OpenRaiser/Intern-Atlas huggingface dataset:
Multiple Exemplars Learning for Fast Image Retrieval
Authors: Tan Yu, Ping Li
Venue: CIKM
Year: 2021

The past decade, we have witnessed rapid progress in compact representation learning for fast image retrieval. In the unsupervised scenario, product quantization (PQ) is one of the promising methods to generate compact image representation for fast and accurate retrieval. Inspired by the great success of deep neural network (DNN) achieved in computer vision, many works attempted to integrate PQ in DNN for end-to-end supervised training. Nevertheless, in existing deep PQ methods, data samples from different classes share the same codebook. Thus, they might be entangled with each other in the feature space. Meanwhile, existing deep PQ methods relying on triplet or pairwise loss require a huge number of training triplets or pairs, which are expensive in computation and scale poorly. In this work, we propose a multiple exemplars learning (MEL) approach to improve retrieval accuracy and training efficiency. For each class, we learn a class-specific codebook consisting of multiple exemplars to partition the class-specific feature space. Since the feature space as well as the codebook is class-specific, samples of different classes are disentangled in the feature space. We incorporate the proposed MEL in a convolutional neural network, supporting end-to-end training. Moreover, we propose MEL loss which trains the network in a considerably more efficient manner than existing deep product quantization approaches based on pairwise or triplet loss. Systematic experiments conducted on two public benchmarks demonstrate the effectiveness and efficiency of our method.
Assorted Attention Network for Cross-Lingual Language-to-Vision Retrieval
Authors: Tan Yu, Yi Yang, Hongliang Fei, Yi Li, Xiaodong Chen, Ping Li
Venue: CIKM
Year: 2021

In this paper, we tackle the cross-lingual language-to-vision (CLLV) retrieval task. In the CLLV retrieval task, given the text query in one language, it seeks to retrieve the relevant images/videos from the database based on visual content in images/videos and their captions in another language. As the CLLV retrieval bridges the modal gap and the language gap, it makes many international cross-modal applications feasible. To tackle the CLLV retrieval, in this paper, we propose an assorted attention network (A2N) to synchronously overcome the language gap, bridge the modal gap and fuse features of two modals in an elegant and effective manner. It represents each text query as a set of word features and represents each image/video as a set of its caption's word features in another language and a set of its local visual features. In this case, the relevance between the text query and the image/video is obtained by the matching between the set of query's word features and two sets of image/video features. To enhance the effectiveness of the matching, A2N merges the query's word features and the image/video's visual and word features into an assorted set and further conducts the self-attention operation on items of the assorted set. On one hand, benefited from the attentions between the query's word features and the video/image's visual features, some important word features or visual features of the image/video can be emphasized. On the other hand, benefited from the attentions between the video/image's visual features and its caption word features, the image/video's visual content and the text information can be fused in a more effective manner. Systematic experiments conducted on four datasets demonstrate the effectiveness of the proposed A2N in the CLLV retrieval task.
Multi-modal Dictionary BERT for Cross-modal Video Search in Baidu Advertising
Authors: Tan Yu, Yi Yang, Yi Li, Lin Liu, Mingming Sun, Ping Li
Venue: CIKM
Year: 2021

Due to their attractiveness, video advertisements are adored by advertisers. Baidu, as one of the leading search advertisement platforms in China, is putting more and more effort into video advertisements for its advertisement customers. Search-based video advertisement display is, in essence, a cross-modal retrieval problem, which is normally tackled through joint embedding methods. Nevertheless, due to the lack of interactions between text features and image features, joint embedding methods cannot achieve as high accuracy as its counterpart based on attention. Inspired by the great success achieved by BERT in NLP tasks, many cross-modal BERT models emerge and achieve excellent performance in cross-modal retrieval. Last year, Baidu also launched a cross-modal BERT, CAN, in video advertisement platform, and achieved considerably better performance than the previous joint-embedding model. In this paper, we present our recent work for video advertisement retrieval, Multi-modal Dictionary BERT (MDBERT) model. Compared with CAN and other cross-modal BERT models, MDBERT integrates a joint dictionary, which is shared among video features and word features. It maps the relevant word features and video features into the same codeword and thus fosters effective cross-modal attention. To support end-to-end training, we propose to soften the codeword assignment. Meanwhile, to enhance the inference efficiency, we adopt the product quantization to achieve fine-level feature space partition at a low cost. After launching MDBERT in Baidu video advertising platform, the conversion ratio (CVR) increases by 3.34%, bringing a considerable revenue boost for advertisers in Baidu.
Storing Multi-model Data in RDBMSs based on Reinforcement Learning
Authors: Gongsheng Yuan, Jiaheng Lu, Shuxun Zhang, Zhengtong Yan
Venue: CIKM
Year: 2021

