyTK is with a sequential form to fit the domain stream. To adaptively control the knowledge transfer strength, DyTK is designed to be capable of modeling the inter-domain relatedness of every inter-domain pair. A theorem that ensures DyTK to be positive semi-definite is then proposed. We also theoretically analyze the transfer performance of GPdk by deriving its generalization error bounds. The error bounds further motivate us to propose a parameter reuse strategy to alleviate the scalability issue of GPdk along time. Extensive experiments on both synthetic and real-world datasets show the effectiveness of GPdk in handling dynamic transfer regression problems.
RelpNet: Relation-based Link Prediction Neural Network
Authors: Ensen Wu, Hongyan Cui, Zunming Chen
Venue: CIKM
Year: 2022

Node-based link prediction methods have occupied a dominant position in the graph link prediction task. These methods commonly aggregate node features from the subgraph to generate the potential link representation. However, in constructing subgraphs, these methods extract each node's local neighborhood from the target node pair separately without considering the correlation between them and the whole node pair. As a result, many nodes in the subgraph may have little contribution to predicting the potential edge. Aggregating these node features will reduce the model's accuracy and efficiency. In addition, these methods indirectly represent the potential link by the node embeddings in the subgraph. We argue that this formalism is not the best choice for link prediction. In this paper, we propose a relation-based link prediction neural network named RelpNet, which aggregates edge features along the structural interactions between two target nodes and directly represents their relationship. RelpNet first extracts paths between the target node pair as structural interactions, which have strong correlations with the whole node pair and fewer nodes and edges than node-based methods' subgraph. To aggregate edge embeddings along the links between edges, we propose transforming the paths into a line graph. Then, the Tree-LSTM model is adopted to transfer and aggregate the node embeddings in the line graph as a comprehensive representation of the target node pair. We evaluate RelpNet on 7 benchmark datasets against 15 popular and state-of-the-art approaches, and the results demonstrate its significant superiority and high training efficiency.
FinBot: A Memory-Augmented Intelligent Financial Assistant
Authors: Yingting Wu, Bingzhu Du, Yongliang Wang, Zihao Wang, Minghui Yang, Yuchi Zhang, Hai Zhao
Venue: CIKM
Year: 2022

Task-oriented chatbots have been drawing a major demand in industry. In this demo, we present FinBot, a Chinese intelligent financial assistant, which is designed to help customers solve various financial investment problems with good expertise. Our FinBot may automatically understand user queries and respond with proper information accordingly in a dialogue session. To solve the difficulties of modeling financial dialogue histories, we adopt a multi-task model jointly capturing the intent and slots, in which the memory from previous dialogue turns and external user behaviors are exploited to further promote the performance of the dialogue response for better interpretability and user experience. The presented FinBot has been deployed in a mobile application that serves hundreds of millions of users.
Efficiently Answering Minimum Reachable Label Set Queries in Edge-Labeled Graphs
Authors: Yanping Wu, Renjie Sun, Chen Chen, Xiaoyang Wang, Xianming Fu
Venue: CIKM
Year: 2022

The reachability query is a fundamental problem in graph analysis. Recently, many studies focus on label-constraint reachability queries, which tries to verify whether two vertices are reachable under a given label set. However, in many real-life applications, it is more practical to find the minimum label set required to ensure the reachability of two vertices, which is neglected by previous research. To fill the gap, in this paper, we propose and investigate the minimum reachable label set (MRLS) problem in edge-labeled graphs. Specifically, given an edge-labeled graph and two vertices s, t, the MRLS problem aims to find a label set L with the minimum size such that s can reach t through L. We prove the hardness of our problem, and develop different optimization strategies to improve the scalability of the algorithms. Extensive experiments on 6 datasets demonstrate the advantages of the proposed algorithms.
Nonlinear Causal Discovery in Time Series
Authors: Tianhao Wu, Xingyu Wu, Xin Wang, Shikang Liu, Huanhuan Chen
Venue: CIKM
Year: 2022

