OpenRaiser/Intern-Atlas huggingface dataset:
Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network
Authors: Mengmeng Li, Tian Gan, Meng Liu, Zhiyong Cheng, Jianhua Yin, Liqiang Nie
Venue: CIKM
Year: 2019

Hashtags, a user provides to a micro-video, are the ones which can well describe the semantics of the micro-video's content in his/her mind. At the same time, hashtags have been widely used to facilitate various micro-video retrieval scenarios (e.g., search, browse, and categorization). Despite their importance, numerous micro-videos lack hashtags or contain inaccurate or incomplete hashtags. In light of this, hashtag recommendation, which suggests a list of hashtags to a user when he/she wants to annotate a post, becomes a crucial research problem. However, little attention has been paid to micro-video hashtag recommendation, mainly due to the following three reasons: 1) lack of benchmark dataset; 2) the temporal and multi-modality characteristics of micro-videos; and 3) hashtag sparsity and long-tail distributions. In this paper, we recommend hashtags for micro-videos by presenting a novel multi-view representation interactive embedding model with graph-based information propagation. It is capable of boosting the performance of micro-videos hashtag recommendation by jointly considering the sequential feature learning, the video-user-hashtag interaction, and the hashtag correlations. Extensive experiments on a constructed dataset demonstrate our proposed method outperforms state-of-the-art baselines. As a side research contribution, we have released our dataset and codes to facilitate the research in this community.
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Authors: Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou
Venue: CIKM
Year: 2019

Anomaly detection in attributed networks (instance-to-instance dependencies and interactions are available) has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks. However, it remains a challenging task since the definition of anomaly becomes more complicated and topological structures are heterogeneous with nodal attributes. In this paper, we propose a spectral convolution and deconvolution based framework - SpecAE, to project the attributed network into a tailored space to detect global and community anomalies. SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority. The learned representations along with reconstruction errors are combined with a density estimation model to perform the detection. Experiments on real-world datasets demonstrate the effectiveness of the proposed SpecAE.
HAES: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity
Authors: Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang
Venue: CIKM
Year: 2019

Recommendation systems provide good guidance for users to find their favorite movies from an overwhelming amount of options. However, most systems excessively pursue the recommendation accuracy and give rise to over-specialization, which triggers the emergence of serendipity. Hence, serendipity recommendation has received more attention in recent years, facing three key challenges: subjectivity in the definition, the lack of data, and users' floating demands for serendipity. To address these challenges, we introduce a new model called HAES, a H ybrid A pproach for movie recommendation with E lastic S erendipity, to recommend serendipitous movies. Specifically, we (1) propose a more objective definition of serendipity, \em content difference and \em genre accuracy, according to the analysis on a real dataset, (2) propose a new algorithm named JohnsonMax to mitigate the data sparsity and build weak ties beneficial to finding serendipitous movies, and (3) define a novel concept of elasticity in the recommendation, to adjust the level of serendipity flexibly and reach a trade-off between accuracy and serendipity. Extensive experiments on real-world datasets show that HAES enhances the serendipity of recommendations while preserving recommendation quality, compared to several widely used methods.
How to Find It Better?: Cross-Learning for WeChat Mini Programs
Authors: He Li, Zhiqiang Liu, Sheng Xu, Zhiyuan Lin, Xiangqun Chen
Venue: CIKM
Year: 2019

WeChat Mini Program is a lightweight app relying on the WeChat client, which can be accessed directly from the search list without downloading and installing. Retrieval and ranking for the Mini Programs differ from traditional web search in two sides. On the one hand, as the search queries are often short and most Mini Programs contain few useful textual information, it is hard to retrieve when the user input is inaccurate. On the other hand, without user scoring and rating system like App Store and Google Play, it is hard to rank relatively better results in a more advanced position. In this paper, we propose a Cross-Learning strategy to improve the search experience, where the semantics of queries and Mini Programs are represented not by itself, but by each other. We treat the search task as an extreme multi-label classification problem where the queries are inputs and the Mini Programs are labels. We propose a N-Gram self-attention query encoder to capture the search intention behind these short queries, and carefully design the label selection strategy based on user behavior to rank higher quality Mini Programs in higher positions. Our model outperforms some state-of-the-art baselines in the offline environment, and brought improvement to our actual business in the online A/B Test, which proves the practical significance of our work.
On Novel Object Recognition: A Unified Framework for Discriminability and Adaptability
Authors: Kai Li, Martin Renqiang Min, Bing Bai, Yun Fu, Hans Peter Graf
Venue: CIKM
Year: 2019

