OpenRaiser/Intern-Atlas huggingface dataset:
STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
Authors: Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Jagannadan Varadarajan
Venue: CIKM
Year: 2020

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods.
Neural (Knowledge Graph) Question Answering Using Synthetic Training Data
Authors: Trond Linjordet
Venue: CIKM
Year: 2020

Deep learning requires volume, quality, and variety of training data. In neural question answering, a trade-off between quality and volume comes from the need to either manually curate or construct realistic question answering data, which is costly, or else augmenting, weakly labeling or generating training data from smaller datasets, leading to low variety and sometimes low quality. What can be done to make the best of this necessary trade-off? What can be understood from the endeavor to seek such solutions?
Deep Spatio-Temporal Multiple Domain Fusion Network for Urban Anomalies Detection
Authors: Ruiqiang Liu, Shuai Zhao, Bo Cheng, Hao Yang, Haina Tang, Taoyu Li
Venue: CIKM
Year: 2020

Multiple domain fusion has been widely used for urban anomalies forecasting problem, as urban anomalies such as traffic accidents or illegal assembly are usually caused by many complex factors and they would affect many fields. Although many efforts have been devoted to fusing multiple datasets for anomalies detection, most of the work is to extract the spatio-temporal features one by one from multiple datasets and then fuse to get the result or anomaly score. However, the correlation between data from multiple domains at each moment is ignored, which is especially important when detecting anomalies by analyzing the impacts from multiple datasets. In this paper, we propose a novel end-to-end deep learning based framework, namely deep spatio-temporal multiple domain fusion network to collect the impacts of urban anomalies on multiple datasets and detect anomalies in each region of the city at next time interval in turn. We formulate the problem on a weighted graph and obtain spatiotemporal features with adaptive graph convolution and temporal convolution. In addition, a cross-domain convolution network is applied to fully obtain connection between multiple domains. We evaluate our method with real-world dataset collected in New York City and experiments on our model show the advantages nearly 10% beyond the state-of-the-art urban anomalies detection methods.
Alike and Unlike: Resolving Class Imbalance Problem in Financial Credit Risk Assessment
Authors: Yang Liu, Xiang Ao, Qiwei Zhong, Jinghua Feng, Jiayu Tang, Qing He
Venue: CIKM
Year: 2020

Financial credit risk assessment serves as the impetus to evaluate the credit admission or potential business failure of customers in order to make early actions prior to the actual financial crisis. It aims to predict the probability that a customer may belong to a high-risk group, which is usually formulated as a binary classification problem. However, due to the lack of high-risk samples, the prevailing models suffer from the severe class-imbalance problem. Oversampling those high-risk users could alleviate this problem but the effect of noise examples is also amplified. In this paper, we propose a novel adversarial data augmentation method to solve the class imbalance problem in financial credit risk assessment. We train a generator for synthetic sample generation with a discriminator to identify real or fake instances. Besides, an auxiliary risk discriminator is trained cooperatively with the generator to assess the credit risk. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed
Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items
Authors: Yiding Liu, Yulong Gu, Zhuoye Ding, Junchao Gao, Ziyi Guo, Yongjun Bao, Weipeng Yan
Venue: CIKM
Year: 2020

Inferring substitutable and complementary items is an important and fundamental concern for recommendation in e-commerce websites. However, the item relationships in real-world are usually heterogeneous, posing great challenges to conventional methods that can only deal with homogeneous relationships. More specifically, for this problem, there is a lack of in-depth investigation on 1) decoupling item semantics for modeling heterogeneous item relationships, and at the same time, 2) incorporating mutual influence between different relationships. To fill this gap, we propose a novel solution, namely Decoupled Graph Convolutional Network (DecGCN), to solve the problem of inferring substitutable and complementary items. DecGCN is designed to model item substitutability and complementarity in separated embedding spaces, and is equipped with a two-step integration scheme,where inherent influences between 1) different graph structures and 2) different item semantics are captured. Our experiments on three real-world datasets demonstrate that DecGCN is more effective than the state-of-the-art baselines for the problem at hand. We also conduct offline and online A/B tests on large-scale industrial data, where the results show that DecGCN is effective to be deployed in real-world applications. We release the codes at https://github.com/liuyiding1993/CIKM2020_DecGCN.
Explainable Recommender Systems via Resolving Learning Representations
Authors: Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, Soo-Hyun Choi
Venue: CIKM
Year: 2020

Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.
Fusing Parallel Social Contexts within Flexible-Order Proximity for Microblog Topic Detection
Authors: Hongyu Liu, Ruifang He, Haocheng Wang, Bo Wang
Venue: CIKM
Year: 2020

Topic detection in social media is a challenging task due to large-scale short, noisy and informal nature of messages. Most existing methods only consider textual content or simultaneously model the posts and the first-order structural characteristics of social networks. They ignore the impact of larger neighborhoods in microblog conversations on topics. Moreover, the simple combination of separated content and structure representations fails to capture their nonlinear correlation and different importance in topic inference. To this end, we propose a novel random walk based Parallel Social Contexts Fusion Topic Model (PCFTM) for weibo conversations. Firstly, a user-level conversation network with content information is built by the reposting and commenting relationships among users. Through random walks of different lengths on network, we obtain the user sequences containing the parallel content and structure contexts, which are used to acquire the flexible-order proximity of users. Then we propose a self-fusion network embedding to capture the nonlinear correlation between parallel social contexts. It is achieved by taking the content embedding sequence processed by CNN as the initial value of structure embedding sequence fed to Bi-LSTM. Meanwhile, a user-level self-attention is further used to mine the different importance of users to topics. Lastly, the user sequence embedding is incorporated into neural variational inference for detecting topics, which adaptively balances the intrinsic complementarity between content and structure, and fully uses both local and global social contexts in topic inference. Extensive experiments on three real-world weibo datasets demonstrate the effectiveness of our proposed model.
Dynamic Representation Learning for Large-Scale Attributed Networks
Authors: Zhijun Liu, Chao Huang, Yanwei Yu, Peng Song, Baode Fan, Junyu Dong
Venue: CIKM
Year: 2020

Network embedding, which aims at learning low-dimensional representations of nodes in a network, has drawn much attention for various network mining tasks, ranging from link prediction to node classification. In addition to network topological information, there also exist rich attributes associated with network structure, which exerts large effects on the network formation. Hence, many efforts have been devoted to tackling attributed network embedding tasks. However, they are also limited in their assumption of static network data as they do not account for evolving network structure as well as changes in the associated attributes. Furthermore, scalability is a key factor when performing representation learning on large-scale networks with huge number of nodes and edges. In this work, we address these challenges by developing the DRLAN-Dynamic Representation Learning framework for large-scale Attributed Networks. The DRLAN model generalizes the dynamic attributed network embedding from two perspectives: First, we develop an integrative learning framework with an offline batch embedding module to preserve both the node and attribute proximities, and online network embedding model that recursively updates learned representation vectors. Second, we design a recursive pre-projection mechanism to efficiently model the attribute correlations based on the associative property of matrices. Finally, we perform extensive experiments on three real-world network datasets to show the superiority of DRLAN against state-of-the-art network embedding techniques in terms of both effectiveness and efficiency. The source code is available at: https://github.com/ZhijunLiu95/DRLAN.
Fast Attributed Multiplex Heterogeneous Network Embedding
Authors: Zhijun Liu, Chao Huang, Yanwei Yu, Baode Fan, Junyu Dong
Venue: CIKM
Year: 2020

