OpenRaiser/Intern-Atlas huggingface dataset:
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
Authors: An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, Julian J. McAuley
Venue: CIKM
Year: 2019

Sequential patterns play an important role in building modern recommender systems. To this end, several recommender systems have been built on top of Markov Chains and Recurrent Models (among others). Although these sequential models have proven successful at a range of tasks, they still struggle to uncover complex relationships nested in user purchase histories. In this paper, we argue that modeling pairwise relationships directly leads to an efficient representation of sequential features and captures complex item correlations. Specifically, we propose a 2D convolutional network for sequential recommendation (CosRec). It encodes a sequence of items into a three-way tensor; learns local features using 2D convolutional filters; and aggregates high-order interactions in a feedforward manner. Quantitative results on two public datasets show that our method outperforms both conventional methods and recent sequence-based approaches, achieving state-of-the-art performance on various evaluation metrics.
Imbalance Rectification in Deep Logistic Regression for Multi-Label Image Classification Using Random Noise Samples
Authors: Wenjin Yan, Ruixuan Li, Jun Wang, Yuhua Li, Jinyang Wang, Pan Zhou, Xiwu Gu
Venue: CIKM
Year: 2019

Logistic regression (LR) is the most commonly used loss function in multi-label image classification. However, it suffers from class imbalance problem caused by the huge difference in quantity between positive and negative samples as well as between different classes. First, we find that feeding randomly generated noise samples into an LR classifier is an effective way to detect class imbalances, and further define an informative imbalance metric named inference tendency based on noise sample analysis. Second, we design an efficient moving average based method for calculating inference tendency, which can be easily done during training with negligible overhead. Third, two novel rectification methods called extremum shift (ES) and tendency constraint (TC) are designed to offset or constrain inference tendency in the loss function, and mitigate class imbalances significantly. Finally, comparative experiments with Resnet on Microsoft COCO, NUS-WIDE and DeepFashion demonstrate the effectiveness of inference tendency and the superiority of our approach over the baseline LR and several state-of-the-art alternatives.
Query-Specific Knowledge Summarization with Entity Evolutionary Networks
Authors: Carl Yang, Lingrui Gan, Zongyi Wang, Jiaming Shen, Jinfeng Xiao, Jiawei Han
Venue: CIKM
Year: 2019

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization. For example, given a query "computer vision'' on a CS literature corpus, rather than returning a list of relevant entities like "cnn'', "imagenet'' and "svm'', we are interested in the connections among them, and furthermore, the evolution patterns of such connections along particular ordinal dimensions such as time. Particularly, we hope to provide structural knowledge relevant to the query, such as "svm'' is related to "imagenet'' but not "cnn''. Moreover, we aim to model the changing trends of the connections, such as "cnn'' becomes highly related to "imagenet'' after 2010, which enables the tracking of knowledge evolutions. In this work, to facilitate such a novel insightful search system, we propose SetEvolve, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora. Systematic experiments on synthetic data and insightful case studies on real-world corpora demonstrate the utility of SetEvolve.
Exploring The Interaction Effects for Temporal Spatial Behavior Prediction
Authors: Huan Yang, Tianyuan Liu, Yuqing Sun, Elisa Bertino
Venue: CIKM
Year: 2019

In location based services, predicting users' temporal-spatial behavior is critical for accurate recommendation. In this paper, we adopt a joint embedding (JointE) model to learn the representations of user, location, and users' action in the same latent space. The functionality of a location is the critical factor influencing different elements of the behavior and is learned by an embedding vector encoding crowd behaviors. A user personalized preference is learned from the user historical behaviors and has two features. One is the combination of action and location, which is learned by maximizing the semantic consistency of the observed behaviors. The other is the periodic preference. Inspired by the notion of periodical temporal rules, we introduce the concept of temporal pattern to describe how often users visit places so as to reduce the high temporal variance of behaviors. A projection matrix is introduced to combine the temporal patterns with location functionality. A user behavior is predicted by the joint probability on behavior elements. We conduct experiments against two representative datasets. The results show that our approach outperforms other approaches.
Streamline Density Peak Clustering for Practical Adoptions
Authors: Shuai Yang, Xipeng Shen, Min Chi
Venue: CIKM
Year: 2019

