OpenRaiser/Intern-Atlas huggingface dataset:
BIP! Finder: Facilitating Scientific Literature Search by Exploiting Impact-Based Ranking
Authors: Thanasis Vergoulis, Serafeim Chatzopoulos, Ilias Kanellos, Panagiotis Deligiannis, Christos Tryfonopoulos, Theodore Dalamagas
Venue: CIKM
Year: 2019

Due to the rapidly increasing number of scientific articles, finding valuable work for further research has become tedious and time consuming. To alleviate this issue, search engines have used citation-based article impact ranking. However, most engines rely on very simplistic impact measures (usually the citation count) and make the problematic assumption that there is a one-size-fits-all impact measure. To address these problems, we present BIP! Finder, a search engine that facilitates the identification of valuable articles by exploiting two different impact measures, each capturing a different aspect of the article impact. In addition, BIP! Finder provides many useful features (article comparison, intuitive visualisations, article bookmarking mechanism, etc.) making it a powerful addition to the researcher's toolbox.
Learning and Reasoning on Graph for Recommendation
Authors: Xiang Wang, Xiangnan He, Tat-Seng Chua
Venue: CIKM
Year: 2019

Recommendation methods construct predictive models to estimate the likelihood of a user-item interaction. Previous models largely follow a general supervised learning paradigm --- treating each interaction as a separate data instance and performing prediction based on the ''information isolated island''. Such methods, however, overlook the relations among data instances, which may result in suboptimal performance especially for sparse scenarios. Moreover, the models built on a separate data instance only can hardly exhibit the reasons behind a recommendation, making the recommendation process opaque to understand. In this tutorial, we revisit the recommendation problem from the perspective of graph learning. Common data sources for recommendation can be organized into graphs, such as user-item interactions (bipartite graphs), social networks, item knowledge graphs (heterogeneous graphs), among others. Such a graph-based organization connects the isolated data instances, bringing benefits to exploiting high-order connectivities that encode meaningful patterns for collaborative filtering, content-based filtering, social influence modeling and knowledge-aware reasoning. Together with the recent success of graph neural networks (GNNs), graph-based models have exhibited the potential to be the technologies for next-generation recommendation systems. This tutorial provides a review on graph-based learning methods for recommendation, with special focus on recent developments of GNNs and knowledge graph-enhanced recommendation. By introducing this emerging and promising topic in this tutorial, we expect the audience to get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies.
Efficient Sequential and Parallel Algorithms for Estimating Higher Order Spectra
Authors: Zigeng Wang, Abdullah-Al Mamun, Xingyu Cai, Nalini Ravishanker, Sanguthevar Rajasekaran
Venue: CIKM
Year: 2019

Higher order spectra (HOS) are a powerful tool in nonlinear time series analysis and they have been extensively used as feature representations in data mining, communications and cosmology domains. However, HOS estimation suffers from high computational cost and memory consumption. Any algorithm for computing the kth order spectra on a dataset of size n needs O(n^k-1 ) time since the output size will be O(n^k-1 ) as well, which makes the direct HOS analysis difficult for long time series, and further prohibits its direct deployment to resource-limited and time-sensitive applications. Existing algorithms for computing HOS are either inefficient or have been implemented on obsolete architectures. Thus it is essential to develop efficient generic algorithms for HOS estimations. In this paper, we present a package of generic sequential and parallel algorithms for computationally and memory efficient HOS estimations which can be employed on any parallel machine or platform. Our proposed algorithms largely reduce the HOS' computational cost and memory usage in spectrum multiplication and smoothing steps through carefully designed prefix sum operations. Moreover, we employ a matrix partitioning technique and design algorithms with optimal memory usage and present the parallel approaches on the PRAM and the mesh models. Furthermore, we implement our algorithms for both bispectrum and trispectrum estimations. We conduct extensive experiments and cross-compare the proposed algorithms' performance. Results show that our algorithms achieve state-of-the-art computational and memory efficiency, and our parallel algorithms achieve close to linear speedups. The code is available at https://github.com/ZigengWang/HOS.
Discerning Edge Influence for Network Embedding
Authors: Yaojing Wang, Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu
Venue: CIKM
Year: 2019