Recent years have witnessed the proliferation of the Functional Causal Model (FCM) for causal learning due to its intuitive representation and accurate learning results. However, existing FCM-based algorithms suffer from the ubiquitous nonlinear relations in time-series data, mainly because these algorithms either assume linear relationships, or nonlinear relationships with additive noise, or do not introduce additional assumptions but can only identify nonlinear causality between two variables. This paper contributes in particular to a practical FCM-based causal learning approach, which can maintain effectiveness for real-world nonstationary data with general nonlinear relationships and unlimited variable scale.Specifically, the non-stationarity of time series data is first exploited with the nonlinear independent component analysis, to discover the underlying components or latent disturbances. Then, the conditional independence between variables and these components is studied to obtain a relation matrix, which guides the algorithm to recover the underlying causal graph. The correctness of the proposal is theoretically proved, and extensive experiments further verify its effectiveness. To the best of our knowledge, the proposal is the first so far that can fully identify causal relationships under general nonlinear conditions.
Contrastive Label Correlation Enhanced Unified Hashing Encoder for Cross-modal Retrieval
Authors: Hongfa Wu, Lisai Zhang, Qingcai Chen, Yimeng Deng, Joanna Siebert, Yunpeng Han, Zhonghua Li, Dejiang Kong, Zhao Cao
Venue: CIKM
Year: 2022

Cross-modal hashing (CMH) has been widely used in multimedia retrieval applications for its low storage cost and fast indexing speed. Thanks to the success of deep learning, cross-modal hashing has made significant progress with high-quality deep features. However, the modal gap is still a crucial bottleneck for existing cross-modal hashing methods: the commonly used convolutional neural network and bag-of-words encoders are customized for single modal prior, limiting the models to learn semantics representation in a cross-modal space. To overcome modality heterogeneity, we propose a shared transformer encoder (UniHash) to unify the cross-modal hashing into the same semantic space. A contrastive label correlation learning (CLC) loss using the category labels as modality bridge is designed together to improve the representation quality. Moreover, we take advantage of the multi-hot label space and propose a negative label generation (NegLG) strategy to get richer and uniformly distributed negative labels for contrast. Extensive experiments on three benchmarks verify the advantage of our proposed method. Besides, the proposed UniHash outperforms state-of-the-art cross-modal hashing methods significantly, establishing a new important baseline for the cross-modal hashing research. Codes are released github.com/idealwhite/Unihash.
RISE: A Velocity Control Framework with Minimal Impacts based on Reinforcement Learning
Authors: Yuyang Xia, Shuncheng Liu, Xu Chen, Zhi Xu, Kai Zheng, Han Su
Venue: CIKM
Year: 2022

Velocity control in autonomous driving is an emerging technology that has achieved rapid progress over the last decade. However, existing velocity control models are developed in single-lane scenarios and ignore the negative impacts caused by harsh velocity changes. In this work, we propose a velocity control framework based on reinforcement learning, called RISE (contRol velocIty for autonomouS vEhicle). In multi-lane circumstances, RISE improves velocity decisions regarding the autonomous vehicle itself, while minimizing impacts on rear vehicles. To achieve multiple objectives, we propose a hybrid reward function to rate each velocity decision from four aspects: safety, efficiency, comfort, and negative impact to guide the autonomous vehicle. Among these reward factors, the negative impact is used to penalize the harsh actions of the autonomous vehicle, thus prompting it to reduce the negative impacts on its rear vehicles. To detect the latent perturbations among surrounding vehicles in multiple lanes, we propose an attention-based encoder to learn the positions and interactions from an impact graph. Extensive experiments evidence that RISE enables safe driving, and outperforms state-of-the-art methods in efficiency, comfort, and alleviating negative impacts.
Balancing Utility and Exposure Fairness for Integrated Ranking with Reinforcement Learning
Authors: Wei Xia, Weiwen Liu, Yifan Liu, Ruiming Tang
Venue: CIKM
Year: 2022

