OpenRaiser/Intern-Atlas huggingface dataset:
Smarter and Safer Traffic Signal Controlling via Deep Reinforcement Learning
Authors: Bingquan Yu, Jinqiu Guo, Qinpei Zhao, Jiangfeng Li, Weixiong Rao
Venue: CIKM
Year: 2020

Recently deep reinforcement learning (DRL) has been used for intelligent traffic light control. Unfortunately, we find that state-of-the-art on DRL-based intelligent traffic light essentially adopts discrete decision making and would suffer from the issue of unsafe driving. Moreover, existing feature representation of environment may not capture dynamics of traffic flow and thus cannot precisely predict future traffic flows. To overcome these issues, in this paper, we propose a DDPG-based DRL framework to learn a continuous time duration of traffic signal phases by introducing 1) a transit phase before the change of current phase for better safety, and 2) vehicle moving speed into feature representation for more precise estimation of traffic flow in next phase. Our preliminary evaluation on a well-known simulator SUMO indicates that our work significantly outperforms a recent work by much smaller number of emergency stops, queue length and waiting time.
How the Quantum-inspired Framework Supports Keyword Searches on Multi-model Databases
Authors: Gongsheng Yuan
Venue: CIKM
Year: 2020

With the trend of increasing vendors to develop various multi-model databases, people have reaped benefits from using a single and unified platform to manage both well-structured and NoSQL data. However, it causes a steep learning curve of mastering a multi-model query language for the specific multi-model database, not to mention various languages for different databases. Therefore, this research discusses the motivations of performing keyword searches on multi-model databases and then presents our current research. Methodologically, we attempt to use the quantum-inspired framework to query and explore multi-model databases. Firstly, we apply non-classical probabilities to estimate the relevance between a keyword query and candidate answers for guaranteeing getting good accuracy. Then we use the Principle Component Analysis (PCA) method to optimize the quantum language model for capturing good scalability. Finally, experiments show that our approaches are effective and our framework outperforms the state-of-the-art approaches.
Exploring Missing Interactions: A Convolutional Generative Adversarial Network for Collaborative Filtering
Authors: Feng Yuan, Lina Yao, Boualem Benatallah
Venue: CIKM
Year: 2020

Adversarial examples can be detrimental to a recommender,leading to a surging enthusiasm for applying adversarial learning to improve recommendation performance, e.g. raising model robustness, alleviating data sparsity, generating initial profiles for cold-start users or items, etc. Most existing adversarial example generation methods fall within three categories: attacking the user-item interactions or auxiliary contents, adding perturbations in latent space, sampling the latent space according to certain distribution. In this work, we focus on the semantic-rich user-item interactions in a recommender system and propose a novel generative adversarial network (GAN) named Convolutional Generative Collaborative Filtering (Conv-GCF). We develop an effective perturbation mechanism (adversarial noise layer) for convolutional neural networks (CNN), based on which we design a generator with residual blocks to synthesize user-item interactions. We empirically demonstrate that on Conv-GCF, the adversarial noise layer is superior to the conventional noise-adding approach. Moreover, we propose two types of discriminators: one using Bayes Personalized Ranking (BPR) and the other with binary classification. On four public datasets, we show that our approach achieves the state-of-the-art top-n recommendation performance among competitive baselines.
Logic Enhanced Commonsense Inference with Chain Transformer
Authors: Chenxi Yuan, Chun Yuan, Yang Bai, Ziran Li
Venue: CIKM
Year: 2020

We study the commonsense inference task that aims to reason and generate the causes and effects of a given event. Existing neural methods focus more on understanding and representing the event itself, but pay little attention to the relations between different commonsense dimensions (e.g. causes or effects) of the event, making the generated results logically inconsistent and unreasonable. To alleviate this issue, we propose Chain Transformer, a logic enhanced commonsense inference model that combines both direct and indirect inferences to construct a logical chain so as to reason in a more logically consistent way. First, we apply a self-attention based encoder to represent and encode the given event. Then a chain of decoders is implemented to reason and generate for different dimensions following the logical chain, where an attention module is designed to link different decoders and to make each decoder attend to the previous reasoned inferences. Experiments on two real-world datasets show that Chain Transformer outperforms previous methods on both automatic and human evaluation, and demonstrate that Chain Transformer can generate more reasonable and logically consistent inference results.
MIMICS: A Large-Scale Data Collection for Search Clarification
Authors: Hamed Zamani, Gord Lueck, Everest Chen, Rodolfo Quispe, Flint Luu, Nick Craswell
Venue: CIKM
Year: 2020

Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still feels the lack of a large-scale dataset for studying different aspects of search clarification. In this paper, we introduce MIMICS, a collection of search clarification datasets for real web search queries sampled from the Bing query logs. Each clarification in MIMICS is generated by a Bing production algorithm and consists of a clarifying question and up to five candidate answers. MIMICS contains three datasets: (1) MIMICS-Click includes over 400k unique queries, their associated clarification panes, and the corresponding aggregated user interaction signals (i.e., clicks). (2) MIMICS-ClickExplore is an exploration data that includes aggregated user interaction signals for over 60k unique queries, each with multiple clarification panes. (3) MIMICS-Manual includes over 2k unique real search queries. Each query-clarification pair in this dataset has been manually labeled by at least three trained annotators. It contains graded quality labels for the clarifying question, the candidate answer set, and the landing result page for each candidate answer. MIMICS is publicly available for research purposes, thus enables researchers to study a number of tasks related to search clarification, including clarification generation and selection, user engagement prediction for clarification, click models for clarification, and analyzing user interactions with search clarification. We also release the results returned by the Bing's web search API for all the queries in MIMICS. This would allow researchers to utilize search results for the tasks related to search clarification.
Mining User Interests from Social Media
Authors: Fattane Zarrinkalam, Guangyuan Piao, Stefano Faralli, Ebrahim Bagheri
Venue: CIKM
Year: 2020

Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work.
Sample Driven Data Mapping for Linked Data and Web APIs
Authors: Tobias Zeimetz, Ralf Schenkel
Venue: CIKM
Year: 2020

In order to create the most comprehensive RDF Knowledge Base possible, data integration is essential. Many different data sources are used to extend a given dataset or to correct errors in the data. Nowadays, Web APIs (instead of data dumps) are common external data sources, since many data providers make their data publicly available. However, the classic problems of data integration, i.e., which parts of the datasets can be mapped, remain. In addition, Web APIs are often more restrictive than data dumps and of course slower to access due to latencies and other constraints. In this paper we demonstrate the FiLiPo (Finding Linkage Points) system to automatically find connections (i.e., linkage points) between Web APIs and local Knowledge Bases in a reasonable amount of time. To this end, we developed a sample-driven schema matching system, which models Web API services as parameterized queries. These Web API services return a view definition of their data which subsequently need to be connected to the local database scheme. Furthermore, our approach is able to find valid input values for Web API services automatically (e.g. IDs) and can determine combined linkage points (e.g. first and last name) despite different structures. Our results on six real world API services with two local databases show that our linkage point detection algorithm performs well in terms of precision (0.89 up to 1.0) and recall (0.69 up to 1.0).
Leveraging User Email Actions to Improve Ad-Close Prediction
Authors: Oleg Zendel, Yaroslav Fyodorov, Fiana Raiber, Natalia Silberstein, Oren Somekh, Ali Tabaja
Venue: CIKM
Year: 2020

Online advertising systems often provide means for users to close ads and also leave feedback. Although closing ads requires additional user engagement and usually indicates a poor user experience, ad closes are not as scarce as one might expect. Recently it was shown that penalizing ads with high closing likelihood during auctions may substantially reduce the number of ad closes while maintaining a small predefined revenue loss. In this work, we focus on email since this is the property in which most ad closes occur. Using data collected from a major email provider, we present interesting insights about the interplay between ad closes in email and email-related user actions. In particular, we explore the merits of integrating information derived from user actions in email for ad-close prediction. Thorough performance evaluation reveals that incorporating such signals significantly improves ad-close prediction quality over previously reported results.
Event-Driven Network for Cross-Modal Retrieval
Authors: Zhixiong Zeng, Nan Xu, Wenji Mao
Venue: CIKM
Year: 2020

Despite extensive research on cross-modal retrieval, existing methods focus on the matching between image objects and text words. However, for the large amount of social media, such as news reports and online posts with images, previous methods are insufficient to model the associations between long text and image. As long text contains multiple entities and relationships between them, as well as complex events sharing a common scenario of the text, it poses unique research challenge to cross-modal retrieval. To tackle the challenge, in this paper, we focus on the retrieval task on long text and image, and propose an event-driven network for cross-modal retrieval. Our approach consists of two modules, namely the contextual neural tensor network (CNTN) and cross-modal matching network (CMMN). The CNTN module captures both event-level and text-level semantics of the sequential events extracted from a long text. The CMMN module learns a common representation space to compute the similarity of image and text modalities. We construct a multimodal dataset based on the news reports in People's Daily. The experimental results demonstrate that our model outperforms the existing state-of-the-art methods and can provide semantic richer text representations to enhance the effectiveness in cross-modal retrieval.
TOMATO: A Topic-Wise Multi-Task Sparsity Model
Authors: Jason (Jiasheng) Zhang, Dongwon Lee
Venue: CIKM
Year: 2020

