OpenRaiser/Intern-Atlas huggingface dataset:
Fast Graph Convolution Network Based Multi-label Image Recognition via Cross-modal Fusion
Authors: Yangtao Wang, Yanzhao Xie, Yu Liu, Ke Zhou, Xiaocui Li
Venue: CIKM
Year: 2020

In multi-label image recognition, it has become a popular method to predict those labels that co-occur in an image via modeling the label dependencies. Previous works focus on capturing the correlation between labels, but neglect to effectively fuse the image features and label embeddings, which severely affects the convergence efficiency of the model and inhibits the further precision improvement of multi-label image recognition. To overcome this shortcoming, in this paper, we introduce Multi-modal Factorized Bilinear pooling (MFB) which works as an efficient component to fuse cross-modal embeddings and propose F-GCN, a fast graph convolution network (GCN) based multi-label image recognition model. F-GCN consists of three key modules: (1) an image representation learning module which adopts a convolution neural network (CNN) to learn and generate image representations, (2) a label co-occurrence embedding module which first obtains the label vectors via the word embeddings technique and then adopts GCN to capture label co-occurrence embeddings and (3) an MFB fusion module which efficiently fuses these cross-modal vectors to enable an end-to-end model with a multi-label loss function. We conduct extensive experiments on two multi-label datasets including MS-COCO and VOC2007. Experimental results demonstrate the MFB component efficiently fuses image representations and label co-occurrence embeddings and thus greatly improves the convergence efficiency of the model. In addition, the performance of image recognition has also been promoted compared with the state-of-the-art methods.
Mining Infrequent High-Quality Phrases from Domain-Specific Corpora
Authors: Li Wang, Wei Zhu, Sihang Jiang, Sheng Zhang, Keqiang Wang, Yuan Ni, Guotong Xie, Yanghua Xiao
Venue: CIKM
Year: 2020

Phrase mining is a fundamental task for text analysis and has various downstream applications such as named entity recognition, topic modeling, and relation extraction. In this paper, we focus on mining high-quality phrases from domain-specific corpora with special consideration of infrequent ones. Previous methods might miss infrequent high-quality phrases in the candidate selection stage. And these methods rely on explicit features to mine phrases while rarely considering the implicit features. In addition, completeness is rarely explicitly considered in the evaluation of a high-quality phrase. In this paper, we propose a novel approach that exploits a sequence labeling model to capture infrequent phrases. And we employ implicit semantic features and contextual POS tag statistics to measure meaningfulness and completeness, respectively. Experiments over four real-world corpora demonstrate that our method achieves significant improvements over previous state-of-the-art methods across different domains and languages.
Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation
Authors: Bei Wang, Chenrui Zhang, Hao Zhang, Xiaoqing Lyu, Zhi Tang
Venue: CIKM
Year: 2020

Cold-start is a long-standing and challenging problem in recommendation systems. To tackle this issue, many cross-domain recommendation approaches are proposed. However, most of them follow a two-stage embedding-and-mapping paradigm, which is hard to be optimized. Besides, they ignore the structure information of the user-item interaction graph, resulting in that the embedding is insufficient to capture the latent collaborative filtering effect. In this paper, we propose a Dual Autoencoder Network (DAN), which implements cross-domain recommendations to cold-start users in an end-to-end manner. The graph convolutional network (GCN) based encoder in DAN explicitly captures high-order collaborative information in user-item interaction graphs. The two-branched decoder is proposed for fully exploiting the data across domains, and therefore the elaborate reconstruction constraints are obtained under a domain swapping strategy. Experiments on two pairs of real-world cross-domain datasets demonstrate that DAN outperforms existing state-of-the-art methods.
Succinct Adaptive Manifold Transfer
Authors: Pengfei Wei, Yiping Ke, Zhiqiang Xu, Tze-Yun Leong
Venue: CIKM
Year: 2020

Capturing the relatedness of different domains is a key challenge in transferring knowledge across domains. In this paper, we propose an effective and efficient Gaussian process (GP) modelling framework, mTGPmk, that can explicitly model domain relatedness and adaptively control the space as well as the strength of knowledge transfer. mTGPmk takes both the discrepancy of input feature space and the discrepancy of predictive function into account in the transfer procedure. Specifically, mTGPmk adaptively selects a good latent manifold shared by different domains, and utilizes a parametric similarity coefficient to measure the predictive function covariance of different domains in this manifold. The latent shared manifold and the similarity coefficient are jointly learned in a coupled manner. By doing so, mTGPmk maximizes the strength of the shared knowledge transfer by choosing the transfer space with the best transfer capacity. More importantly, mTGPmk exploits a succinct and computationally efficient manifold learning approach so that it can be well trained with scarce target training data. Extensive experimental studies using 36 synthetic transfer tasks and 10 real-world transfer tasks show the effectiveness of mTGPmk on capturing the relatedness and the transfer adaptiveness.
Active Hazard Observation via Human in the Loop Social Media Analytics System
Authors: Zhenyu Wen, Jedsada Phengsuwan, Nipun Balan Thekkummal, Rui Sun, Pooja jamathi-Chidananda, Tejal Shah, Philip James, Rajiv Ranjan
Venue: CIKM
Year: 2020