How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multi-model database that can be compared with existing RDBMSs in handling security, query optimization, transaction management, etc. In this paper, we reconsider employing relational databases to store and query multi-model data. Unfortunately, the mismatch between the complexity of multi-model data structure and the simplicity of flat relational tables makes this difficult. Against this challenge, we utilize the reinforcement learning (RL) method to learn a relational schema by interacting with an RDBMS. Instead of using the classic Q-learning algorithm, we propose a variant Q-learning algorithm, called Double Q-tables, to reduce the dimension of the original Q-table and improve learning efficiency. Experimental results show that our approach could learn a relational schema outperforming the existing multi-model storage schema in terms of query time and space consumption.
Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks
Authors: Jinliang Yuan, Hualei Yu, Meng Cao, Ming Xu, Junyuan Xie, Chongjun Wang
Venue: CIKM
Year: 2021

Graph Neural Networks (GNNs) have achieved significant success in handling graph-structured data, such as knowledge graphs, citation networks, molecular structures, etc. However, most of them are usually shallow structures because of the over-smoothing problem that the representations of nodes are indistinguishable when stacking many layers. Several recent studies have tried to design deep GNNs for powerful expression ability by enlarging the receptive fields to aggregate information from high-order neighbors. But deep models may give rise to overfitting problem. In this paper, we propose a novel insight to aggregate more useful information based on multi-view which does not require deep structures. Specifically, we first design two complementary views to describe global topology and feature similarity of nodes. Then we devise an attention strategy to fuse node representations, named M ulti-V iew G raph C onvolutional N etowrk(MV-GCN). Further, we introduce a self-supervised technique to learn node representations by contrastive learning on different views, which can learn distinctive node embeddings from a large number of unlabeled data, named M ulti-V iew C ontrastive G raph C onvolutional Network(MV-CGC). Finally, we conduct extensive experiments on six public datasets for node classification, which prove the superiority of two proposed models compared with state-of-the-art methods.
Multi-objective Privacy-preserving Text Representation Learning
Authors: Huixin Zhan, Kun Zhang, Chenyi Hu, Victor S. Sheng
Venue: CIKM
Year: 2021

Private information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.
Relation Prediction via Graph Neural Network in Heterogeneous Information Networks with Missing Type Information
Authors: Han Zhang, Yu Hao, Xin Cao, Yixiang Fang, Won-Yong Shin, Wei Wang
Venue: CIKM
Year: 2021

Relation prediction is a fundamental task in network analysis which aims to predict the relationship between two nodes. Thus, this differes from the traditional link prediction problem predicting whether a link exists between a pair of nodes, which can be viewed as a binary classification task. However, in the heterogeneous information network (HIN) which contains multiple types of nodes and multiple relations between nodes, the relation prediction task is more challenging. In addition, the HIN might have missing relation types on some edges and missing node types on some nodes, which makes the problem even harder. In this work, we propose RPGNN, a novel relation prediction model based on the graph neural network (GNN) and multi-task learning to solve this problem. Existing GNN models for HIN representation learning usually focus on the node classification/clustering task. They require the type information of all edges and nodes and always learn a weight matrix for each type, thus requiring a large number of learning parameters on HINs with rich schema. In contrast, our model directly encodes and learns relations in HIN and avoids the requirement of type information during message passing in GNN. Hence, our model is more robust to the missing types for the relation prediction task on HINs. The experiments on real HINs show that our model can consistently achieve better performance than several state-of-the-art HIN representation learning methods.
HORNET: Enriching Pre-trained Language Representations with Heterogeneous Knowledge Sources
Authors: Taolin Zhang, Zerui Cai, Chengyu Wang, Peng Li, Yang Li, Minghui Qiu, Chengguang Tang, Xiaofeng He, Jun Huang
Venue: CIKM
Year: 2021