Network embedding, which learns the low-dimensional representations of nodes, has gained significant research attention. Despite its superior empirical success, often measured by the prediction performance of downstream tasks (e.g., multi-label classification), it is unclear \em why a given embedding algorithm outputs the specific node representations, and \em how the resulting node representations relate to the structure of the input network. In this paper, we propose to discern the edge influence as the first step towards understanding skip-gram basd network embedding methods. For this purpose, we propose an auditing framework Near, whose key part includes two algorithms (Near-add \ and Near-del ) to effectively and efficiently quantify the influence of each edge. Based on the algorithms, we further identify high-influential edges by exploiting the linkage between edge influence and the network structure. Experimental results demonstrate that the proposed algorithms (Near-add \ and Near-del ) are significantly faster (up to $2,000\times$) than straightforward methods with little quality loss. Moreover, the proposed framework can efficiently identify the most influential edges for network embedding in the context of downstream prediction task and adversarial attacking.
Beyond word2vec: Distance-graph Tensor Factorization for Word and Document Embeddings
Authors: Suhang Wang, Charu C. Aggarwal, Huan Liu
Venue: CIKM
Year: 2019

The \em word2vec methodology such as Skip-gram and CBOW has seen significant interest in recent years because of its ability to model semantic notions of word similarity and distances in sentences. A related methodology, referred to as \em doc2vec is also able to embed sentences and paragraphs. These methodologies, however, lead to different embeddings that cannot be related to one another. In this paper, we present a tensor factorization methodology, which simultaneously embeds words and sentences into latent representations in one shot. Furthermore, these latent representations are concretely related to one another via tensor factorization. Whereas \em word2vec and \em doc2vec are dependent on the use of contextual windows in order to create the projections, our approach treats each document as a structural graph on words. Therefore, all the documents in the corpus are jointly factorized in order to simultaneously create an embedding for the individual documents and the words. Since the graphical representation of a document is much richer than a contextual window, the approach is capable of designing more powerful representations than those using the \em word2vec family of methods. We use a carefully designed negative sampling methodology to provide an efficient implementation of the approach. We relate the approach to factorization machines, which provides an efficient alternative for its implementation. We present experimental results illustrating the effectiveness of the approach for document classification, information retrieval and visualization.
TraVis: An Interactive Visualization System for Mining Inbound Traveler Activities by Leveraging Mobile Ad Request Data
Authors: Pei-Xun Wang, Hsuan Chiu, Wen-Qian Chen, Che-Chia Chang, Yu-Hsuan Huang, Tzu-Hao Huang, Yuchun Lai, Chia-Hu Chang
Venue: CIKM
Year: 2019

The growth of inbound travel is fully coordinate with the successful urban development. Increasing the number of inbound travelers not only creates more jobs and economic opportunities but also drives the country toward prosperity. Thus, inbound traveler analysis through trajectory pattern mining, a subfield of urban computing, is regarded as a promising solution. This paper introduces large-scale mobile ad requests as an alternative data source of trajectory pattern mining in order to eliminate the limitations of conventional data sources, such as GPS data, cellular data, and IP address data. In addition, to expedite a comprehensive inbound traveler analysis, we build TraVis, a real-world system for efficiently exploring the inbound travelers' activities through the interactive visualization interface. By incorporating various modules, such as mobile users' home country and travel intent prediction, frequent trajectory pattern mining, and interactive visualization, TraVis proves the capability of profiling the travelers' behavior pattern. We use Japan inbound travelers in the case study to present the mining insights, and we also demonstrate the extensive system functionalities. Our system has been assisting Japan government agencies to formulate travel marketing strategies, including tourist experience enhancement and attractions marketing.
On Heavy-user Bias in A/B Testing
Authors: Yu Wang, Somit Gupta, Jiannan Lu, Ali Mahmoudzadeh, Sophia Liu
Venue: CIKM
Year: 2019

On-line experimentation (also known as A/B testing) has become an integral part of software development. To timely incorporate user feedback and continuously improve products, many software companies have adopted the culture of agile deployment, requiring online experiments to be conducted and concluded on limited sets of users for a short period. While conceptually efficient, the result observed during the experiment duration can deviate from what is seen after the feature deployment, which makes the A/B test result biased. In this paper, we provide theoretical analysis to show that heavy-users can contribute significantly to the bias, and propose a re-sampling estimator for bias adjustment.
Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities
Authors: Xiaobao Wang, Di Jin, Mengquan Liu, Dongxiao He, Katarzyna Musial, Jianwu Dang
Venue: CIKM
Year: 2019