We demonstrate AHOM, a system that can Actively Observe Hazards via Monitoring Social Media Streams. AHOM proposes an active way to include the human in the loop of hazard information ac-quisition for social media. Different from state of the art, it supports bi-directional interaction between social media data processing system and social media users, which leads to the establishment of deeper and more accurate situational awareness of hazard events. We demonstrate how AHOM utilizes Twitter streams and bi-directional information exchange with social media users for enhanced hazard observation.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
Authors: Tao Wu, Ellie Ka In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John R. Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao
Venue: CIKM
Year: 2020

Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query, item) combinations, often referred to as the cold start problem. Furthermore, the search system can be biased towards items that are frequently shown to a query previously, also known as the 'rich get richer' (a.k.a. feedback loop) problem. In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation. In this paper, we propose a new Zero-Shot Heterogeneous Transfer Learning framework that transfers learned knowledge from the recommender system component to improve the search component of a content platform. First, it learns representations of items and their natural-language features by predicting (item, item) correlation graphs derived from the recommender system as an auxiliary task. Then, the learned representations are transferred to solve the target search retrieval task, performing query-to-item prediction without having seen any (query, item) pairs in training. We conduct online and offline experiments on one of the world's largest search and recommender systems from Google, and present the results and lessons learned. We demonstrate that the proposed approach can achieve high performance on offline search retrieval tasks, and more importantly, achieved significant improvements on relevance and user interactions over the highly-optimized production system in online experiments.
UI-FAME: A High-Performance Forgetting System for Creating Views of Ontologies
Authors: Xuan Wu, Wenxing Deng, Chang Lu, Hao Feng, Yizheng Zhao
Venue: CIKM
Year: 2020

This paper describes a Java-based forgetting system UI-FAME for creating views of ontologies. In relational databases, a view is a subset of the database, while in ontologies, a view is more than a subset; it contains not only axioms that are contained in the original ontology, but also newly-derived axioms that are entailed by the original ontology. Forgetting is a form of non-standard reasoning that can be used to create views of ontologies by eliminating from the ontologies a set of concept and role names, namely the forgetting signature, while keeping all logical consequences over the names in the remaining signature. We compared UI-FAME with publicly accessible forgetting systems, namely LETHE and an early prototypical version of UI-FAME, over the 'AL'-TBox fragment of 494 ontologies taken from the Oxford Ontology Library. The results showed that UI-FAME had better success rates than LETHE and its prototypical version, and outsped LETHE by a large margin. UI-FAME has been integrated as back-end support in Babylon Health's (a leading digital healthcare company based in London) knowledge base (formed by the merger and alignment of medical ontologies from different sources) interfaces for their main concerns of ontology analysis and keeping track of the changes in different versions of its knowledge base.
Deep Behavior Tracing with Multi-level Temporality Preserved Embedding
Authors: Runze Wu, Hao Deng, Jianrong Tao, Changjie Fan, Qi Liu, Liang Chen
Venue: CIKM
Year: 2020

Behavior tracing or predicting is a key component in various application scenarios like online user modeling and ubiquitous computing, which significantly benefits the system design (e.g., resource pre-caching) and improves the user experience (e.g., personalized recommendation). Traditional behavior tracing methods like Markovian and sequential models take recent behaviors as input and infer the next move by using the most real-time information. However, these existing methods rarely comprehensively model the low-level temporal irregularity in the recent behavior sequence, i.e., the unevenly distributed time intervals between consecutive behaviors, and the high-level periodicity in the long-term activity cycle, i.e., the periodic behavior patterns of each user. In this paper, we propose an intuitive and effective embedding method called Multi-level Aligned Temporal Embedding (MATE), which can tackle the temporal irregularity of recent behavior sequence and then align with the long-term periodicity in the activity cycle. Specifically, we combine time encoding and decoupled attention mechanism to build a temporal self-attentive sequential decoder to address the behavior-level temporal irregularity. To embed the activity cycle from the raw behavior sequence, we employ a novel temporal dense interpolation followed by a self-attentive sequential encoder. Then we first propose the periodic activity alignment to capture the long-term activity-level periodicity and construct the activity-behavior alignment to combine the activity-level with behavior-level representation to make the final prediction. We experimentally prove the effectiveness of the proposed model on a game player behavior sequence dataset and a real-world App usage trace dataset. Further, we deploy the proposed behavior tracing model into a game scene preloading service which can effectively reduce the waiting time of scene transfer by preloading the predicted game scene for each user.
A Joint Inverse Reinforcement Learning and Deep Learning Model for Drivers' Behavioral Prediction
Authors: Guojun Wu, Yanhua Li, Shikai Luo, Ge Song, Qichao Wang, Jing He, Jieping Ye, Xiaohu Qie, Hongtu Zhu
Venue: CIKM
Year: 2020