In recent years, heterogeneous network representation learning has attracted considerable attentions with the consideration of multiple node types. However, most of them ignore the rich set of network attributes (attributed network) and different types of relations (multiplex network), which can hardly recognize the multi-modal contextual signals across different interactions. While a handful of network embedding techniques are developed for attributed multiplex heterogeneous networks, they are significantly limited to the scalability issue on large-scale network data, due to their heavy cost both in computation and memory. In this work, we propose a Fast Attributed Multiplex heterogeneous network Embedding framework (FAME) for large-scale network data, by mapping the units from different modalities (i.e., network topological structures, various node features and relations) into the same latent space in a very efficient way. Our FAME is an integrative architecture with the scalable spectral transformation and sparse random projection, to automatically preserve both attribute semantics and multi-type interactions in the learned embeddings. Extensive experiments on four real-world datasets with various network analytical tasks, demonstrate that FAME achieves both effectiveness and significant efficiency over state-of-the-art baselines. The source code is available at: https://github.com/ZhijunLiu95/FAME.
Active Query of Private Demographic Data for Learning Fair Models
Authors: Yijun Liu, Chao Lan
Venue: CIKM
Year: 2020

Learning a fair prediction model is an important research problem with profound societal impacts. Most approaches assume free access to the sensitive demographic data, whereas these data are becoming restricted to use by privacy regulations. Existing solutions are broadly based on multi-party computation or demographic proxy, but each direction has its own limits in certain scenarios. In this paper, we propose a new direction called active demographic query. We assume sensitive demographic data can be queried with cost, e.g., a company may pay to get a customer's consent on using his private data. Building on Dwork's decoupled fair model, we propose two active query strategies: QmCo queries for the most controversial data maximally disagreed by the decoupled models, and QmRe queries for the most resistant data maximally deteriorating fairness of the current model. In experiment, we show both strategies efficiently improve model fairness on three data sets.
Feature Fusion Based Subgraph Classification for Link Prediction
Authors: Zheyi Liu, Darong Lai, Chuanyou Li, Meng Wang
Venue: CIKM
Year: 2020

Link prediction, which centers on whether or not a pair of nodes is likely to be connected, is a fundamental problem in complex network analysis. Network-embedding-based link prediction has shown strong performance and robustness in previous studies on complex networks, recommendation systems, and knowledge graphs. This approach has certain drawbacks, however; namely, the hierarchical structure of a subgraph is ignored and the importance of different nodes is not distinguished. In this study, we established the Subgraph Hierarchy Feature Fusion (SHFF) model for link prediction. To probe the existence of links between node pairs, the SHFF first extracts a subgraph around the two nodes and learns a function to map the subgraph to a vector for subsequent classification. This reveals any link between the two target nodes. The SHFF learns a function to obtain a representation of the extracted subgraph by hierarchically aggregating the features of nodes in that subgraph, which is accomplished by grouping nodes with similar structures and assigning different importance to the nodes during the feature fusion process. We compared the proposed model against other state-of-the-art link-prediction methods on a wide range of data sets to find that it consistently outperforms them.
An NVM SSD-Optimized Query Processing Framework
Authors: Xinyu Liu, Yu Pan, Yusen Li, Gang Wang, Xiaoguang Liu
Venue: CIKM
Year: 2020

Commercial search engines generally maintain hundreds of thousands of machines equipped with large sized DRAM in order to process huge volume of user queries with fast responsiveness, which incurs high hardware cost since DRAM is very expensive. Recently, NVM Optane SSD has been considered as a promising underlying storage device due to its price advantage over DRAM and speed advantage over traditional slow block devices. However, to achieve a comparable efficiency performance with in-memory index, applying NVM to both latency and I/O bandwidth critical applications such as search engine still faces non-trivial challenges, because NVM has much lower I/O speed and bandwidth compared to DRAM. In this paper, we propose an NVM SSD-optimized query processing framework, aiming to address both the latency and bandwidth issues of using NVM in search engines. Our framework consists of three distinguished properties. First, we propose a pipelined query processing methodology which significantly reduces the I/O waiting time by fine-grained overlapping of the computation and I/O operations. Second, we propose a cache-aware query reordering algorithm which enables queries sharing more data to be processed adjacently so that the I/O traffic is minimized. Third, we propose a data prefetching mechanism which reduces the extra thread waiting time due to data sharing and improves bandwidth utilization. Extensive experimental studies show that our framework significantly outperforms the state-of-the-art baselines, which obtains comparable processing latency and throughput with DRAM while using much less space.
Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation
Authors: Qi Liu, Ruobing Xie, Lei Chen, Shukai Liu, Ke Tu, Peng Cui, Bo Zhang, Leyu Lin
Venue: CIKM
Year: 2020

