OpenRaiser/Intern-Atlas huggingface dataset:
Multi-subspace Implicit Alignment for Cross-modal Retrieval on Cooking Recipes and Food Images
Authors: Lin Li, Ming Li, Zichen Zan, Qing Xie, Jianquan Liu
Venue: CIKM
Year: 2021

Cross-modal retrieval technology can help people quickly achieve mutual information between cooking recipes and food images. Both the embeddings of the image and the recipe consist of multiple representation subspaces. We argue that multiple aspects in the recipe are related to multiple regions in the food image. It is challenging to improve the cross-modal retrieval quality by making full use of the implicit connection between multiple subspaces of recipes and images. In this paper, we propose a multi-subspace implicit alignment cross-modal retrieval framework of recipes and images. Our framework learns multi-subspace information about cooking recipes and food images with multi-head attention networks; the implicit alignment at the subspace level promotes narrowing the semantic gap between recipe embeddings and food image embeddings; triple loss and adversarial loss are combined to help our framework for cross-modal learning. The experimental results show that our framework significantly outperforms to state-of-the-art methods in terms of MedR and R@K on Recipe 1M.
Graph-based Semi-Supervised Learning by Strengthening Local Label Consistency
Authors: Chen Li, Xutan Peng, Hao Peng, Jia Wu, Lihong Wang, Philip S. Yu, Jianxin Li, Lichao Sun
Venue: CIKM
Year: 2021

Graph-based algorithms have drawn much attention thanks to their impressive success in semi-supervised setups. For better model performance, previous studies have learned to transform the topology of the input graph. However, these works only focus on optimizing the original nodes and edges, leaving the direction of augmenting existing data insufficiently explored. In this paper, we propose a novel heuristic pre-processing technique, namelyLocal Label Consistency Strengthening (ŁLCS), which automatically expands new nodes and edges to refine the label consistency within a dense subgraph. Our framework can effectively benefit downstream models by substantially enlarging the original training set with high-quality generated labeled data and refining the original graph topology. To justify the generality and practicality of ŁLCS, we couple it with the popular graph convolution network and graph attention network to perform extensive evaluations on three standard datasets. In all setups tested, our method boosts the average accuracy by a large margin of 4.7% and consistently outperforms the state-of-the-art.
Block Access Pattern Discovery via Compressed Full Tensor Transformer
Authors: Xing Li, Qiquan Shi, Gang Hu, Lei Chen, Hui Mao, Yiyuan Yang, Mingxuan Yuan, Jia Zeng, Zhuo Cheng
Venue: CIKM
Year: 2021

The discovery and prediction of block access patterns in hybrid storage systems is of crucial importance for effective tier management. Existing methods are usually based on heuristics and unable to handle complex patterns. This work newly introduces transformer to block access pattern prediction. We remark that block accesses in the tier management systems are aggregated temporally and spatially as multivariate time series of block access frequency, so the runtime requirements are relaxed, making complex models applicable for the deployment. Moreover, enormous and rarely accessed blocks in storage systems and the structure of traditional transformer models would result in millions of redundant parameters and make them impractical to be deployed. We incorporate Tensor-Train Decomposition (TTD) with transformer and propose the Compressed Full Tenor Transformer (CFTT), in which all linear layers in the vanilla transformer are replaced with tensor-train layers. Weights of input and output layers are shared to further reduce parameters and reuse knowledge implicitly. CFTT can significantly reduce the model size and computation cost, which is critical to save storage space and inference time. Extensive experiments are conducted on synthetic and real-world datasets. The results demonstrate that transformers achieve state-of-the-art performance stably in terms of top-k hit rates. Moreover, the proposed CFTT compresses transformers 16× to 461× and speeds up inference 5× without sacrificing performance on the whole, which facilitates its applications in tier management in hybrid storage systems.
Hubness-aware User Identity Linkage
Authors: Chaozhuo Li, Senzhang Wang, Feiran Huang, Jie Xu, Philip S. Yu
Venue: CIKM
Year: 2021

Nowadays, it is common for one natural person to join multiple social networks to enjoy different types of services. User identity linkage (UIL), which aims to link identical identities across different social platforms, has attracted increasing research interests recently. Most existing approaches focus on the sophisticated architecture engineering of the linkage model but ignore the challenge of hubness in the post-processing nearest neighbor search phase. Hubness appears as some identities in a social platform, called hubs, being extra-ordinary close to the identities in the other platform, which will degrade the alignment performance. Different from existing heuristic methods, in this paper we propose a hubness-aware user identity linkage model HAUIL to smoothly learn hubless linkage signals. A carefully-designed objective function is presented to explicitly mitigate the hubness information from the pre-learned linkage guidance. HAUIL can be easily adapted to most existing UIL models. Empirically, we evaluate HAUIL over multiple publicly available datasets, and the experimental results demonstrate its superiority.
Unsupervised Categorical Representation Learning for Package Arrival Time Prediction
Authors: Yang Li, Xingyu Wu, Jinglong Wang, Yong Liu, Xiaoqing Wang, Yuming Deng, Chunyan Miao
Venue: CIKM
Year: 2021

