OpenRaiser/Intern-Atlas huggingface dataset:
Topic Modeling for Multi-Aspect Listwise Comparisons
Authors: Delvin Ce Zhang, Hady W. Lauw
Venue: CIKM
Year: 2021

As a well-established probabilistic method, topic models seek to uncover latent semantics from plain text. In addition to having textual content, we observe that documents are usually compared in listwise rankings based on their content. For instance, world-wide countries are compared in an international ranking in terms of electricity production based on their national reports. Such document comparisons constitute additional information that reveal documents' relative similarities. Incorporating them into topic modeling could yield comparative topics that help to differentiate and rank documents. Furthermore, based on different comparison criteria, the observed document comparisons usually cover multiple aspects, each expressing a distinct ranked list. For example, a country may be ranked higher in terms of electricity production, but fall behind others in terms of life expectancy or government budget. Each comparison criterion, or aspect, observes a distinct ranking. Considering such multiple aspects of comparisons based on different ranking criteria allows us to derive one set of topics that inform heterogeneous document similarities. We propose a generative topic model aimed at learning topics that are well aligned to multi-aspect listwise comparisons. Experiments on public datasets demonstrate the advantage of the proposed method in jointly modeling topics and ranked lists against baselines comprehensively.
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
Authors: Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, Qiang Yang
Venue: CIKM
Year: 2021

We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phases: named entity recognition (NER) and attribute value normalization (AVN). However, existing works only focus on the NER phase but neglect equally important AVN. To bridge this gap, this paper proposes a unified query attribute value extraction system in e-commerce search named QUEACO, which involves both two phases. Moreover, by leveraging large-scale weakly-labeled behavior data, we further improve the extraction performance with less supervision cost. Specifically, for the NER phase, QUEACO adopts a novel teacher-student network, where a teacher network that is trained on the strongly-labeled data generates pseudo-labels to refine the weakly-labeled data for training a student network. Meanwhile, the teacher network can be dynamically adapted by the feedback of the student's performance on strongly-labeled data to maximally denoise the noisy supervisions from the weak labels. For the AVN phase, we also leverage the weakly-labeled query-to-attribute behavior data to normalize surface form attribute values from queries into canonical forms from products. Extensive experiments on a real-world large-scale E-commerce dataset demonstrate the effectiveness of QUEACO.
International Workshop on Privacy, Security and Trust in Computational Intelligence (PSTCI2021)
Authors: Xuyun Zhang, Deepak Puthal, Chi Yang, Guanfeng Liu, Kim-Kwang Raymond Choo, Hongzhi Yin
Venue: CIKM
Year: 2021

While being a lasting theme, privacy, security, and trust (PST) has been increasingly important in recent days due to the pervasive (but more prone) computation infrastructure and deep (but more intrusive) data analytics, and has been hugely demanded from governments, companies, and individuals. This workshop aims at providing a forum for researchers, practitioners and developers from different background areas such as computational intelligence, data privacy and cyber security, trust management, cloud computing, edge computing, Internet of Things, big data analytics, machine learning and data mining, knowledge discovery to exchange the latest experience, research ideas and synergic research and development on fundamental issues and applications about privacy, security and trust issues in computational intelligence.
DSDD: Domain-Specific Dataset Discovery on the Web
Authors: Haoxiang Zhang, Aécio S. R. Santos, Juliana Freire
Venue: CIKM
Year: 2021

With the push for transparency and open data, many datasets and data repositories are becoming available on the Web. This opens new opportunities for data-driven exploration, from empowering analysts to answer new questions and obtain insights to improving predictive models through data augmentation. But as datasets are spread over a plethora of Web sites, finding data that are relevant for a given task is difficult. In this paper, we take a first step towards the construction of domain-specific data lakes. We propose an end-to-end dataset discovery system, targeted at domain experts, which given a small set of keywords, automatically finds potentially relevant datasets on the Web. The system makes use of search engines to hop across Web sites, uses online learning to incrementally build a model to recognize sites that contain datasets, utilizes a set of discovery actions to broaden the search, and applies a multi-armed bandit based algorithm to balance the trade-offs of different discovery actions. We report the results of an extensive experimental evaluation over multiple domains, and demonstrate that our strategy is effective and outperforms state-of-the-art content discovery methods.
Pareto-optimal Community Search on Large Bipartite Graphs
Authors: Yuting Zhang, Kai Wang, Wenjie Zhang, Xuemin Lin, Ying Zhang
Venue: CIKM
Year: 2021