Integrated ranking is critical in industrial recommendation systems and has attracted increasing attention. In an integrated ranking system, items from multiple channels are merged together and form an integrated list. During this process, apart from optimizing the system's utility like the total number of clicks, a fair allocation of the exposure opportunities over different channels also needs to be satisfied. To address this problem, we propose an integrated ranking model called Integrated Deep-Q Network (iDQN), which jointly considers user preferences, the platform's utility, and the exposure fairness. Extensive offline experiments validate the effectiveness of iDQN in managing the tradeoff between utility and fairness. Moreover, iDQN also has been deployed onto the online AppStore platform in Huawei, where the online A/B test shows iDQN outperforms the baseline by 1.87% and 2.21% in terms of utility and fairness, respectively.
MARINA: An MLP-Attention Model for Multivariate Time-Series Analysis
Authors: Jiandong Xie, Yue Cui, Feiteng Huang, Chao Liu, Kai Zheng
Venue: CIKM
Year: 2022

The proliferation of real-time monitoring applications such as Artificial Intelligence for IT Operations (AIOps) and the Internet of Things (IoT) has led to the generation of a vast amount of time-series data. To extract the underlying value of the data, both the industry and the academia are in dire need of efficient and effective methods for time-series analysis. To this end, in this paper, we propose a Multi-layer perceptron (MLP)-attention based multivariate time-series analysis model MARINA. MARINA is designed to simultaneously learn the temporal and spatial correlations among multivariate time-series. Also, the model is versatile in that it is suitable for major time-series analysis tasks such as forecasting and anomaly detection. Through extensive comparisons with the representative multivariate time-series forecasting and anomaly detection algorithms, MARINA is shown to achieve state-of-the-art (SOTA) performance in both forecasting and anomaly detection tasks.
Favorite+: Favorite Tuples Extraction via Regret Minimization
Authors: Min Xie, Yang Liu
Venue: CIKM
Year: 2022

When faced with a database containing millions of tuples, a user might be only interested in some of them. In this paper, we study how to help an end user to find the favorite tuples based on the recent advancements in regret minimization queries, which guarantees the tuples returned are not far from the user's favorite tuple in the database, without asking the user to scan the entire database. We consider three types of regret minimization queries: (1) End-to-end query: Given an output size k, we directly return a subset of at most k tuples from the database; (2) Interactive query: We identify the user's favorite tuple via user interaction, where a user might be presented with a few pairs of tuples, and the user is asked to indicate the one s/he favors more from each pair; and (3) Incremental query: Analogous to how we use search engines, if the user is not satisfied with the current tuples, we continually return more. We developed a demonstration system, called Favorite+, by supporting the above queries. We demonstrate that the system could help the users to find their favorite tuples in the database efficiently and effectively.
Multi-granularity Fatigue in Recommendation
Authors: Ruobing Xie, Cheng Ling, Shaoliang Zhang, Feng Xia, Leyu Lin
Venue: CIKM
Year: 2022

Personalized recommendation aims to provide appropriate items according to user preferences mainly from their behaviors. Excessive homogeneous user behaviors on similar items will lead to fatigue, which may decrease user activeness and degrade user experience. However, existing models seldom consider user fatigue in recommender systems. In this work, we propose a novel multi-granularity fatigue, modeling user fatigue from coarse to fine. Specifically, we focus on the recommendation feed scenario, where the underexplored global session fatigue and coarse-grained taxonomy fatigue have large impacts. We conduct extensive analyses to demonstrate the characteristics and influence of different types of fatigues in real-world recommender systems. In experiments, we verify the effectiveness of multi-granularity fatigue in both offline and online evaluations. Currently, the fatigue-enhanced model has also been deployed on a widely-used recommendation system of WeChat.