Estimated Time of package Arrival (ETA) is an essential task for Alibaba E-commerce platforms like Taobao and Tmall, which may influence the user experiences of one billion customers. The main challenge in ETA prediction of Alibaba platforms is learning from high-dimensional categorical attributes, which is equally important to obtain appropriate representations for each feature, and describe the proximity among them. Although recent supervised end-to-end methods have achieved great improvements, the unsupervised embedding method for categorical attributes has not been well-studied yet, especially when dealing with large-scale sparse datasets. In this paper, we propose Bayesian Graph Embedding (BGE) to learn dense representations for high-dimensional categorical attributes in an unsupervised way. Ignited by the idea of Bayesian network and graph embedding, we design an unsupervised algorithm to absorb the knowledge of prior dependencies and unobserved attributes, which is ignored by end-to-end methods. A joint optimization objective is raised to mine the proximity between categorical attributes, which achieves a consistency with Bayesian network. Moreover, a multi-tower model architecture is put forward for the multi-task learning of the joint objective, based on which the dense representations of categorical attributes can be well-exploited. The produced embeddings can be applied in downstream tasks such as regression and classification with further fine-tuning. Extensive experiments have been conducted on two datasets with more than two million samples, collected from Alibaba real production environment. The experimental results demonstrate the proposed approach outperforms both supervised and unsupervised baseline methods in the effectiveness and efficiency of E-commerce ETA prediction task.
From Community Search to Community Understanding: A Multimodal Community Query Engine
Authors: Zhao Li, Pengcheng Zou, Xia Chen, Shichang Hu, Peng Zhang, Yumou Zhang, Bingsheng He, Yuchen Li, Xing Tang
Venue: CIKM
Year: 2021

In this demo, we present an online multi-modal community query engine (MQE) on Alibaba's billion-scale heterogeneous network. MQE has two distinct features in comparison with existing community query engines. Firstly, MQE supports multimodal community search on heterogeneous graphs with keyword and image queries. Secondly, to facilitate community understanding in real business scenarios, MQE generates natural language descriptions for the retrieved community in combination with other useful demographic information. The distinct features of MQE benefit many downstream applications in Alibaba's e-commerce platform like recommendation. Our experiments confirm the effectiveness and efficiency of MQE on graphs with billions of edges.
Learning to Bundle Proactively for On-Demand Meal Delivery
Authors: Chengbo Li, Lin Zhu, Guangyuan Fu, Longzhi Du, Canhua Zhao, Tianlun Ma, Chang Ye, Pei Lee
Venue: CIKM
Year: 2021

On-demand meal delivery (ODMD) platforms such as DoorDash and Ele.me have experienced explosive growth in recent years. Effective logistics optimization strategies that could guarantee high service standards with controlled costs are crucial for the long-term sustainability of these platforms, and yet are also non-trivial due to the nature of ODMD operations. In particular, most of the orders are not known until they are placed by the customers, and any dispatching policy that only considers known requests would risk making myopic decisions in such a setting. In this paper, we propose a novel approach to address this problem. At the core of our method is a learning-based metric called Proactive Bundle Cost Vector (PBCV), which quantifies the easiness of bundling a particular order with future orders. Based on PBCV, we build a proactive bundling policy that that considers the viability of serving unknown requests. Extensive online A/B tests demonstrate that the resultant policy has shown significant improvements of key performance metrics over baseline policies. Our solution has been successfully deployed at one of the world's largest ODMD platforms, serving tens of millions of customers on a daily basis.
Failure Prediction for Large-scale Water Pipe Networks Using GNN and Temporal Failure Series
Authors: Shuming Liang, Zhidong Li, Bin Liang, Yu Ding, Yang Wang, Fang Chen
Venue: CIKM
Year: 2021