In many real-world applications, bipartite graphs are naturally used to model relationships between two types of entities. Community discovery over bipartite graphs is a fundamental problem and has attracted much attention recently. However, all existing studies overlook the weight (e.g., influence or importance) of vertices in forming the community, thus missing useful properties of the community. In this paper, we propose a novel cohesive subgraph model named Pareto-optimal (α β), which is the first to consider both structure cohesiveness and weight of vertices on bipartite graphs. The proposed Pareto-optimal (α β) model follows the concept of (α, β)-core by imposing degree constraints for each type of vertices, and integrates the Pareto-optimality in modelling the weight information from two different types of vertices. An online query algorithm is developed to retrieve Pareto-optimal (α β) with the time complexity of O(p. m) where p is the number of resulting communities, and m is the number of edges in the bipartite graph G. To support efficient query processing over large graphs, we also develop index-based approaches. A complete index i is proposed, and the query algorithm based on i achieves linear query processing time regarding the result size (i.e., the algorithm is optimal). Nevertheless, the index i incurs prohibitively expensive space complexity. To strike a balance between query efficiency and space complexity, a space-efficient compact index 𝕀 is proposed. Computation-sharing strategies are devised to improve the efficiency of the index construction process for the index 𝕀. Extensive experiments on 9 real-world graphs validate both the effectiveness and the efficiency of our query processing algorithms and indexing techniques.
Comprehensively Computing Link-based Similarities by Building A Random Surfer Graph
Authors: Mingxi Zhang, Xifeng Yan, Wei Wang
Venue: CIKM
Year: 2021

Link-based similarity computation arises in many real applications, including web search, clustering and recommender system. Lots of similarity measures are devoted recently, but there is one undesirable drawback, called ''path missing'' issue, i.e., the paths between objects are not fully considered for similarity computation. For example, SimRank considers only in-coming paths of equal length from a common ''center'' object, and a large portion of other paths are fully neglected. A comprehensive measure can be modeled by tallying all the possible paths between objects, but a large number of traverses would be required for these paths to fetch the similarities, which might increase the computational difficulty. In this paper, we propose a comprehensive similarity measure, namely RG-SimRank (Random surfer Graph-based SimRank), which resolves the "path missing'' issue with inheriting the philosophy of SimRank. We build a random surfer graph by allowing the surfer to stay at current object, go to other objects against in-links or along out-links. RG-SimRank adopts SimRank to compute similarities in random surfer graph instead of the original network, which has a same form of SimRank and hence inherits the optimization techniques on similarity computation. We prove that RG-SimRank considers all the possible paths of any direction and any length. And it provides a general solution to assess similarities, under which lots of existing similarity measures become its special cases. Other similarity measures besides SimRank can also be enhanced similarly using random surfer graph. Extensive experiments on real datasets demonstrate the performance of the proposed approach.
DML: Dynamic Multi-Granularity Learning for BERT-Based Document Reranking
Authors: Xuanyu Zhang, Qing Yang
Venue: CIKM
Year: 2021

Recently, pre-trained language models have been successfully applied to the task of text retrieval and ranking. However, in real scenes, users' click behavior is usually affected by selection, position, or exposure bias, which may lead to insufficient positive annotations and introduce additional noise. And for different candidate documents of the same query, the previous optimization objectives usually use a single granularity and static loss weights. It makes the performance of ranking models more susceptible to the bias issue mentioned above. Thus, in this paper, we focus on BERT-based document reranking and propose Dynamic Multi-Granularity Learning (DML). By introducing Gaussian distribution into traditional loss functions, the weights of different documents can change dynamically according to the prediction probability to avoid the impact of unlabeled positive documents. Besides, both document-granularity and instance-granularity are considered to balance relative relations and absolute scores of candidate documents. Extensive experiments show that DML significantly outperforms previous state-of-the-art models on the MS MARCO document ranking dataset.
SNPR: A Serendipity-Oriented Next POI Recommendation Model
Authors: Mingwei Zhang, Yang Yang, Rizwan Abbas, Ke Deng, Jianxin Li, Bin Zhang
Venue: CIKM
Year: 2021

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited. As a result, users become bored with obvious recommendations. To address this issue, we propose Serendipity-oriented Next POI Recommendation model (SNPR), a supervised multi-task learning problem, with objective to recommend unexpected and relevant POIs only. To this end, we define the quantitativeserendipity as a trade-off ofrelevance andunexpectedness in the context of next POI recommendation, and design a dedicated neural network with Transformer to capture complex interdependencies between POIs in user's check-in sequence. Extensive experimental results show that our model can improverelevance significantly while theunexpectedness outperforms the state-of-the-art serendipity-oriented recommendation methods.
Minimizing Spectral Radius of Non-Backtracking Matrix by Edge Removal
Authors: Zuobai Zhang, Zhongzhi Zhang, Guanrong Chen
Venue: CIKM
Year: 2021

