OpenRaiser/Intern-Atlas huggingface dataset:
In2Rec: Influence-based Interpretable Recommendation
Authors: Huafeng Liu, Jingxuan Wen, Liping Jing, Jian Yu, Xiangliang Zhang, Min Zhang
Venue: CIKM
Year: 2019

Interpretability of recommender systems has caused increasing attention due to its promotion of the effectiveness and persuasiveness of recommendation decision, and thus user satisfaction. Most existing methods, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. In this paper, we focus on probabilistic factorization model and further assume the absence of any auxiliary information, such as item content or user review. We propose an influence mechanism to evaluate the importance of the users' historical data, so that the most related users and items can be selected to explain each predicted rating. The proposed method is thus called Influencebased Interpretable Recommendation model (In2Rec). To further enhance the recommendation accuracy, we address the important issue of missing not at random, i.e., missing ratings are not independent from the observed and other unobserved ratings, because users tend to only interact what they like. In2Rec models the generative process for both observed and missing data, and integrates the influence mechanism in a Bayesian graphical model. A learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to maximum a posteriori estimation for In2Rec. A series of experiments on four real-world datasets (Movielens 10M, Netflix, Epinions, and Yelp) have been conducted. By comparing with the state-of-the-art recommendation methods, the experimental results have shown that In2Rec can consistently benefit the recommendation system in both rating prediction and ranking estimation tasks, and friendly interpret the recommendation results with the aid of the proposed influence mechanism.
Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning
Authors: Xiangyang Liu, Bingkun Wei, Fanhua Shang, Hongying Liu
Venue: CIKM
Year: 2019

Stochastic gradient hard thresholding methods have recently been shown to work favorably for solving large-scale empirical risk minimization problems under sparsity constraints. Many stochastic hard thresholding methods (e.g., SVRG-HT) conduct a full gradient update with a constant frequency and perform a hard thresholding operation at each iteration, which leads to a high computational complexity especially for high-dimensional and sparse problems. To be more efficient in large-scale datasets, we propose an efficient single-layer semi-stochastic gradient hard thresholding (LSSG-HT) method. The proposed algorithm updates full gradient with a given probability p and reduces lots of hard thresholding operations by setting frequency m, which reduces hard thresholding complexity in theory to O(κ_s/młog(1/ε)) compared with O(κ_słog(1/ε)) of SVRG-HT. We prove that our algorithm can converge to an optimal solution with a linear convergence rate. Furthermore, we also present an asynchronous parallel variant of LSSG-HT. Numerical experimental results demonstrate that the efficiency of our algorithms with comparison against the state-of-the-art algorithms.
Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks
Authors: Zhining Liu, Dawei Zhou, Jingrui He
Venue: CIKM
Year: 2019

Many complex systems with relational data can be naturally represented as dynamic processes on graphs, with the addition/deletion of nodes and edges over time. For such graphs, network embedding provides an important class of tools for leveraging the node proximity to learn a low-dimensional representation before using the off-the-shelf machine learning models. However, for dynamic graphs, most, if not all, embedding approaches rely on various hyper-parameters to extract spatial and temporal context information, which differ from task to task and from data to data. Besides, many regulated industries (e.g., finance, health care) require the learning models to be interpretable and the output results to meet compliance. Therefore, a natural research question is how we can jointly model the spatial and temporal context information and learn a unique network representation, while being able to provide interpretable inference over the observed data. To address this question, we propose a generic graph attention neural mechanism named STANE, which guides the context sampling process to focus on the crucial part of the data. Moreover, to interpret the network embedding results, STANE enables the end users to investigate the graph context distributions along three dimensions (i.e., nodes, training window length, and time). We perform extensive experiments regarding quantitative evaluation and case studies, which demonstrate the effectiveness and interpretability of STANE.
Hierarchical Community Structure Preserving Network Embedding: A Subspace Approach
Authors: Qingqing Long, Yiming Wang, Lun Du, Guojie Song, Yilun Jin, Wei Lin
Venue: CIKM
Year: 2019