Pipe failure prediction in the water industry aims to prioritize the pipes that are at high risk of failure for proactive maintenance. However, existing statistical or machine learning models that rely on historical failures and asset attributes can hardly leverage the structure information of pipe networks. In this work, we develop a failure prediction framework for pipe networks by jointly considering the pipes' features, the network structure, the geographical neighboring effect, and the temporal failure series. We apply a multi-hop Graph Neural Network (GNN) to failure prediction. We propose a method of constructing a geographical graph structure depending on not only the physical connections but also geographical distances between pipes. To differentiate the pipes with diverse properties, we employ an attention mechanism in the neighborhood aggregation process of each GNN layer. Also, residual connections and layer-wise aggregation are used to avoid the over-smoothing issue in deep GNNs. The historical failures exhibit a strong temporal pattern. Inspired by point process, we develop a module to learn the pipes' evolutionary effect and the time-decayed excitement of historical failures on the current state of the pipe. The proposed framework is evaluated on two real-world large-scale pipe networks. It outperforms the existing statistical, machine learning, and state-of-the-art GNN baselines. Our framework provides the water utility with core data-driven support for proactive maintenance including regular pipe inspection, pipe renewal planning, and sensor system deployment. It can be extended to other infrastructure networks in the future.
Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning
Authors: Bin Liang, Wangda Luo, Xiang Li, Lin Gui, Min Yang, Xiaoqi Yu, Ruifeng Xu
Venue: CIKM
Year: 2021

Most existing aspect-based sentiment analysis (ABSA) research efforts are devoted to extracting the aspect-dependent sentiment features from the sentence towards the given aspect. However, it is observed that about 60% of the testing aspects in commonly used public datasets are unknown to the training set. That is, some sentiment features carry the same polarity regardless of the aspects they are associated with (aspect-invariant sentiment), which props up the high accuracy of existing ABSA models when inevitably inferring sentiment polarities for those unknown testing aspects. Therefore, in this paper, we revisit ABSA from a novel perspective by deploying a novel supervised contrastive learning framework to leverage the correlation and difference among different sentiment polarities and between different sentiment patterns (aspect-invariant/-dependent). This allows improving the sentiment prediction for (unknown) testing aspects in the light of distinguishing the roles of valuable sentiment features. Experimental results on 5 benchmark datasets show that our proposed approach substantially outperforms state-of-the-art baselines in ABSA. We further extend existing neural network-based ABSA models with our proposed framework and achieve improved performance.
Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding
Authors: Zhehan Liang, Yu Rong, Chenxin Li, Yunlong Zhang, Yue Huang, Tingyang Xu, Xinghao Ding, Junzhou Huang
Venue: CIKM
Year: 2021

Nowadays, it is common for a person to possess different identities on multiple social platforms. Social network alignment aims to match the identities that from different networks. Recently, unsupervised network alignment methods have received significant attention since no identity anchor is required. However, to capture the relevance between identities, the existing unsupervised methods generally rely heavily on user profiles, which is unobtainable and unreliable in real-world scenarios. In this paper, we propose an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile. The embedding module of LSNA, named Cross Network Embedding Model (CNEM), aims to integrate the topology information and the network correlation to simultaneously guide the embedding process. Moreover, in order to adapt LSNA to large-scale networks, we propose a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems. The proposed method is evaluated over multiple real-world social network datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.
GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation
Authors: Xuming Lin, Shaobo Cui, Zhongzhou Zhao, Wei Zhou, Ji Zhang, Haiqing Chen
Venue: CIKM
Year: 2021

Existing data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generation in the commercial scenario, these methods may generate illogical and uncontrollable texts. To address these aforementioned issues, we propose a graph-based grouping planner~(GGP) following the idea of first-plan-then-generate. Specifically, given a collection of key phrases, GGP firstly encodes these phrases into a instance-level sequential representation and a corpus-level graph-based representation separately. With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text. We conduct our experiments on three long text generation datasets and the experimental results reveal that GGP significantly outperforms baselines, which proves that GGP can control the long text generation with knowing how to say and in what order.
Grammatical Error Correction with Dependency Distance
Authors: Haowen Lin, Jinlong Li, Xu Zhang, Huanhuan Chen
Venue: CIKM
Year: 2021

Grammatical Error Correction (GEC) task is always considered as low resource machine translation task which translates a sentence in an ungrammatical language to a grammatical language. As the state-of-the-art approach to GEC task, transformer-based neural machine translation model takes input sentence as a token sequence without sentence's structure information, and may be misled by some strange ungrammatical contexts. In response, to lay more attention on a given token's correct collocation rather than the misleading tokens, we propose dependent self-attention to relatively increase the attention score between correct collocations according to the dependency distance between tokens. However, as the source sentence is ungrammatical in GEC task, the correct collocations can hardly be extracted by normal dependency parser. Therefore, we propose dependency parser for ungrammatical sentence to get the dependency distance between tokens in the ungrammatical sentence. Our method achieves competitive results on both BEA-2019 shared task, CoNLL-2014 shared task and JFLEG test sets.
Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic
Authors: Chung-Yi Lin, Hung-Ting Su, Shen-Lung Tung, Winston H. Hsu
Venue: CIKM
Year: 2021