The rich and accessible labeled data fueled the revolutionary successes of deep learning in object recognition. However, recognizing objects of novel classes with limited supervision information provided, i.e., Novel Object Recognition (NOR), remains a challenging task. We identify in this paper two key factors for the success of NOR that previous approaches fail to simultaneously guarantee. The first is producing discriminative feature representations for images of novel classes, and the second is generating a flexible classifier readily adapted to novel classes provided with limited supervision signals. To secure both key factors, we propose a framework which decouples a deep classification model into a feature extraction module and a classification module. We learn the former to ensure feature discriminability with a standard multi-class classification task by fully utilizing the competing information among all classes within a training set, and learn the latter to secure adaptability by training a meta-learner network which generates classifier weights whenever provided with minimal supervision information of target classes. Extensive experiments on common benchmark datasets in the settings of both zero-shot and few-shot learning demonstrate our method achieves state-of-the-art performance.
Domain Knowledge Guided Deep Atrial Fibrillation Classification and Its Visual Interpretation
Authors: Xiaoyu Li, Buyue Qian, Jishang Wei, Xianli Zhang, Sirui Chen, Qinghua Zheng
Venue: CIKM
Year: 2019

Hand-crafted features have been proven useful in solving the electrocardiograph~(ECG) classification problem. The features rely on domain knowledge and carry clinical meanings. However, the construction of the features requires tedious fine tuning in practice. Lately, a set of end-to-end deep neural network models have been proposed and show promising results in ECG classification. Though effective, such models learn patterns which usually mismatch human's concept, and thereby it is hard to get a convincing explanation with interpretation methods. This limitation significantly narrows the applicability of deep models, considering it is difficult for cardiologists to accept the unexplainable results from deep learning. To alleviate such limitation, we are bringing the best from the two worlds and propose a domain knowledge guided deep neural network. Specifically, we utilize a deep residual network as a classification framework, within which key feature ~(P-wave and R-peak position) reconstruction tasks are adopted to incorporate domain knowledge in the learning process. The reconstruction tasks make the model pay more attention to key feature points within ECG. Furthermore, we utilize occlusion method to get visual interpretation and design a visualization at both heartbeat level and feature point level. Our experiments show the superior performance of the proposed ECG classification methods compared to the model without P-wave and R-peak tasks, and the patterns learnt by our model is more explainable.
Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage
Authors: Chaozhuo Li, Senzhang Wang, Hao Wang, Yanbo Liang, Philip S. Yu, Zhoujun Li, Wei Wang
Venue: CIKM
Year: 2019

With the increasing popularity and diversity of social media, users tend to join multiple social platforms to enjoy different types of services. User identity linkage, which aims to link identical identities across different social platforms, has attracted increasing research attentions recently. Existing methods usually focus on pairwise identity linkage between two platforms, which cannot piece up the information from multi-sources to depict the intrinsic figures of social users. In this paper, we propose a novel adversarial learning based framework MSUIL with partially shared generators to perform Semi-supervised User Identity Linkage across Multiple social networks. The isomorphism across multiple platforms is captured as the complementary to link identities. The insight is that we aim to learn the desirable projection functions (generators) to not only minimize the distance between the distributions of user identities in arbitrary pairs of platforms, but also incorporate the available annotations as the learning guidance. The projection functions of different platform pairs share partial parameters, which ensures MSUIL can capture the interdependencies among multiple platforms and improves the model efficiency. Empirically, we evaluate our proposal over multiple datasets. The experimental results demonstrate the superiority of the proposed MSUIL model.
Heterogeneous Components Fusion Network for Load Forecasting of Charging Stations
Authors: Kai Li, Fei Yu, Cheng Feng, Tian Xia
Venue: CIKM
Year: 2019

Accurate load forecasting of charging stations enable managers to reduce the drivers' waiting time and operating costs. But the existing works for spatial-temporal sequence forecasting usually assume the spatial-continuity of signals. However, the recharging scenario, in which the above assumptions are not valid due to the sparse spatial distribution of stations, need further research. To fill the gap, we present a Heterogeneous Components Fusion Network to model dual components sourced from the planned and the unplanned recharging events independently. For planned recharging component, we design a customized transformer to 'looks up' the reference 'memory' for the prediction. And we propose the time-variant graph to model highly dynamic unplanned events. Experiments conducted on a load reading dataset of 120 stations suggest that our model achieves better performance than a series of state-of-the-arts for spatial-temporal sequence prediction problem.
Real-time Edge Repartitioning for Dynamic Graph
Authors: He Li, Hang Yuan, Jianbin Huang
Venue: CIKM
Year: 2019