Since Density Peak Clustering (DPC) algorithm was proposed in 2014, it has drawn lots of interest in various domains. As a clustering method, DPC features superior generality, robustness, flexibility and simplicity. There are however two main roadblocks for its practical adoptions, both centered around the selection of cutoff distance, the single critical hyperparameter of DPC. This work proposes an improved algorithm named Streamlined Density Peak Clustering (SDPC). SDPC speeds up DPC executions on a sequence of cutoff distances by 2.2-8.8X while at the same time reducing memory usage by a magnitude. As an algorithm preserving the original semantic of DPC, SDPC offers an efficient and scalable drop-in replacement of DPC for data clustering.
An Active and Deep Semantic Matching Framework for Query Rewrite in E-Commercial Search Engine
Authors: Yatao Yang, Jun Tan, Hongbo Deng, Zibin Zheng, Yutong Lu, Xiangke Liao
Venue: CIKM
Year: 2019

In order to make the query retrieve much more related products, some query rewrite methods have been proposed to obtain a set of candidate queries which can infer users' search intents and reduce the vocabulary gap between the original query and title of related products. However, previous studies ignore that some candidate queries may change users' search intents and retrieve irrelevant products. As a result, users' search experience will be impacted significantly. To reduce this influence, we need to design a semantic matching model to determine whether the candidate query change the original query's search intents (semantics). In addition, building a semantic matching model faces the following challenges: 1) Queries are usually very short and have limited information. It is very hard to learn an effective semantic matching model with the textual information of queries and candidate queries. 2) In order to get a generalized and effective mode, sufficient data samples are required to train the model. However, the cost of labeling is very huge. In order to address the above challenges, we propose an active and deep semantic matching framework (ActiveMatch) which is composed of two components. One component is the deep semantic matching (DSM) model which can make full use of the search log information to enhance the representation of queries and candidate queries. Then, it can estimate the semantic similarity between the original query and the candidate query more accurately. The other component is an uncertainty and novelty sampling (UNS) strategy which selects the samples to label based on the difficulty of the model estimating and the probability of the occurrence of new words. It not only reduces the cost of labeling but also ensures the effectiveness of the model. The experimental results on the Taobao e-commercial search platform verify the effectiveness of our framework.
Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks
Authors: Yiying Yang, Zhongyu Wei, Qin Chen, Libo Wu
Venue: CIKM
Year: 2019

This paper focuses on a novel financial event prediction task that takes a historical event chain as input and predicts what event will happen next. We introduce financial news as supplementary information to solve problems of multiple interpretations of same financial event. Besides, a gated graph neural network based approach is utilized to capture complicated relationships between event graphs for better event prediction. For the evaluation, we build a new dataset consisting of financial events for thousands of Chinese listed companies from 2013 to 2017. Experimental results show the effectiveness of our proposed model.
Regularizing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method
Authors: Shuai Yao, Yuexian Hou, Liangzhu Ge, Zeting Hu
Venue: CIKM
Year: 2019

Deep Neural Networks (DNNs) with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Till now, many regularizers such as dropout, data augmentation have been proposed to prevent overfitting. Motivated by ensemble learning, we treat each hidden layer in neural networks as an ensemble of some base learners by dividing hidden units into some non-overlapping groups and each group is considered as a base learner. Based on the theoretical analysis of generalization error of ensemble estimators (bias-variance-covariance decomposition), we find the variance of each base learner plays an important role in preventing overfitting and propose a novel regularizer---\emphEnsemble-based Low-Level Sample-Variances Method (ELSM) to encourage each base learner of hidden layers to have a low-level sample-variance. Experiments across a number of datasets and network architectures show that ELSM can effectively reduce overfitting and improve the generalization ability of DNNs.
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Authors: Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang
Venue: CIKM
Year: 2019

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous works focus either on knowledge graph embedding or behavior graph embedding while few works consider both in a unified way. Here we present BEM, a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs. To be more specific, BEM takes as prior the pre-trained embeddings from the knowledge graph, and integrates them with the pre-trained embeddings from the behavior graphs via a Bayesian generative model. BEM is able to mutually refine the embeddings from both sides while preserving their own topological structures. To show the superiority of our method, we conduct a range of experiments on three benchmark datasets: node classification, link prediction, triplet classification on two small datasets related to Freebase, and item recommendation on a large-scale e-commerce dataset.
Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis
Authors: Da Yin, Xiao Liu, Xiaojun Wan
Venue: CIKM
Year: 2019