Users' behavioral predictions are crucially important for many domains including major e-commerce companies, ride-hailing platforms, social networking, and education. The success of such prediction strongly depends on the development of representation learning that can effectively model the dynamic evolution of user's behavior. This paper aims to develop a joint framework of combining inverse reinforcement learning (IRL) with deep learning (DL) regression model, called IRL-DL, to predict drivers' future behavior in ride-hailing platforms. Specifically, we formulate the dynamic evolution of each driver as a sequential decision-making problem and then employ IRL as representation learning to learn the preference vector of each driver. Then, we integrate drivers' preference vector with their static features (e.g., age, gender) and other attributes to build a regression model (e.g., LTSM-neural network) to predict drivers' future behavior. We use an extensive driver data set obtained from a ride-sharing platform to verify the effectiveness and efficiency of our IRL-DL framework, and results show that our IRL-DL framework can achieve consistent and remarkable improvements over models without drivers' preference vectors.
Maximum Signed (k, r)-Truss Identification in Signed Networks
Authors: Yanping Wu, Renjie Sun, Chen Chen, Xiaoyang Wang, Qiuyu Zhu
Venue: CIKM
Year: 2020

Mining cohesive subgraphs is a fundamental problem in social network analysis. The k-truss model has been widely used to measure the cohesiveness of subgraphs. Most existing studies about k-truss focus on unsigned graphs. However, in real applications, the edges in the networks can be either positive or negative, e.g., friend or foe relationships, which represents more information than unsigned networks. Therefore, the traditional k-truss model is not applicable for the signed networks. Motivated by this, in this paper, we propose a novel model, named signed (k,r)-truss, which leverages the property of balanced triangle in singed network analysis. Specifically, a signed (k,r)-truss is a subgraph where each edge has no less than k balanced support and no more than r unbalanced support. We prove that the problem of identifying the maximum signed (k,r)-truss is NP-hard. Due to the hardness of the problem, we tend to the heuristic strategies. A trivial algorithm is first presented. Then, two greedy algorithms are developed to enhance the processing. Finally, we conduct comprehensive experiments on real-world signed networks to verify the performance of proposed techniques.
Relevance Ranking for Real-Time Tweet Search
Authors: Yan Xia, Yu Sun, Tian Wang, Juan Caicedo Carvajal, Jinliang Fan, Bhargav Mangipudi, Lisa Huang, Yatharth Saraf
Venue: CIKM
Year: 2020

Relevance ranking is a key component of many search engines, including the Tweet search engine at Twitter. Users often use Tweet search to discover live discussions and different voices on trending topics or recent events. Tweet search is thus unique due to its focus on real-time content, where both the retrieved content and queries change drastically on an hourly basis. Another important property of Tweet search is that its relevance ranking takes the social endorsements from other users into account, e.g., "likes" and "retweets", which is different from mainly relying on clicks as implicit feedback. The relevance ranking of Tweet search is also subject to strict latency constraints, because every second, a large amount of Tweets are posted and indexed, while tens of thousands of queries are issued to search posted Tweets. Considering the above properties and constraints, we present a relevance ranking system for Tweet search addressing all these challenges at Twitter. We first discuss the formation of the relevance ranking pipeline, which consists of a series of ranking models. We then present the methodology for training the models and the various groups of features we use, including real-time and personalized features. We also investigate approaches of achieving unbiased model training and building up automatic online tuning of system parameters. Experiments using online A/B testing demonstrate the effectiveness of the proposed approaches and we have deployed the proposed relevance ranking system in production for more than three years.
OPHiForest: Order Preserving Hashing Based Isolation Forest for Robust and Scalable Anomaly Detection
Authors: Haolong Xiang, Zoran Salcic, Wanchun Dou, Xiaolong Xu, Lianyong Qi, Xuyun Zhang
Venue: CIKM
Year: 2020