To improve the performance of large graph computing, graph partitioning has become a mandatory step in distributed graph computing frameworks. Some existing frameworks partition edges of an input graph in a streaming way. As the scale of real-world graphs grows dynamically, they need to limit the increasing communication cost and time cost in graph computing by reducing vertex replicas(each vertex can be replicated to multiple partitions). In this paper, we propose a real-time edge repartitioning algorithm for dynamic graph, which reduces the vertex replicas by reassigning edges near the new edge. We find that some edges are migrated just after being assigned, which leads to unnecessary migrations. To reduce migration cost, according to the replicas distribution of neighbors of two vertices connected by the new edge, we assign the new edge to the partition where it is most likely to be located after repartitioning. Our evaluation shows that it improves the performance of graph computing by only a small amount of migration.
Online Kernel Selection via Tensor Sketching
Authors: Shizhong Liao, Xiao Zhang
Venue: CIKM
Year: 2019

Online kernel selection is a more complex problem compared with offline kernel selection, which intermixes training and selection at each round and requires a sublinear regret and low computational complexities. But existing online kernel selection approaches have at least linear time and space complexities at each round with respect to the number of rounds, or lack sublinear regret guarantees for an uncountably infinite number of candidate kernels. To address these issues, we propose a novel online kernel selection approach using tensor sketching, which has constant computational complexities at each round and enjoys a sublinear regret bound for an uncountably infinite number of candidate kernels. We represent the data using the tensor products and construct data sketches using the Taylor series and the Count Sketch matrices, which yields a sketched reproducing kernel Hilbert space (SRKHS). Then we update the optimal kernels and the hypotheses using online gradient descent in SRKHS. We prove that the kernel corresponding to SRKHS satisfies the reproducing property, the hypotheses in SRKHS are convex with respect to the kernel parameter, and the proposed online kernel selection approach in SRKHS enjoys a regret bound of order $O(\sqrtT )$ for an uncountably infinite number of candidate kernels, which is optimal for a convex loss function, where T is the number of rounds. By the fast Fourier transform, the hypotheses in SRKHS can be computed in a quasilinear time complexity and a logarithmic space complexity with respect to the sketch size at each round, where the sketch size is a constant. Experimental results demonstrate that our online kernel selection approach is more accurate and efficient for online kernel learning on high dimension data.
An Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals
Authors: Jionghao Lin, Shirui Pan, Cheng Siong Lee, Sharon L. Oviatt
Venue: CIKM
Year: 2019

Affective computing is an emerging research area which provides insights on human's mental state through human-machine interaction. During the interaction process, bio-signal analysis is essential to detect human affective changes. Currently, machine learning methods to analyse bio-signals are the state of the art to detect the affective states, but most empirical works mainly deploy traditional machine learning methods rather than deep learning models due to the need for explainability. In this paper, we propose a deep learning model to process multimodal-multisensory bio-signals for affect recognition. It supports batch training for different sampling rate signals at the same time, and our results show significant improvement compared to the state of the art. Furthermore, the results are interpreted at the sensor- and signal- level to improve the explainaibility of our deep learning model.
Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network
Authors: Xi Lin, Yequan Wang, Xiaokui Xiao, Zengxiang Li, Sourav S. Bhowmick
Venue: CIKM
Year: 2019

Estimation of path travel time provides great value to applications like bus line designs and route plannings. Existing approaches are mainly based on single-source trajectory datasets that are usually large in size to ensure a satisfactory performance. This leads to two limitations: 1) Large-scale data may not always be attainable, e.g. city-scale public bus data is usually small compared to taxi data due to relative fewer bus trips in a day. 2) Considering only single-source trajectory data neglects the potential estimation-improving insights of external data, e.g. trajectory dataset of other vehicle sources obtained from the same geographical region. A challenge is how to effectively utilize such other trajectory sources. Moreover, existing work does not attend the important attributes of a trajectory including vehicle ID, day of week, rainfall level etc., which are important for estimating the path travel time. Motivated by these and the recent successes of neural network models, we propose Attribute-related Hybrid Trajectories Network~(AtHy-TNet), a neural model that effectively utilizes the attribute correlations, as well as the spatial and temporal relationships across hybrid trajectory data. We apply this to a novel problem of estimating path travel time of a type of vehicles using a hybrid trajectory dataset that includes trajectories from other vehicle types. We demonstrate in our experiments the benefits of considering hybrid data for travel time estimation, and show that AtHy-TNet significantly outperforms state-of-the-art methods on real-world trajectory datasets.
SkyRec: Finding Pareto Optimal Groups
Authors: Jinfei Liu, Li Xiong, Jian Pei, Jun Luo, Haoyu Zhang, Si Zhang
Venue: CIKM
Year: 2019