To depict ubiquitous relational data in real world, network data have been widely applied in modeling complex relationships. Projecting vertices to low dimensional spaces, quoted as Network Embedding, would thus be applicable to diverse real-world predicative tasks. Numerous works exploiting pairwise proximities, one characteristic owned by real networks, the clustering property, namely vertices are inclined to form communities of various ranges and hence form a hierarchy consisting of communities, has barely received attention from researchers. In this paper, we propose our network embedding framework, abbreviated SpaceNE, preserving hierarchies formed by communities through subspaces, manifolds with flexible dimensionalities and are inherently hierarchical. Moreover, we propose that subspaces are able to address further problems in representing hierarchical communities, including sparsity and space warps. Last but not least, we proposed constraints on dimensions of subspaces to denoise, which are further approximated by differentiable functions such that joint optimization is enabled, along with a layer-wise scheme to alleviate the overhead cause by the vast number of parameters. We conduct various experiments with results demonstrating our model's effectiveness in addressing community hierarchies.
Synergy of Database Techniques and Machine Learning Models for String Similarity Search and Join
Authors: Jiaheng Lu, Chunbin Lin, Jin Wang, Chen Li
Venue: CIKM
Year: 2019

String data is ubiquitous and string similarity search and join are critical to the applications of information retrieval, data integration, data cleaning, and also big data analytics. To support these operations, many techniques in the database and machine learning areas have been proposed independently. More precisely, in the database research area, there are techniques based on the filtering-and-verification framework that can not only achieve a high performance, but also provide guaranteed quality of results for given similarity functions. In the machine learning research area, string similarity processing is modeled as a problem of identifying similar text records; Specifically, the deep learning approaches use embedding techniques that map text to a low-dimensional continuous vector space. In this tutorial, we review a number of studies of string similarity search and join in these two research areas. We divide the studies in each area into different categories. For each category, we provide a comprehensive review of the relevant works, and present the details of these solutions. We conclude this tutorial by pinpointing promising directions for future work to combine techniques in these two areas.
Temporal Network Embedding with Micro- and Macro-dynamics
Authors: Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye
Venue: CIKM
Year: 2019

Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopic and macroscopic dynamics, are ubiquitous. The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale. Both micro- and macro-dynamics are the key factors to network evolution; however, how to elegantly capture both of them for temporal network embedding, especially macro-dynamics, has not yet been well studied. In this paper, we propose a novel temporal network embedding method with micro- and macro-dynamics, named $\rmM^2DNE $. Specifically, for micro-dynamics, we regard the establishments of edges as the occurrences of chronological events and propose a temporal attention point process to capture the formation process of network structures in a fine-grained manner. For macro-dynamics, we define a general dynamics equation parameterized with network embeddings to capture the inherent evolution pattern and impose constraints in a higher structural level on network embeddings. Mutual evolutions of micro- and macro-dynamics in a temporal network alternately affect the process of learning node embeddings. Extensive experiments on three real-world temporal networks demonstrate that $\rmM^2DNE $ significantly outperforms the state-of-the-arts not only in traditional tasks, e.g., network reconstruction, but also in temporal tendency-related tasks, e.g., scale prediction.
Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding
Authors: Junwei Lu, Chaoqi Yang, Xiaofeng Gao, Liubin Wang, Changcheng Li, Guihai Chen
Venue: CIKM
Year: 2019

Display advertising is a billion dollar business which is the primary income of many companies. In this scenario, real-time bidding optimization is one of the most important problems, where the bids of ads for each impression are determined by an intelligent policy such that some global key performance indicators are optimized. Due to the highly dynamic bidding environment, many recent works try to use reinforcement learning algorithms to train the bidding agents. However, as the probability of the occurrence of a particular state is typically low and the state representation in current work lacks sequential information, the convergence speed and performance of deep reinforcement algorithms are disappointing. To tackle these two challenges in the real-time bidding scenario, we propose ClusterA3C, a novel Advantage Asynchronous Actor-Critic (A3C) variant integrated with a sequential information extraction scheme and a clustering based state aggregation scheme. We conduct extensive experiments to validate the proposed scheme on a real-world commercial dataset. Experimental results show that the proposed scheme outperforms the state of the art methods in terms of either performance or convergence speed.
Best Co-Located Community Search in Attributed Networks
Authors: Jiehuan Luo, Xin Cao, Xike Xie, Qiang Qu
Venue: CIKM
Year: 2019