Spatial-temporal prediction is a critical problem for intelligent transportation, which is helpful for tasks such as traffic control and accident prevention. Previous studies rely on large-scale traffic data collected from sensors. However, it is unlikely to deploy sensors in all regions due to the device and maintenance costs. This paper addresses the problem via outdoor cellular traffic distilled from over two billion records per day in a telecom company, because outdoor cellular traffic induced by user mobility is highly related to transportation traffic. We study road intersections in urban and aim to predict future outdoor cellular traffic of all intersections given historic outdoor cellular traffic. Furthermore, we propose a new model for multivariate spatial-temporal prediction, mainly consisting of two extending graph attention networks (GAT). First GAT is used to explore correlations among multivariate cellular traffic. Another GAT leverages the attention mechanism into graph propagation to increase the efficiency of capturing spatial dependency. Experiments show that the proposed model significantly outperforms the state-of-the-art methods on our dataset.
ECMA: An Efficient Convoy Mining Algorithm for Moving Objects
Authors: Yiyang Liu, Hua Dai, Bohan Li, Jiawei Li, Geng Yang, Jun Wang
Venue: CIKM
Year: 2021

With the popularity of mobile devices equipped with positioning devices, it is convenient to obtain enormous amounts of trajectory data. The development promotes the study of extracting moving patterns from trajectory data of moving objects. One such pattern is the convoy, which refers to a group of objects moving together for a period of time. The existing convoy mining algorithms have a large time cost because they adopt a density-based clustering algorithm over global objects. In this paper, we propose an efficient convoy mining algorithm (ECMA) that adopts the divide-and-conquer methodology. A block-based partition model (BP-Model) is designed to divide objects into multiple maximized connected nonempty block areas (MOBAs). The convoy mining problem is then solved by processing each MOBA sequentially, which significantly reduces the time cost of convoy mining. In the experiments, we evaluate the performance of our algorithm on real-world datasets. The results show that the ECMA is more efficient than existing convoy mining algorithms.
A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network
Authors: Yi Liu, Bohan Li, Yalei Zang, Aoran Li, Hongzhi Yin
Venue: CIKM
Year: 2021

Sequential recommendation systems seek to learn users' preferences to predict their next actions based on the items engaged recently. Static behavior of users requires a long time to form, but short-term interactions with items usually meet some actual needs in reality and are more variable. RNN-based models are always constrained by the strong order assumption and are hard to model the complex and changeable data flexibly. Most of the CNN-based models are limited to the fixed convolutional kernel. All these methods are suboptimal when modeling the dynamics of item-to-item transitions. It is difficult to describe the items with complex relations and extract the fine-grained user preferences from the interaction sequence. To address these issues, we propose a knowledge-aware sequential recommender with the attention-enhanced dynamic convolutional network (KAeDCN). Our model combines the dynamic convolutional network with attention mechanisms to capture changing dependencies in the sequence. Meanwhile, we enhance the representations of items with Knowledge Graph (KG) information through an information fusion module to capture the fine-grained user preferences. The experiments on four public datasets demonstrate that KAeDCN outperforms most of the state-of-the-art sequential recommenders. Furthermore, experimental results also prove that KAeDCN can enhance the representations of items effectively and improve the extractability of sequential dependencies.
SCMGR: Using Social Context and Multi-Granularity Relations for Unsupervised Social Summarization
Authors: Huanyu Liu, Ruifang He, Liangliang Zhao, Haocheng Wang, Ruifang Wang
Venue: CIKM
Year: 2021

Social summarization aims to produce a concise summary that describes the core content of a collection of posts on a specific topic. Existing methods tend to produce sparse or ambiguous representations of posts due to only using short and informal text content. Latest researches use social relations to improve diversity of summaries, yet they model social relations as a regularization item, which has poor flexibility and generalization. Those methods could not embody the deep semantic and social interactions among posts, making summaries still suffer from redundancy. We propose to use Social Context and Multi-Granularity Relations (SCMGR) to improve unsupervised social summarization. It learns more informative representations of posts considering both text semantics and social structure information without any annotated data. First, we design two sociologically motivated meta-paths to construct a social context graph among posts, and adopt a graph convolutional network to aggregate social context information from neighbors. Second, we design a multi-granularity relation decoder to capture the deeper semantic and social interactions from post-word and post-post aspects respectively, which can provide guidance for summary selection from semantic and social structure perspectives. Finally, a sparse reconstruction-based extractor is used to select posts that can best reconstruct original content and social network structure as summaries. Our approach improves the coverage and diversity of summaries. Experimental results on both English and Chinese corpora prove the effectiveness of our model.