We present SkyRec (Skyline Recommender), a recommendation toolkit for finding optimal groups based on the notion of group skyline. Skyline computation, aiming at identifying a set of skyline points that are not dominated by any other point, is particularly useful for multi-criteria data analysis and decision-making. Traditional skyline computation, however, is inadequate to answer queries that need to analyze not only individual points but also groups of points. o address this gap, SkyRec finds Pare to optimal groups with two group skyline models: G-Skyline [3] and Sum-Skyline [2]. SkyRecre turns Pare to optimal groups with group size k that are not dom-inated by any other group with the same group size. Users can examine the results of the group skyline based recommendation compared to traditional top-k and skyline based recommendation and how different group skyline notions differ from each other. Although we demonstrate Sky Rec for hotel reservation in this paper, it can be applied to various decision-making applications
Convolution-Consistent Collective Matrix Completion
Authors: Xu Liu, Jingrui He, Sam Duddy, Liz O&apos, Sullivan
Venue: CIKM
Year: 2019

Collective matrix completion refers to the problem of simultaneously predicting the missing entries in multiple matrices by leveraging the cross-matrix information. It finds abundant applications in various domains such as recommender system, dimensionality reduction, and image recovery. Most of the existing work represents the cross-matrix information in a shared latent structure constrained by the Euclidean-based pairwise similarity, which may fail to capture the nonlinear relationship of the data. To address this problem, in this paper, we propose a new collective matrix completion framework, named C4, which uses the graph spectral filters to capture the non-Euclidean cross-matrix information. To the best of our knowledge, this is the first effort to represent the cross-matrix information in the graph spectral domain. We benchmark our model against 8 recent models on 10 real-world data sets, and our model outperforms state-of-the-art methods in most tasks.
Sexual Harassment Story Classification and Key Information Identification
Authors: Yingchi Liu, Quanzhi Li, Xiaozhong Liu, Qiong Zhang, Luo Si
Venue: CIKM
Year: 2019

Recently more and more personal stories about sexual harassment are shared online, mainly inspired by the \#MeToo movement. Safecity is an online forum for victims of sexual harassment to share their personal experience. Previous study applied neural network models to classify the harassment forms of the stories. To uncover patterns of sexual harassment, the extraction of the key elements and the categorization of these stories in different dimensions can be useful as well. In this study, we proposed neural network models to extract key elements including harasser, time, location and trigger words. In addition, we categorized these stories from different dimensions, such as location, time, and harassers' characteristics, including their age range, single/multiple harassers, profession, and relationship with the victims. We further demonstrated that encoding the key element information in the story categorization model can improve its performance. The proposed approaches and analysis would be helpful in automatically filing reports, raising public awareness, making preventing strategies and etc.
MoBoost: A Self-improvement Framework for Linear-based Hashing
Authors: Xingbo Liu, Xiushan Nie, Xiaoming Xi, Lei Zhu, Yilong Yin
Venue: CIKM
Year: 2019

The linear model is commonly utilized in hashing methods owing to its efficiency. To obtain better accuracy, linear-based hashing methods focus on designing a generalized linear objective function with different constraints or penalty terms that consider neighborhood information. In this study, we propose a novel generalized framework called Model Boost (MoBoost), which can achieve the self-improvement of the linear-based hashing. The proposed MoBoost is used to improve model parameter optimization for linear-based hashing methods without adding new constraints or penalty terms. In the proposed MoBoost, given a linear-based hashing method, we first execute the method several times to get several different hash codes for training samples, and then combine these different hash codes into one set utilizing one novel fusion strategy. Based on this set of hash codes, we learn some new parameters for the linear hash function that can significantly improve accuracy. The proposed MoBoost can be generally adopted in existing linear-based hashing methods, achieving more precise and stable performance compared to the original methods while imposing negligible added expenditure in terms of time and space. Extensive experiments are performed based on three benchmark datasets, and the results demonstrate the superior performance of the proposed framework.
Machine Reading Comprehension: Matching and Orders
Authors: Ao Liu, Lizhen Qu, Junyu Lu, Chenbin Zhang, Zenglin Xu
Venue: CIKM
Year: 2019

In this paper, we study the machine reading comprehension of temporal order in text. Given a document of instruction sequences, a model aims to find out the most coherent sequences of activities matching the document among all answer candidates. To tackle the task, we proposeOrdMatch model, which is able to match each activity in a sequence to the corresponding instruction in the document and regularizes the partial order of activities to match the order of instructions. We evaluate the task using the RecipeQA dataset, which includes step-by-step instructions of cooking recipes. Our model outperforms the state-of-the-art models with a wide margin. The experimental results demonstrate the effectiveness of our novel ordering regularizer. Our code will be made available at \hrefhttps://github.com/Aolius/OrdMatch https://github.com/Aolius/OrdMatch.