Various networks have rich attributes such as texts (e.g., tweets) and locations (e.g., check-ins). The community search in such attributed networks have been intensively studied recently due to its wide applications in recommendation, marketing, biology, etc. In this paper, we study the problem of searching the \underlineB est \underlineC o-located \underlineC ommunity (\BCC) in attributed networks, which returns a community that satisfies the following properties: i) structural cohesiveness: members in the community are densely connected, ii) spatial co-location: members are close to each other, and iii) quality optimality: the community has the best quality in terms of given attributes. The problem can be used in social network user behavior analysis, recommendation systems, disease predication, etc. We first propose an index structure called \DTree to integrate the spatial information, the local structure information, and the attribute information together to accelerate the query processing. Then, based on this index we develop an efficient algorithm. The experimental study conducted on both real and synthetic datasets demonstrate the efficiency and effectiveness of the proposed methods.
Set Reconciliation with Cuckoo Filters
Authors: Lailong Luo, Deke Guo, Ori Rottenstreich, Richard T. B. Ma, Xueshan Luo
Venue: CIKM
Year: 2019

Set reconciliation is a common and fundamental task in distributed systems. In many cases, given set A on $Host_A$ and set B on $Host_B$, applications need to identify those elements that appear in set A but not in set B, and vice versa. However, existing methods incur unsatisfactory space utilization and non-trivial false positives and false negatives. In this paper, we present a novel reconciliation method based on Cuckoo filter (CF). After exchanging the CFs each of which represents a set of elements, we query the local elements against the received CF to determine the elements that only belong to the local host and should be transmitted to the other host. The evaluation results indicate that the CF-based reconciliation method outperforms existing methods significantly.
Constrained Co-embedding Model for User Profiling in Question Answering Communities
Authors: Yupeng Luo, Shangsong Liang, Zaiqiao Meng
Venue: CIKM
Year: 2019

In this paper, we study the problem of user profiling in question answering communities. We address the problem by proposing a constrained co-embedding model (CCEM). CCEM jointly infers the embeddings of both users and words in question answering communities such that the similarities between users and words can be semantically measured. Our CCEM works with constraints which enforce the inferred embeddings of users and words subject to this criteria: given a question in the community, embeddings of users whose answers receive more votes are closer to the embeddings of the words occurring in these answers, compared to the embeddings of those whose answers receive less votes. Experiments on a Chinese dataset, Zhihu dataset, demonstrate that our proposed co-embedding algorithm outperforms state-of-the-art methods in the task of user profiling.
Cross-modal Image-Text Retrieval with Multitask Learning
Authors: Junyu Luo, Ying Shen, Xiang Ao, Zhou Zhao, Min Yang
Venue: CIKM
Year: 2019

In this paper, we propose a multi-task learning approach for cross-modal image-text retrieval. First, a correlation network is proposed for relation recognition task, which helps learn the complicated relations and common information of different modalities. Then, we propose a correspondence cross-modal autoencoder for cross-modal input reconstruction task, which helps correlate the hidden representations of two uni-modal autoencoders. In addition, to further improve the performance of cross-modal retrieval, two regularization terms (variance and consistency constraints) are introduced to the cross-modal embeddings such that the learned common information has large variance and is modality invariant. Finally, to enable large-scale cross-modal similarity search, a flexible binary transform network is designed to convert the text and image embeddings into binary codes. Extensive experiments on two benchmark datasets demonstrate that our model has robust superiority over the compared strong baseline methods. Source code is available at \urlhttps://github.com/daerv/DAEVR.
Deep Graph Similarity Learning for Brain Data Analysis
Authors: Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke, Dipanjan Sengupta, Michael W. Cole, Nicholas B. Turk-Browne, Philip S. Yu
Venue: CIKM
Year: 2019

We propose an end-to-end graph similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks. Our proposed framework performs higher-order convolutions by incorporating higher-order proximity in graph convolutional networks to characterize and learn the community structure in brain connectivity networks. To the best of our knowledge, this is the first community-preserving graph similarity learning framework for multi-subject brain network analysis. Experimental results on four real fMRI datasets demonstrate the potential use cases of the proposed framework for multi-subject brain analysis in health and neuropsychiatric disorders. Our proposed approach achieves an average AUC gain of $75$% compared to PCA, an average AUC gain of $65.5$% compared to Spectral Embedding, and an average AUC gain of $24.3$% compared to S-GCN across the four datasets, indicating promising applications in clinical investigation and brain disease diagnosis.
GPU-Accelerated Decoding of Integer Lists
Authors: Antonio Mallia, Michal Siedlaczek, Torsten Suel, Mohamed Zahran
Venue: CIKM
Year: 2019