The Multi-Task Learning (MTL) leverages the inter-relationship across tasks and is useful for applications with limited data. Existing works articulate different task relationship assumptions, whose validity is vital to successful multi-task training. We observe that, in many scenarios, the inter-relationship across tasks varies across different groups of data (i.e., topic), which we call within-topic task relationship hypothesis. In this case, current MTL models with homogeneous task relationship assumption cannot fully exploit different task relationships among different groups of data. Based on this observation, in this paper, we propose a generalized topic-wise multi-task architecture, to capture the within-topic task relationship, which can be combined with any existing MTL designs. Further, we propose a new specialized MTL design, topic-task-sparsity, along with two different types of sparsity constraints. The architecture, combined with the topic-task-sparsity design, constructs our proposed TOMATO model. The experiments on both synthetic and 4 real-world datasets show that our proposed models consistently outperform 6 state-of-the-art models and 2 baselines with improvement from $5%$ to $46%$ in terms of task-wise comparison, demonstrating the validity of the proposed within-topic task relationship hypothesis. We release the source codes and datasets of TOMATO at: https://github.com/JasonLC506/MTSEM.
Seasonal-Periodic Subgraph Mining in Temporal Networks
Authors: Qianzhen Zhang, Deke Guo, Xiang Zhao, Xinyi Li, Xi Wang
Venue: CIKM
Year: 2020

\emphSeasonal periodicity is a frequent phenomenon for social interactions in temporal networks. A key property of this behavior is that it exhibits periodicity for multiple particular periods in temporal networks. Mining such seasonal-periodic patterns is significant since it can indicate interesting relationships between the individuals involved in the interactions. Unfortunately, most previous studies for periodic pattern mining ignore the seasonal feature. This motivates us to explore mining seasonal-periodic subgraphs, and the investigation presents a novel model, called maximal σ-periodic $ømega$-seasonal k-subgraph. It represents a subgraph with size larger than k and that appears at least σ times periodically in at least $ømega$ particular periods on the temporal graph. Since seasonal-periodic patterns do not satisfy the anti-monotonic property, we propose a weak version of support measure with an anti-monotonic property to reduce the search space efficiently. Then, we present an effective mining algorithm to seek all maximal σ-periodic $ømega$-seasonal k-subgraphs. Experimental results on real-life datasets show the effectiveness and efficiency of our approach.
More Than One: A Cluster-Prototype Matching Framework for Zero-Shot Learning
Authors: Jing Zhang, Yangli-ao Geng, Qingyong Li, Chuan Shi
Venue: CIKM
Year: 2020

Zero-shot learning (ZSL) aims to recognize unseen categories whose data is unavailable during the training stage. Most existing ZSL algorithms focus on learning an embedding space and determine the classes of test samples according to sample-prototype similarities in this space. However, we observe that, in contrast to the single sample-prototype relationship, an ensemble criterion usually benefits the final classification, just as the saying "more than one". Inspired by this, we introduce a novel cluster-prototype matching (CPM) strategy and propose a ZSL framework based on CPM. Firstly, we learn a mapping between the visual space and the semantic space utilizing a well-established ZSL algorithm. Via the learned mapping, all test samples are projected into the embedding space and clustered in this space. Secondly, two CPM methods, soft-CPM and hard-CPM, are proposed to match clusters and class prototypes, along with cluster-prototype similarities calculated. Finally, the label of each sample is determined by the combination of the sample-prototype similarity and the cluster-prototype similarity. We apply our framework to five basic ZSL methods and compare them with several advanced baselines of ZSL. The experimental results demonstrate that the proposed framework can significantly improve the performance of the basic ZSL models and help them achieve or beyond the state-of-the-art.
Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting
Authors: Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia
Venue: CIKM
Year: 2020