The spectral radius of the non-backtracking matrix for an undirected graph plays an important role in various dynamic processes running on the graph. For example, its reciprocal provides an excellent approximation of epidemic and edge percolation thresholds. In this paper, we study the problem of minimizing the spectral radius of the non-backtracking matrix of a graph with n nodes and m edges, by deleting k selected edges. We show that the objective function of this combinatorial optimization problem is not submodular, although it is monotone. Since any straightforward approach to solving the optimization problem is computationally infeasible, we present an effective, scalable approximation algorithm with complexity O (n+km). Extensive experiment results for a large set of real-world networks verify the effectiveness and efficiency of our algorithm, and demonstrate that our algorithm outperforms several baseline schemes.
k-sums Clustering: A Stochastic Optimization Approach
Authors: Wan-Lei Zhao, Shi-Ying Lan, Run-Qing Chen, Chong-Wah Ngo
Venue: CIKM
Year: 2021

In this paper, we revisit the decades-old clustering method k -means. The egg-chicken loop in traditional k -means has been replaced by a pure stochastic optimization procedure. The optimization is undertaken from the perspective of each individual sample. Different from existing incremental k -means, an individual sample is tentatively joined into a new cluster to evaluate its distance to the corresponding new centroid, in which the contribution from this sample is accounted. The sample is moved to this new cluster concretely only after we find the reallocation makes the sample closer to the new centroid than it is to the current one. Compared with traditional k -means and other variants, this new procedure allows the clustering to converge faster to a better local minimum. This fundamental modification over the k -means loop leads to the redefinition of a family of k -means variants, such as hierarchical k -means, and Sequential k -means. As an extension, a new target function that minimizes the summation of pairwise distances within clusters is presented. Under l2-norm, it could be solved under the same stochastic optimization procedure. The re-defined traditional k -means, hierarchical k -means, as well as Sequential k-means all show considerable performance improvement over their traditional counterparts under different settings and on various types of datasets.
SaDes: An Interactive System for Sensitivity-aware Desensitization towards Tabular Data
Authors: Kechun Zhao, Hui Li, Zheng Gong, Jiangtao Cui
Venue: CIKM
Year: 2021

Before the publication of particular datasets, in order to protect the private information while preserving the usability as much as possible, desensitization is required. Automatic identification and evaluation of sensitive attributes are prerequisites for targeted desensitization of datasets, sensitivity can also reflect the effect of desensitization in turn. However, existing desensitization systems all rely on predefined desensitization model with respect to manually given sensitivity levels, which is subjective and unable to be applied end-to-end. Besides, there is no way for the user to tell whether the desensitization is performed enough or superfluous. In this demonstration, we present an interactive system for sensitivity-aware desensitization towards tabular data (SaDes). It automatically evaluates the risks of re-identification for arbitrary columns according to record-linkage attack, and performs desensitization accordingly. The risks of re-identification for the desensitized data can be immediately evaluated such that the user can iteratively execute desensitization in order to achieve a better balance between the usability and privacy. To the best of our knowledge, SaDes is the first system that provides automatic sensitivity evaluation and interactive desensitization in a back-to-back manner.
Multimodal Graph Meta Contrastive Learning
Authors: Feng Zhao, Donglin Wang
Venue: CIKM
Year: 2021

In recent years, graph contrastive learning has achieved promising node classification accuracy using graph neural networks (GNNs), which can learn representations in an unsupervised manner. However, such representations cannot be generalized to unseen novel classes with only few-shot labeled samples in spite of exhibiting good performance on seen classes. In order to assign generalization capability to graph contrastive learning, we propose multimodal graph meta contrastive learning (MGMC) in this paper, which integrates multimodal meta learning into graph contrastive learning. On one hand, MGMC accomplishes effectively fast adapation on unseen novel classes by the aid of bilevel meta optimization to solve few-shot problems. On the other hand, MGMC can generalize quickly to a generic dataset with multimodal distribution by inducing the FiLM-based modulation module. In addition, MGMC incorporates the lastest graph contrastive learning method that does not rely on the onstruction of augmentations and negative examples. To our best knowledge, this is the first work to investigate graph contrastive learning for few-shot problems. Extensieve experimental results on three graph-structure datasets demonstrate the effectiveness of our proposed MGMC in few-shot node classification tasks.
When Hardness Makes a Difference: Multi-Hop Knowledge Graph Reasoning over Few-Shot Relations
Authors: Shangfei Zheng, Wei Chen, Pengpeng Zhao, An Liu, Junhua Fang, Lei Zhao
Venue: CIKM
Year: 2021