An inverted index is the basic data structure used in most current large-scale information retrieval systems. It can be modeled as a collection of sorted sequences of integers. Many compression techniques for inverted indexes have been studied in the past, with some of them reaching tremendous decompression speeds through the use of SIMD instructions available on modern CPUs. While there has been some work on query processing algorithms for Graphics Processing Units (GPUs), little of it has focused on how to efficiently access compressed index structures, and we see some potential for significant improvements in decompression speed. In this paper, we describe and implement two encoding schemes for index decompression on GPU architectures. Their format and decoding algorithm is adapted from existing CPU-based compression methods to exploit the execution model and memory hierarchy offered by GPUs. We show that our solutions, GPU-BP and GPU-VByte, achieve significant speedups over their already carefully optimized CPU counterparts.
Intent Term Weighting in E-commerce Queries
Authors: Saurav Manchanda, Mohit Sharma, George Karypis
Venue: CIKM
Year: 2019

E-commerce search engines can fail to retrieve results that satisfy a query's product intent because: (i) conventional retrieval approaches, such as BM25, may ignore the important terms in queries owing to their low "inverse document frequency" " (IDF), and (ii) for long queries, as is usually the case in rare queries (i.e., tail queries), they may fail to determine the relevant terms that are representative of the query's product intent. In this paper, we leverage the historical query reformulation logs of a large e-retailer (walmart.com) to develop a distant-supervision-based approach to identify the relevant terms that characterize the query's product intent. The key idea underpinning our approach is that the terms retained in the reformulation of a query are more important in describing the query's product intent than the discarded terms. Additionally, we also use the fact that the significance of a term depends on its context (other terms in the neighborhood) in the query to determine the term's importance towards the query's product intent. We show that identifying and emphasizing the terms that define the query's product intent leads to a 3% improvement in ranking and outperforms the context-unaware baselines.
Investigating the Learning Process in Job Search: A Longitudinal Study
Authors: Jiaxin Mao, Damiano Spina, Sargol Sadeghi, Falk Scholer, Mark Sanderson
Venue: CIKM
Year: 2019

We investigated the learning process in search by conducting a log-based study involving registered job seekers of a commercial job search engine. The analysis shows that job search is a complex task: seekers usually submit multiple queries over sessions that can last days or even weeks. We find that querying, clicking, and job application rates change over time: job seekers tend to use more filters and a less diverse set of query terms. In terms of click and application behavior, we observed a significant decrease in click rate and query term diversity, as well as an increase in application rates. These trends are found to largely match information seeking models of learning in a complex search task. However, common behaviors are observed in the logs that suggest the existing models may not be sufficient to describe all of the users' learning and seeking processes.
Clustering Recurrent and Semantically Cohesive Program Statements in Introductory Programming Assignments
Authors: Victor J. Marin, Carlos R. Rivero
Venue: CIKM
Year: 2019

Students taking introductory programming courses are typically required to complete assignments and expect timely feedback to advance their learning. With the current popularity of these courses in both traditional and online versions, graders are seeing themselves overwhelmed by the sheer amount of student programs they have to handle, and the quality of the educational experience provided is often compromised for promptness. Thus, there is a need for automated approaches to effectively increase grading productivity. Existing approaches in this context fail to support flexible grading schemes and customization based on the assignment at hand. This paper presents a data-driven approach for clustering recurrent program statements performing similar but not exact semantics across student programs, which we refer to as core statements. We rely on structural graph clustering over the program dependence graph representations of student programs. Such clustering is performed over the graph resulting from the pairwise approximate graph alignments of programs. Core statements help graders understand solution variations at a glance and, since they group program statements present in individual student programs, can be used to propagate feedback, thus increasing grading productivity. Our experimental results show that, on average, we discover core statements covering more than 50% of individual student programs, and that program statements grouped by core statements are semantically cohesive, which ensures effective grading.
Fast Approximations of Betweenness Centrality with Graph Neural Networks
Authors: Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata
Venue: CIKM
Year: 2019

Betweenness centrality is an important measure to find out influential nodes in networks in terms of information spread and connectivity. However, the exact calculation of betweenness centrality is computationally expensive. Although researchers have proposed approximation methods, they are either less efficient, or suboptimal, or both. In this paper, we present a Graph Neural Network(GNN) based inductive framework which uses constrained message passing of node features to approximate betweenness centrality. As far as we know, we are the first to propose a GNN based model to accomplish this task. We demonstrate that our approach dramatically outperforms current techniques while taking less amount of time through extensive experiments on a series of real-world datasets.