Traffic flow prediction plays an important role in many spatial-temporal data applications, e.g., traffic management and urban planning. Various deep learning techniques are developed to model the traffic dynamic patterns with different neural network architectures, such as attention mechanism, recurrent neural network. However, two important challenges have yet to be well addressed: (i) Most of these methods solely focus on local spatial dependencies and ignore the global inter-region dependencies in terms of traffic distributions; (ii) It is important to capture channel-aware semantics when performing spatial-temporal information aggregation. To address these challenges, we propose a new traffic prediction framework--Spatial-Temporal Convolutional Graph Attention Network (ST-CGA), to enable the traffic prediction with the modeling of region dependencies, from locally to globally in a comprehensive manner. In our ST-CGA framework, we first develop a hierarchical attention networks with a graph-based neural architecture, to capture both the multi-level temporal relations and cross-region traffic dependencies. Furthermore, a region-wise spatial relation encoder is proposed to supercharge ST-CGA mapping spatial and temporal signals into different representation subspaces, with channel-aware recalibration residual network. Extensive experiments on four real-world datasets demonstrate that ST-CGA achieve substantial gains over many state-of-the-art baselines. Source codes are available at: https://github.com/shurexiyue/ST-CGA.
Multiplex Graph Neural Networks for Multi-behavior Recommendation
Authors: Weifeng Zhang, Jingwen Mao, Yi Cao, Congfu Xu
Venue: CIKM
Year: 2020

This paper focuses on the multi-behavior recommendation problem, i.e., generating personalized recommendation based on multiple types of user behaviors. Methods proposed recently usually leverage the ordinal assumption, which means that users? different types of behaviors should take place in a fixed order. However, this assumption may be too strong in some scenarios. In this paper, a more general model named Multiplex Graph Neural Network (MGNN) is proposed as a remedy. MGNN tackles the multi-behavior recommendation problem from a novel perspective, i.e., the perspective of link prediction in multiplex networks. By taking advantage of both the multiplex network structure and graph representation learning techniques, MGNN learns shared embeddings and behavior-specific embeddings for users and items to model the collective effect of multiple types of behaviors. Experiments conducted on both ordinal-behavior datasets and generic-behavior datasets demonstrate the effectiveness of the proposed MGNN model.
Network Alignment: Recent Advances and Future Directions
Authors: Si Zhang, Hanghang Tong
Venue: CIKM
Year: 2020

In the era of big data, networks are often from multiple sources such as the social networks of diverse platforms (e.g., Facebook, Twitter), protein-protein interaction (PPI) networks of different tissues, transaction networks at multiple financial institutes and knowledge graphs derived from a variety of knowledge bases (e.g., DBpedia, Freebase, etc.). The very first step before exploring insights from these multi-sourced networks is to integrate and unify different networks. In general, network alignment is such a task that aims to uncover the correspondences among nodes across different graphs. The challenges of network alignment include: (1) the heterogeneity of the multi-sourced networks, e.g., different structural patterns, (2) the variety of the real-world networks, e.g., how to leverage the rich contextual information, and (3) the computational complexity. The goal of this tutorial is to (1) provide a comprehensive overview of the recent advances in network alignment, and (2) identify the open challenges and future trends. We believe this can be beneficial to numerous application problems, and attract both researchers and practitioners from both data mining area and other interdisciplinary areas. In particular, we start with introducing the backgrounds, problem definition and key challenges of network alignment. Next, our emphases will be on (1) the recent techniques on addressing network alignment problem and other related problems with a careful balance between the algorithms and applications, and (2) the open challenges and future trends.
Elevated Road Network: A Metric Learning Method for Recognizing Whether a Vehicle is on an Elevated Road
Authors: Xiaobing Zhang, Hailiang Xu, Jian Yang, Jia Sun, Fan Chen, Leiyun Li
Venue: CIKM
Year: 2020

Mobile navigation is a critical component in mobile maps. Yawing detection (does a vehicle yaw) is an important task in mobile navigation. In regions containing parallel and close elevated and surface roads, it is hard to detect yawing events using traditional methods, which mainly rely on low-accuracy positions and moving directions. Recognizing whether a vehicle is moving on an elevated road can significantly improve the performance of yawing detection. We propose Elevated Road Network (ERNet), a lightweight and real industrial neural network model for mobile navigation, to solve elevated road recognition fundamentally. For an elevated road fragment and a surface road fragment in the same group (they are parallel and close), ERNet takes four types of high-level features as input and learns two 10-dim descriptors (A and B). In inference stage, ERNet predicts a 10-dim embedding (C) for a position of a vehicle. By comparing ||A-C||2 2 and ||B-C||22, and applying a technique called confidence constraint, we recognize the road type corresponding to the position. Significant improvements on elevated road recognition and yawing detection have been achieved compared with several methods in extensive experiments. ERNet is deployed as part of AMap, the famous mobile map in China, serves drivers in three large cities: Beijing, Shanghai and Guangzhou, and will cover the whole country as soon as possible.