In this paper, we propose an interactive multi-grained joint model for targeted sentiment analysis. Firstly, different from previous works, we leverage the correlation between target and sentiment clues and deeply strengthen interaction between them because targets are highly related to the sentiment clues in a sentence. Moreover, we apply a multi-layer structure to consider multi-grained target and sentiment tagging information more comprehensively. Also, we design two specific loss functions to prevent a word from being both part of a target and a sentiment clue simultaneously, and to align the boundary information of two labeling subsystems. We conduct experiments on English and Spanish datasets and the experimental results show that our approach substantially outperforms a variety of previous models and achieves new state-of-the-art results on these datasets.
Experimental Study of Multivariate Time Series Forecasting Models
Authors: Jiaming Yin, Weixiong Rao, Mingxuan Yuan, Jia Zeng, Kai Zhao, Chenxi Zhang, Jiangfeng Li, Qinpei Zhao
Venue: CIKM
Year: 2019

Multivariate time series forecasting has wide applications such as traffic flow prediction, supermarket commodity demand forecasting and etc. In literature, Due to the complex temporal patterns and inter-dependencies among multivariate time series, a large number of forecasting models have been developed. However, one question still remains unclear: how these models perform on a certain forecasting task, and there is lack of comprehensive performance comparison of these models on different tasks. To this end, in this paper, we conduct a systematic evaluation of eight representative forecasting models over eight multivariate time series datasets, and have the following findings: 1) When the datasets exhibit strong periodic patterns, deep learning models perform best. Otherwise on the datasets in a non-periodic manner, the statistical models such as ARIMA perform best. 2) For the long term prediction involving a high horizon value, the direct prediction strategy could lead to lower errors than the recursive one, but at the cost of higher training time. 3) For the multivariate time series explicitly involving graphic inter-dependencies among the multivariates, e.g., the road network topology in the spatio-temporal time series of traffic volumes in multiple routes, the Graph Convolution Network can incorporate the graphic inter-dependencies into their forecasting models for smaller prediction errors.
Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation
Authors: Di You, Nguyen Vo, Kyumin Lee, Qiang Liu
Venue: CIKM
Year: 2019

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted via social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this paper we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms eight state-of-the-art recommendation models, achieving at least 3$\sim$5.3% improvement. Our source code and dataset are available at \urlhttps://web.cs.wpi.edu/~kmlee/data.html .
Locally Slope-based Dynamic Time Warping for Time Series Classification
Authors: Jidong Yuan, Qianhong Lin, Wei Zhang, Zhihai Wang
Venue: CIKM
Year: 2019

Dynamic time warping (DTW) has been widely used in various domains of daily life. Essentially, DTW is a non-linear point-to-point matching method under time consistency constraints to find the optimal path between two temporal sequences. Although DTW achieves a globally optimal solution, it does not naturally capture locally reasonable alignments. Concretely, two points with entirely dissimilar local shape may be aligned. To solve this problem, we propose a novel weighted DTW based on local slope feature (LSDTW), which enhances DTW by taking regional information into consideration. LSDTW is inherently a DTW algorithm. However, it additionally attempts to pair locally similar shapes, and to avoid matching points with distinct neighborhood slopes. Furthermore, when LSDTW is used as a similarity measure in the popular nearest neighbor classifier, it beats other distance-based methods on the vast majority of public datasets, with significantly improved classification accuracies. In addition, case studies establish the interpretability of the proposed method.
Selecting the Optimal Groups: Efficiently Computing Skyline k-Cliques
Authors: Chen Zhang, Wenjie Zhang, Ying Zhang, Lu Qin, Fan Zhang, Xuemin Lin
Venue: CIKM
Year: 2019

In many applications, graphs often involve the nodes with multi-dimensional numerical attributes, and it is desirable to retrieve a group of nodes that are both highly connected (e.g., clique) and optimal according to some ranking functions. It is well known that the skyline returns candidates for the optimal objects when ranking functions are not specified. Motivated by this, in this paper we formulate the novel model of skyline k-cliques over multi-valued attributed graphs and develop efficient algorithms to conduct the computation. To verify the group based dominance between two k-cliques, we make use of maximum bipartite matching and develop a set of optimization techniques to improve the verification efficiency. Then, a progressive computation algorithm is developed which enumerates the k-cliques in an order such that a k-clique is guaranteed not to be dominated by those generated after it. Novel pruning and early termination techniques are developed to exclude unpromising nodes or cliques by investigating the structural and attribute properties of the multi-valued attributed graph. Empirical studies on four real datasets demonstrate the effectiveness of the skyline k-clique model and the efficiency of the novel computing techniques.
An Interactive Mechanism to Improve Question Answering Systems via Feedback
Authors: Xinbo Zhang, Lei Zou, Sen Hu
Venue: CIKM
Year: 2019