The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.
Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding
Authors: Pei-Chi Wang, Cheng-Te Li
Venue: CIKM
Year: 2019

Heterogeneous network is a useful data representation in depicting complex interactions among multi-typed entities and relations. In this work, by representing criminal and terrorism activities as a heterogeneous network, we propose a novel unsupervised method, Outlier Spotting with behavior-aware Network Embedding (OSNE), to identify terrorists among potential criminals. The basic idea of OSNE is to exploit high-order relation paths for translation-based embedding learning, and distinguish same-type entities based on behavior penalty and type-aware negative sampling. We evaluate the effectiveness of OSNE using six criminal network datasets provided by DARPA, and make comparison with strong competitors. The results exhibit the promising performance of OSNE.
Neighborhood Interaction Attention Network for Link Prediction
Authors: Zhitao Wang, Yu Lei, Wenjie Li
Venue: CIKM
Year: 2019

Interactions between neighborhoods of two target nodes are often regarded as important clues for link prediction. In this paper, we propose a novel link prediction neural model named Neighborhood Interaction Attention Network (NIAN), which is able to automatically learn comprehensive neighborhood interaction features and predict links in an end-to-end way. The proposed model mainly consists of two attention layers. A node-level attention is designed to extract latent structure features of nodes in target neighborhoods. Based on the latent node features, a neighborhood-level attention is proposed to learn neighborhood interaction features by considering different importance of pair-wise interactions. The superiority of NIAN is demonstrated by extensive experiments on 6 benchmark datasets against 12 popular and state-of-the-art approaches.
Modeling Sentiment Evolution for Social Incidents
Authors: Yunjie Wang, Hui Li, Chen Lin
Venue: CIKM
Year: 2019

Modeling sentiment evolution for social incidents in Microblogs is of vital importance for both enterprises and government officials. Existing works on sentiment tracking are not satisfying, due to the lack of entity-level sentiment extraction and accurate sentiment shift detection. Identifying entity-level sentiment is challenging as Microbloggers often use multiple opinion expressions in a sentence which targets different entities. Moreover, the evolution of the background sentiment, which is essential to shift detection, is ignored in the previous study. To address these issues, we leverage the proximity information to obtain more precise entity-level sentiment extraction. Based on it, we propose to simultaneously model the evolution of background opinion and the sentiment shift using a state space model on the time series of sentiment polarities. Experiments on real data sets demonstrate that our proposed approaches outperform state-of-the-art methods on the task of modeling sentiment evolution for social incidents.
Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor
Authors: Jingyi Wang, Qiang Liu, Zhaocheng Liu, Shu Wu
Venue: CIKM
Year: 2019

With the influence of information explosion, there are more and more choices exposed to public view. Next item recommendation is being a significant and challenging task. Recently, attention mechanism, Convolutional Neural Networks (CNN) and other kinds of deep components are used to model user behaviors. However, the proposed models often fail to extract the feature of user behaviors in different time periods and the CNN-based models before are hard to make the used CNN interpretable. In this paper, we propose a CNN & Attention-based Sequential Feature Extractor (CASFE) module to capture the possible features of user behaviors at different time intervals. Specifically, we import CNN to extract multi-level features of user behaviors with different time periods. After each CNN layer, we use attention module to emphasize the different effect of behaviors on the prediction result. Besides, the features we try to extract here have the similar concept and meaning with the hand-crafted features in Feature Engineering, which proves the validity of CASFE. Accordingly, CASFE becomes a general sequential feature extractor that can be used in various sequential prediction tasks. With Multi-Layer Perceptron (MLP), CASFE would be a state-of-the-art next item recommendation model. The model obtains good performance on Last.fm_1K dataset and MovieLens_1M dataset. Besides, as a compatible extractor module, it can also promote CTR prediction models as well as other sequential prediction tasks.
QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation
Authors: Panpan Wang, Zhao Li, Yazhou Zhang, Yuexian Hou, Liangzhu Ge
Venue: CIKM
Year: 2019

Recently, recurrent neural networks (RNNs) based methods have achieved profitable performance on mining temporal characteristics in user behavior. However, user preferences are changing over time and have not been fully exploited in e-commerce scenarios. To fill in the gap, we propose an approach, called quantum inspired preference interactive networks (QPIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to interactively learn user preferences. Specifically, the tensor product operation is used to model the interaction among a single user's own preferences, i.e. individual preferences. A quantum many-body wave function (QMWF) is employed to model interaction among all users' preferences, i.e. group preferences. Further, we bridge them by deriving a rigorous projection, and thus take the interplay between them into account. Experiments on an Amazon dataset as well as a real-world e-commerce dataset demonstrate the effectiveness of QPIN, which achieves superior performances compared with the state-of-the-art methods in terms of AUC and F1-score.
Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning
Authors: Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke
Venue: CIKM
Year: 2019