Knowledge graph (KG) reasoning is a significant method for KG completion. To enhance the explainability of KG reasoning, some studies adopt reinforcement learning (RL) to complete the multi-hop reasoning. However, RL-based reasoning methods are severely limited by few-shot relations (only contain few triplets). To tackle the problem, recent studies introduce meta-learning into RL-based methods to improve reasoning performance. However, the generalization abilities of their models are limited due to the problem of low reasoning accuracies over hard relations (e.g., language and title). To overcome this problem, we propose a novel model called THML (Two-level Hardness-aware Meta-reinforcement Learning). Specifically, the model contains the following two components: (1) A hardness-aware meta-reinforcement learning method is proposed to predict the missing element by training hardness-aware batches. (2) A two-level hardness-aware sampling is proposed to effectively generate new hardness-aware batches from relation level and relation-cluster level. The generalization ability of our model is significantly improved by repeating the process of these two components in an alternate way. The experimental results demonstrate that THML notably outperforms the state-of-the-art approaches in few-shot scenarios.
HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting
Authors: Shun Zheng, Zhifeng Gao, Wei Cao, Jiang Bian, Tie-Yan Liu
Venue: CIKM
Year: 2021

The outbreak of the COVID-19 pandemic has largely influenced the world and our normal daily lives. To combat this pandemic efficiently, governments usually need to coordinate essential resources across multiple regions and adjust intervention polices at the right time, which all call for accurate and robust forecasting of future epidemic trends. However, designing such a forecasting system is non-trivial, since we need to handle all kinds of locations at different administrative levels, which include pretty different epidemic-evolving patterns. Moreover, there are dynamic and volatile correlations of pandemic conditions among these locations, which further enlarge the difficulty in forecasting. With these challenges in mind, we develop a novel spatial-temporal forecasting framework. First, to accommodate all kinds of locations at different administrative levels, we propose a unified hierarchical view, which mimics the aggregation procedure of pandemic statistics. Then, this view motivates us to facilitate joint learning across administrative levels and inspires us to design the cross-level consistency loss as an extra regularization to stabilize model training. Besides, to capture those dynamic and volatile spatial correlations, we design a customized spatial module with adaptive edge gates, which can both reinforce effective messages and disable irrelevant ones. We put this framework into production to help the battle against COVID-19 in the United States. A comprehensive online evaluation across three months demonstrates that our projections are the most competitive ones among all results produced by dozens of international group and even surpass the official ensemble in many cases. We also visualize our unique edge gates to understand the evolvement of spatial correlations and present intuitive case studies. Besides, we open source our implementation at https://github.com/dolphin-zs/HierST to facilitate future research towards better epidemic modeling.
Understanding the Property of Long Term Memory for the LSTM with Attention Mechanism
Authors: Wendong Zheng, Putian Zhao, Kai Huang, Gang Chen
Venue: CIKM
Year: 2021

Recent trends of incorporating LSTM network with different attention mechanisms in time series forecasting have led researchers to consider the attention module as an essential component. While existing studies revealed the effectiveness of attention mechanism with some visualization experiments, the underlying rationale behind their outstanding performance on learning long-term dependencies remains hitherto obscure. In this paper, we aim to elaborate on this fundamental question by conducting a thorough investigation of the memory property for LSTM network with attention mechanism. We present a theoretical analysis of LSTM integrated with attention mechanism, and demonstrate that it is capable of generating an adaptive decay rate which dynamically controls the memory decay according to the obtained attention score. In particular, our theory shows that attention mechanism brings significantly slower decays than the exponential decay rate of a standard LSTM. Experimental results on four real-world time series datasets demonstrate the superiority of the attention mechanism for maintaining long-term memory when compared to the state-of-the-art methods, and further corroborate our theoretical analysis.
Automated Query Graph Generation for Querying Knowledge Graphs
Authors: Weiguo Zheng, Mei Zhang
Venue: CIKM
Year: 2021

Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. However, it remains a challenge to bridge the gap between unstructured questions and structured knowledge graphs. To address the problem, a natural discipline is building a structured query to represent the input question. Searching the structured query over the knowledge graph can produce answers to the question. Distinct from the existing methods that are based on semantic parsing or templates, we propose an effective approach qaSQP powered by a novel notion, structural query pattern, in this paper. Given an input question, we first generate its query sketch that is compatible with the underlying structure of the knowledge graph. Then, we complete the query graph by labeling the nodes and edges under the guidance of the structural query pattern. Finally, answers can be retrieved by executing the constructed query graph over the knowledge graph. In order to improve the overall performance of answering questions, we propose the mutual optimization technique. Evaluations on three question-answering benchmarks show that our proposed approach outperforms state-of-the-art methods significantly.