Semantic parsing-based RDF question answering (QA) systems are to interpret users' natural language questions as query graphs and return answers over RDF repository. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' question sentences precisely, hence QA systems may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this paper, we design an I nteractive M echanism aiming for PRO motion V ia users' fe edback to Q A systems (IMPROVE-QA), a whole framework to not only make existing QA systems return more precise answers based on a few feedbacks over the original answers given by RDF QA systems, but also enhance paraphrasing dictionaries to ensure a continuous-learning capability in improving RDF QA systems. To provide better interactivity and online performance, we design a holistic graph mining algorithm (HWspan) to automatically refine the query graph. Extensive experiments on both Freebase and DBpedia confirm the effectiveness and superiority of our approach.
Practical Access Pattern Privacy by Combining PIR and Oblivious Shuffle
Authors: Zhilin Zhang, Ke Wang, Weipeng Lin, Ada Wai-Chee Fu, Raymond Chi-Wing Wong
Venue: CIKM
Year: 2019

We consider the following secure data retrieval problem: a client outsources encrypted data blocks to a semi-trusted cloud server and later retrieves blocks without disclosing access patterns. Existing PIR and ORAM solutions suffer from serious performance bottlenecks in terms of communication or computation costs. To help eliminate this void, we introduce "access pattern unlinkability'' that separates access pattern privacy into short-term privacy at individual query level and long-term privacy at query distribution level. This new security definition provides tunable trade-offs between privacy and query performance. We present an efficient construction, called SBR protocol, using PIR and Oblivious Shuffling to enable secure data retrieval while satisfying access pattern unlinkability. Both analytical and empirical analysis show that SBR exhibits flexibility and usability in practice.
Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence
Authors: Yin Zhang, James Caverlee
Venue: CIKM
Year: 2019

Fashion-focused key opinion bloggers on Instagram, Facebook, and other social media platforms are fast becoming critical influencers. They can inspire consumer clothing purchases by linking high fashion visual evolution with daily street style. In this paper, we build thefirst visual influence-aware fashion recommender (FIRN) with leveraging fashion bloggers and their dynamic visual posts. Specifically, we extract thedynamic fashion features highlighted by these bloggers via a BiLSTM that integrates a large corpus of visual posts and community influence. We then learn theimplicit visual influence funnel from bloggers to individual users via a personalized attention layer. Finally, we incorporate user personal style and her preferred fashion features across time in a recurrent recommendation network for dynamic fashion-updated clothing recommendation. Experiments show that FIRN outperforms state-of-the-art fashion recommenders, especially for users who are most impacted by fashion influencers, and utilizing fashion bloggers can bring greater improvements in recommendation compared with using other potential sources of visual information. We also release a largetime-aware high-quality visual dataset of fashion influencers that can be exploited for future research.
Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework
Authors: Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi
Venue: CIKM
Year: 2019

Online underground forums have been widely used by cybercriminals to exchange knowledge and trade in illicit products or services, which have played a central role in the cybercriminal ecosystem. In order to combat the evolving cybercrimes, in this paper, we propose and develop an intelligent system named iDetective to automate the analysis of underground forums for the identification of key players (i.e., users who play the vital role in the value chain). In iDetective, we first introduce an attributed heterogeneous information network (AHIN) for user representation and use a meta-path based approach to incorporate higher-level semantics to build up relatedness over users in underground forums; then we propose Player2Vec to efficiently learn node (i.e., user) representations in AHIN for key player identification. In Player2Vec, we first map the constructed AHIN to a multi-view network which consists of multiple single-view attributed graphs encoding the relatedness over users depicted by different designed meta-paths; then we employ graph convolutional network (GCN) to learn embeddings of each single-view attributed graph; later, an attention mechanism is designed to fuse different embeddings learned based on different single-view attributed graphs for final representations. Comprehensive experiments on the data collections from different underground forums (i.e., Hack Forums, Nulled) are conducted to validate the effectiveness of iDetective in key player identification by comparisons with alternative approaches.