Medicine Combination Prediction (MCP) based on Electronic Health Record (EHR) can assist doctors to prescribe medicines for complex patients. Previous studies on MCP either ignore the correlations between medicines (i.e., MCP is formulated as a binary classifcation task), or assume that there is a sequential correlation between medicines (i.e., MCP is formulated as a sequence prediction task). The latter is unreasonable because the correlations between medicines should be considered in an order-free way. Importantly, MCP must take additional medical knowledge (e.g., Drug-Drug Interaction (DDI)) into consideration to ensure the safety of medicine combinations. However, most previous methods for MCP incorporate DDI knowledge with a post-processing scheme, which might undermine the integrity of proposed medicine combinations. In this paper, we propose a graph convolutional reinforcement learning model for MCP, named Combined Order-free Medicine Prediction Network (CompNet), that addresses the issues listed above. CompNet casts the MCP task as an order-free Markov Decision Process (MDP) problem and designs a Deep Q Learning (DQL) mechanism to learn correlative and adverse interactions between medicines. Specifcally, we frst use a Dual Convolutional Neural Network (Dual-CNN) to obtain patient representations based on EHRs. Then, we introduce the medicine knowledge associated with predicted medicines to create a dynamic medicine knowledge graph, and use a Relational Graph Convolutional Network (R-GCN) to encode it. Finally, CompNet selects medicines by fusing the combination of patient information and the medicine knowledge graph. Experiments on a benchmark dataset, i.e., MIMIC-III, demonstrate that CompNet signifcantly outperforms state-of-the-art methods and improves a recently proposed model by 3.74%pt, 6.64%pt in terms of Jaccard and F1 metrics.
Multi-Target Multi-Camera Tracking with Human Body Part Semantic Features
Authors: Mingkun Wang, Dianxi Shi, Naiyang Guan, Wei Yi, Tao Zhang, Zunlin Fan
Venue: CIKM
Year: 2019

Recently, Multi-Target Multi-Camera Tracking (MTMCT) has gained more and more attention. It is a challenging task with major problems including occlusion, background clutter, poses and camera point of view variations. Compared to single camera tracking, which takes advantage of location information and strict time constraints, good appearance features are more important to MTMCT. This drives us to extract robust and discriminative features for MTMCT. We propose MTMCT\_HS which uses human body part semantic features to overcome the above challenges. We use a two-stream deep neural network to extract the global appearance features and human body part semantic maps separately, and employ aggregation operations to generate final features. We argue that these features are more suitable for affinity measurement, which can be seen as the average of appearance similarity weighted by the corresponding human body part similarity. Next, our tracker adopts a hierarchical correlation clustering algorithm, which combines targets' appearance feature similarity with motion correlation for data association. We validate the effectiveness of our MTMCT\_HS method by demonstrating its superiority over the state-of-the-art method on DukeMTMC benchmark. Experiments show that the extracted features with human body part semantics are more effective for MTMCT compared with the methods solely employing global appearance features.
Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network
Authors: Heyuan Wang, Ziyi Wu, Junyu Chen
Venue: CIKM
Year: 2019

Building an intelligent chatbot with multi-turn dialogue ability is a major challenge, which requires understanding the multi-view semantic and dependency correlation among words, n-grams and sub-sequences. In this paper, we investigate selecting the proper response for a context through multi-grained representation and interactive matching. To construct hierarchical representation types of text segments, we propose a refined architecture which exclusively consists of gated dilated-convolution and self-attention. Compared with the recurrent-based sentence modeling methods, this architecture provides more flexibility and a speedup. The matching signals of each utterance-response pair are extracted by integrating the interactive information from different views. Then a turns-aware attention mechanism is utilized to aggregate the matching sequence, so as to identify important utterances and capture the implicit relationship of the whole context. Experiments on two large-scale public data sets show that our model significantly outperforms the state-of-the-art methods in terms of all metrics. We empirically provide a thorough ablation test, as well as the comparison of different representation and matching strategies, for a better insight into how each component affects the performance of the model.