Anomaly detection is one of the most important data mining tasks in many real-life applications such as network intrusion detection for cybersecurity and medical diagnosis for healthcare. In the big data era, these applications demand fast and versatile anomaly detection capability to handle various types of increasingly huge-volume data. However, existing detection methods are either slow due to high computational complexity, or unable to deal with complicated anomalies like local anomalies. In this paper, we propose a novel anomaly detection method named OPHiForest with the use of the order preserving hashing based isolation forest. The core idea is to learn the information from data to construct better isolation forest structure than the state-of-the-art methods like iForest and LSHiForest, which can achieve robust detection of various anomaly types. We design a fast two-step learning process for the order preserving hashing scheme. This leads to stronger order preservation for better hashing, and therefore enhances anomaly detection robustness and accuracy. Extensive experiments on both synthetic and real-world data sets demonstrate that our method is highly robust and scalable.
Generating Full Spatiotemporal Vehicular Paths: A Data Fusion Approach
Authors: Nan Xiao, Nan Hu, Liang Yu, Cheng Long
Venue: CIKM
Year: 2020

Vehicular path flow (trajectories) is an important data source for smart mobility, from which many road traffic parameters can be inferred. However, it has been a long-existing challenge that single source of trajectory data is biased in terms of its spatiotemporal coverage. In this paper, we leverage two types of large traffic datasets - point flows and sample trajectories - to generate the full city-scale vehicular paths. Our method consists of a low-granularity data fusion (LGDF) module, which uses point flow data to estimate the sparse paths that pass through some specific links (where sensors are mounted), and a high-granularity model training (HGMT) component, which uses sample trajectory data to pre-train a bi-gram sequence generation model. Afterwards, the results from LGDF and HGMT are combined to produce detailed on-road spatiotemporal paths. In this way, the data safety of single trajectory is protected while the full-scale city traffic can be reproduced for transportation analytics. The proposed method is verified via real-data case studies. As a result, starting from August 2019, this method has been implemented in Alibaba's city brain project and successively deployed in many cities in China for the purpose of traffic analysis and optimization.
Deep Multi-Interest Network for Click-through Rate Prediction
Authors: Zhibo Xiao, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, Hao Wang
Venue: CIKM
Year: 2020

Click-through rate prediction plays an important role in many fields, such as recommender and advertising systems. It is one of the crucial parts to improve user experience and increase industry revenue. Recently, several deep learning-based models are successfully applied to this area. Some existing studies further model user representation based on user historical behavior sequence, in order to capture dynamic and evolving interests. We observe that users usually have multiple interests at a time and the latent dominant interest is expressed by the behavior. The switch of latent dominant interest results in the behavior changes. Thus, modeling and tracking latent multiple interests would be beneficial. In this paper, we propose a novel method named as Deep Multi-Interest Network (DMIN) which models user's latent multiple interests for click-through rate prediction task. Specifically, we design a Behavior Refiner Layer using multi-head self-attention to capture better user historical item representations. Then the Multi-Interest Extractor Layer is applied to extract multiple user interests. We evaluate our method on three real-world datasets. Experimental results show that the proposed DMIN outperforms various state-of-the-art baselines in terms of click-through rate prediction task.
Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction
Authors: Qinge Xie, Tiancheng Guo, Yang Chen, Yu Xiao, Xin Wang, Ben Y. Zhao
Venue: CIKM
Year: 2020

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks.
InterNet: Multistep Traffic Forecasting by Interacting Spatial and Temporal Features
Authors: Yilian Xin, Dezhuang Miao, Mengxia Zhu, Cheqing Jin, Xuesong Lu
Venue: CIKM
Year: 2020

Traffic forecasting on the entire road network is challenging due to the non-linear temporal dynamics and complex spatial correlations. Multi-step traffic forecasting further increases the difficulty because of the accumulated prediction errors. Existing forecasting models attempt to extract both spatial and temporal features of all locations on the road network for prediction, but often overlook the interaction between the two types of features, which has led to sub-optimal performance. In this work, we tackle this problem by proposing InterNet, which applies the multi-head attention mechanism on the extracted spatio-temporal features and enables the interaction of the spatial (temporal) features of one location with the temporal (spatial) features of all locations. Moreover, we extract the features of all locations using a graph convolutional layer and a bidirectional LSTM layer, before feeding them into the multi-head attention layer. The three layers are seamlessly integrated and thereby enable end-to-end learning. Experimental results show that the InterNet model outperforms the state-of-the-art models in terms of the prediction accuracy, which demonstrates the potential of such interactions.