In tag-enhanced video recommendation systems, videos are attached with some tags that highlight the contents of videos from different aspects. Tag ranking in such recommendation systems provides personalized tag lists for videos from their tag candidates. A better tag ranking model could attract users to click more tags, enter their corresponding tag channels, and watch more tag-specific videos, which improves both tag click rate and video watching time. However, most conventional tag ranking models merely concentrate on tag-video relevance or tag-related behaviors, ignoring the rich information in video-related behaviors. We should consider user preferences on both tags and videos. In this paper, we propose a novel Graph neural network based tag ranking (GraphTR) framework on a huge heterogeneous network with video, tag, user and media. We design a novel graph neural network that combines multi-field transformer, GraphSAGE and neural FM layers in node aggregation. We also propose a neighbor-similarity based loss to encode various user preferences into heterogeneous node representations. In experiments, we conduct both offline and online evaluations on a real-world video recommendation system in WeChat Top Stories. The significant improvements in both video and tag related metrics confirm the effectiveness and robustness in real-world tag-enhanced video recommendation. Currently, GraphTR has been deployed on WeChat Top Stories for more than six months. The source codes are in https://github.com/lqfarmer/GraphTR.
Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks
Authors: Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C. H. Hoi
Venue: CIKM
Year: 2020

Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embedding. In this paper, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the personalization, we propose a locality-aware meta-learning framework, called meta-tail2vec, which learns to learn the regression model for the tail nodes at different localities. Finally, we conduct extensive experiments and demonstrate the promising results of meta-tail2vec. (Supplemental materials including code and data are available at https://github.com/smufang/meta-tail2vec.)
Jointly Modeling Individual Student Behaviors and Social Influence for Prediction Tasks
Authors: Haobing Liu, Yanmin Zhu, Tianzi Zang, Jiadi Yu, Haibin Cai
Venue: CIKM
Year: 2020

Prediction tasks about students such as predicting students' academic performances have practical real-world significance at both the student level and the college level. With the rapid construction of smart campuses, colleges not only offer residence and academic programs but also record students' daily life. The digital footprints provide an opportunity to offer better solutions for prediction tasks. In this paper, we aim to propose a general deep neural network which can jointly model student heterogeneous daily behaviors generated from digital footprints and social influence to deal with prediction tasks. To this end, we design a variant of LSTM and a novel attention mechanism to model the daily behavior sequence. The proposed LSTM is able to consider context information (e.g., weather conditions) while modeling the daily behavior sequence. The proposed attention mechanism can dynamically learn the different importance degrees of different days for every student. Based on behavior information, we propose an unsupervised way to construct a social network to model social influence. Moreover, we design a residual network based decoder to model the complex interactions between the features and get the predicted values such as future academic performances. Qualitative and quantitative experiments on two real-world datasets collected from a college have demonstrated the effectiveness of our model.
Efficiently Training Intelligible Models for Global Explanations
Authors: Yin Lou, Yongliang Wang, Shiwei Liang, Yang Dong
Venue: CIKM
Year: 2020

Generalized additive models (GAMs) are one of the popular methods of building intelligible models on classification and regression problems. Fitting the most accurate GAMs is usually done via gradient boosting with bagged shallow trees. However, such method can be expensive for large industrial applications. In this work, we aim to improve the training efficiency of GAM. To this end, we propose to use subsample aggregating (subagging) in place of bootstrap aggregating (bagging). Our key observation is that subsamples of reasonable size (e.g., 60% of the training set) usually overlap. Such property allows us to explore the computation ordering inside a subagged ensemble and we present a novel algorithm to speed up the computation of subagged ensemble with no loss of accuracy. Our experimental results on public datasets demonstrate that our proposed method can achieve up to 3.7x speedup over bagged ensembles with comparable accuracy. Finally, we demonstrate our methodology of finding global explanations on a real application at Alipay. We have developed several strategies from the findings of those explanations and found those strategies achieved significant lift on key metrics through online experiments.