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the language understanding abilities of deep language models by leveraging the rich semantic knowledge from knowledge graphs, other than plain pre-training texts. However, previous efforts mostly use homogeneous knowledge (especially structured relation triples in knowledge graphs) to enhance the context-aware representations of entity mentions, whose performance may be limited by the coverage of knowledge graphs. Also, it is unclear whether these KEPLMs truly understand the injected semantic knowledge due to the "black-box'' training mechanism. In this paper, we propose a novel KEPLM named HORNET, which integrates Heterogeneous knowledge from various structured and unstructured sources into the Roberta NETwork and hence takes full advantage of both linguistic and factual knowledge simultaneously. Specifically, we design a hybrid attention heterogeneous graph convolution network (HaHGCN) to learn heterogeneous knowledge representations based on the structured relation triplets from knowledge graphs and the unstructured entity description texts. Meanwhile, we propose the explicit dual knowledge understanding tasks to help induce a more effective infusion of the heterogeneous knowledge, promoting our model for learning the complicated mappings from the knowledge graph embedding space to the deep context-aware embedding space and vice versa. Experiments show that our HORNET model outperforms various KEPLM baselines on knowledge-aware tasks including knowledge probing, entity typing and relation extraction. Our model also achieves substantial improvement over several GLUE benchmark datasets, compared to other KEPLMs.
Causally Attentive Collaborative Filtering
Authors: Jingsen Zhang, Xu Chen, Wayne Xin Zhao
Venue: CIKM
Year: 2021

Attention-based recommender models hold the promise of improving performance by learning to discriminate different user/item feature importances. However, due to the existence of the latent confounders, the correlations captured by attention mechanisms may fail to reflect the true influence of the features on the targets (i.e., spurious correlation). In this paper, we propose to empower attention mechanism by the causal inference, which is a powerful tool to identify the real causal effects. Our model is based on the potential outcome framework, where the item features are regarded as the treatment and the outcome is the predicted user preference. In specific, the causal relation of each feature on the outcome is measured by the individual treatment effect (ITE). In order to distill the causal information into the attention learning process, we minimize the distance between the traditional attention weights and the normalized ITE. With such causal regularization, the learned attention weights can capture the real causal effects, which are expected to correct the feature importances for improving performance. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness.
Adversarial Separation Network for Cross-Network Node Classification
Authors: Xiaowen Zhang, Yuntao Du, Rongbiao Xie, Chongjun Wang
Venue: CIKM
Year: 2021

Node classification is an important yet challenging task in various network applications, and many effective methods have been developed for a single network. While for cross-network scenarios, neither single network embedding nor traditional domain adaptation can directly solve the task. Existing approaches have been proposed to combine network embedding and domain adaptation for cross-network node classification. However, they only focus on domain-invariant features, ignoring the individual features of each network, and they only utilize 1-hop neighborhood information (local consistency), ignoring the global consistency information. To tackle the above problems, in this paper, we propose a novel model, Adversarial Separation Network(ASN), to learn effective node representations between source and target networks. We explicitly separate domain-private and domain-shared information. Two domain-private encoders are employed to extract the domain-specific features in each network and a shared encoder is employed to extract the domain-invariant shared features across networks. Moreover, in each encoder, we combine local and global consistency to capture network topology information more comprehensively. ASN integrates deep network embedding with adversarial domain adaptation to reduce the distribution discrepancy across domains. Extensive experiments on real-world datasets show that our proposed model achieves state-of-the-art performance in cross-network node classification tasks compared with existing algorithms.
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder
Authors: Zhenyu Zhang, Tao Guo, Meng Chen
Venue: CIKM
Year: 2021

With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and extract the key entities from the conversational utterances. However, understanding dialogues is regarded as a very challenging task. Different from common language understanding, utterances in dialogues appear alternately from different roles and are usually organized as hierarchical structures. To facilitate the understanding of dialogues, in this paper, we propose a novel contextual dialogue encoder (i.e. DialogueBERT) based on the popular pre-trained language model BERT. Five self-supervised learning pre-training tasks are devised for learning the particularity of dialouge utterances. Four different input embeddings are integrated to catch the relationship between utterances, including turn embedding, role embedding, token embedding and position embedding. DialogueBERT was pre-trained with 70 million dialogues in real scenario, and then fine-tuned in three different downstream dialogue understanding tasks. Experimental results show that DialogueBERT achieves exciting results with 88.63% accuracy for intent recognition, 94.25% accuracy for emotion recognition and 97.04% F1 score for named entity recognition, which outperforms several strong baselines by a large margin.
Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features
Authors: Wang Zhang, Xuan Guo, Ting Pan, Chaochao Liu, Pengfei Jiao, Lin Pan, Wenjun Wang
Venue: CIKM
Year: 2021

Roles of nodes are defined as classes of equivalent nodes. Nodes that have similar local connective patterns may share the same role. As a complementary concept of community, role can also help to recognize real-world entities. For example, it can denote identity or function in social networks. Role has been studied over the past decades, and learning role-based network representations is crucial to many downstream tasks. The important step for role-based network embedding method is extracting features to measure structural similarity instead of proximity. Although some methods have been developed to capture role features to learn structural similarities between nodes, they all design these features of fixed types, such as the global, local, and higher-order features. These features can only represent a certain type of structure, and it is very difficult to model the complex relationship between different scale features in the field of role-based network embedding. Therefore, we propose a novel role-oriented network embedding framework based on adversarial learning between higher-order and local features (ARHOL) to generate powerful role-based node representations. The higher-order features are discrete so we leverage the Auto-Encoder on them to obtain continuous representations. Then we apply the GIN on its outputs to aggregate local information. Finally, we consider the GIN as the generator and design an adversarial game between local features and GIN outputs to integrate these two aspects of features, which can enhance each other and improve the robustness. The extensive experiments on real-world networks demonstrate the superiority and efficiency of our model, and prove the effectiveness of integrating higher-order and local features.
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
Authors: Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin
Venue: CIKM
Year: 2021

With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and beyond groups. Besides, group recommendation suffers seriously from the problem of data sparsity due to severely sparse group-item interactions. In this paper, we propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals: (1) capturing the intra- and inter-group interactions among users; (2) alleviating the data sparsity issue with the raw data itself. Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups. For (2), we design a double-scale node dropout strategy to create self-supervision signals that can regularize user representations with different granularities against the sparsity issue. The experimental analysis on multiple benchmark datasets demonstrates the superiority of the proposed model and also elucidates the rationality of the hypergraph modeling and the double-scale self-supervision.
RxNet: Rx-refill Graph Neural Network for Overprescribing Detection
Authors: Jianfei Zhang, Ai-Te Kuo, Jianan Zhao, Qianlong Wen, Erin L. Winstanley, Chuxu Zhang, Yanfang Ye
Venue: CIKM
Year: 2021

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce Overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. Despite a few machine-learning-based methods that have been proposed for detecting overprescribing, they usually ignore the patient prescribing behavior and their performances are not satisfying. In light of this, we propose a novel model RxNet for overprescribing detection in PDMP. RxNet builds a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various Rx entries (e.g., patients) whose representations are encoded by graph neural network. In addition, to explore the dynamic Rx-refill behavior and medical condition variation of patients, an RxLSTM network is designed to update representations of patients. Based on the output of RxLSTM, a dosing-adaptive network is leveraged to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a 1-year Ohio PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse, with an average of 5.7% and 7.3% improvement on F1 score respectively.