OpenRaiser/Intern-Atlas huggingface dataset:
PAS: Privacy Algorithms in Systems
Authors: Philip S. Yu, Olivera Kotevska, Tyler Derr
Venue: CIKM
Year: 2022

Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.
Multi-scale Multi-modal Dictionary BERT For Effective Text-image Retrieval in Multimedia Advertising
Authors: Tan Yu, Jie Liu, Zhipeng Jin, Yi Yang, Hongliang Fei, Ping Li
Venue: CIKM
Year: 2022

Visual content in multimedia advertising effectively attracts the customer's attention. Search-based multimedia advertising is a cross-modal retrieval problem. Due to the modal gap between texts and images/videos, cross-modal image/video retrieval is a challenging problem. Recently, multi-modal dictionary BERT has bridged the model gap by unifying the images/videos and texts from different modalities through a multi-modal dictionary. In this work, we improve the multi-modal dictionary BERT by developing a multi-scale multi-modal dictionary and propose a Multi-scale Multi-modal Dictionary BERT (M^2D-BERT). The multi-scale dictionary partitions the feature space into different levels and is effective in describing the fine-level relevance and the coarse-level relevance between the text and images. Meanwhile, we constrain that the code-words in dictionaries from different scales to be orthogonal to each other. Thus, it ensures multiple dictionaries are complementary to each other. Moreover, we adopt a two-level residual quantization to enhance the capacity of each multi-modal dictionary. Systematic experiments conducted on large-scale cross-modal retrieval datasets demonstrate the excellent performance of our M2D-BERT.
The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation
Authors: Ruiyun Yu, Kang Yang, Bingyang Guo
Venue: CIKM
Year: 2022

Deep learning-based recommendation systems have made significant strides in recent years. However, the problem of recommendation systems' generalizability has not been solved. After the training phase, most current models can only solve problems on a particular dataset and are not as generalizable as NLP and CV models. Therefore, a large amount of computing power is required to make conventional recommendation models available to different trades. In real-world scenarios, offline retailers often opt out of recommendation algorithms due to a lack of computer capacity, which puts them at a competitive disadvantage. As a result, we propose an Interaction Graph Auto-encoder Network (IGA) based on topology-aware to address the transferable recommendation problem. IGA is composed primarily of the following components: Interaction Feature Subgraph Extraction, Subgraph Node Labeling, Subgraph Interaction Auto-encoder, and Interaction Preference Attention Network. IGA can transfer knowledge from the training dataset to the new dataset without fine-tuning and give users reliable, personalized recommendation results. Experiments on the MovieLens, Douban, LastFM, and Book-Crossing datasets demonstrate that IGA outperforms state-of-the-art approaches in transferable scenarios. Additionally, IGA requires fewer computing power and is highly adaptable across datasets.
An Actor-critic Reinforcement Learning Model for Optimal Bidding in Online Display Advertising
Authors: Congde Yuan, Mengzhuo Guo, Chaoneng Xiang, Shuangyang Wang, Guoqing Song, Qingpeng Zhang
Venue: CIKM
Year: 2022

The real-time bidding (RTB) paradigm allows the advertisers to submit a bid for each impression in online display advertising. A usual demand of the advertisers is to maximize the total value of winning impressions under constraints on some key performance indicators. Unfortunately, the existing RTB research in industrial applications can hardly achieve the optimum due to the stochastic decision scenarios and complex consumer behaviors. In this study, we address the application of RTB to mobile gaming where the in-app purchase action is of high uncertainty, making it challenging to evaluate individual impression opportunities. We first formulate the bidding process into a constrained optimization problem and then propose an actor-critic reinforcement learning (ACRL) model for obtaining the optimal policy under a dynamic decision environment. To avoid feeding too many samples with zero labels to the model, we provide a new way to quantify impression opportunities by integrating the in-app actions, such as conversion and purchase, and the characteristics of the candidate ad inventories. Moreover, the proposed ACRL learns a Gaussian distribution to simulate the audience's decision in a more real bidding scenario by taking additional contextual side information about both media and the audience. We also introduce how to deploy the learned model online to help adjust the final bid. At last, we conduct comprehensive offline experiments to demonstrate the effectiveness of ACRL and carefully set an online A/B testing experiment. The online experimental results verify the efficacy of the proposed ACRL in terms of multiple critical commercial indicators. ACRL has been deployed in the Tencent online display advertising platform and impacts billions of traffic every day. We believe proposed modifications for optimal bidding problems in RTB are practically innovative and can inspire the relative works in this field.
Interactive Contrastive Learning for Self-Supervised Entity Alignment
Authors: Kaisheng Zeng, Zhenhao Dong, Lei Hou, Yixin Cao, Minghao Hu, Jifan Yu, Xin Lv, Lei Cao, Xin Wang, Haozhuang Liu, Yi Huang, Junlan Feng, Jing Wan, Juanzi Li, Ling Feng
Venue: CIKM
Year: 2022

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the-art (SOTA) self-supervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, it advocates the minimum information requirement for self-supervised EA, while we argue that self-described KG's side information (e.g., entity name, relation name, entity description) shall preferably be explored to the maximum extent for the self-supervised EA task. In this work, we propose an interactive contrastive learning model for self-supervised EA. It conducts bidirectional contrastive learning via building pseudo-aligned entity pairs as pivots to achieve direct cross-KG information interaction. It further exploits the integration of entity textual and structural information and elaborately designs encoders for better utilization in the self-supervised setting. Experimental results show that our approach outperforms the previous best self-supervised method by a large margin (over 9% Hits@1 absolute improvement on average) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA. The code and data are available at https://github.com/THU-KEG/ICLEA.
Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce
Authors: Zhiyuan Zeng, Yuzhi Huang, Tianshu Wu, Hongbo Deng, Jian Xu, Bo Zheng
Venue: CIKM
Year: 2022

Product searching is fundamental in online e-commerce systems, it needs to quickly and accurately find the products that users required. Relevance is essential for e-commerce search, which role is avoiding displaying products that do not match search intent and optimizing user experience. Measuring semantic relevance is necessary because distributional biases between search queries and product titles may lead to large lexical differences between relevant textual expressions. Several problems limit the performance of semantic relevance learning, including extremely long-tail product distribution and low-quality labeled data. Recent works attempt to conduct relevance learning through user behaviors. However, noisy user behavior can easily cause inadequately semantic modeling. Therefore, it is valuable but challenging to utilize user behavior in relevance learning. In this paper, we first propose a weakly supervised contrastive learning framework that focuses on how to provide effective semantic supervision and generate reasonable representation. We utilize topology structure information contained in a user behavior heterogeneous graph to design a semantically aware data construction strategy. Besides, we propose a contrastive learning framework suitable for e-commerce scenarios with targeted improvements in data augmentation and training objectives. For relevance calculation, we propose a novel hybrid method that combines fine-tuning and transfer learning. It eliminates the negative impacts caused by distributional bias and guarantees semantic matching capabilities. Extensive experiments and analyses show the promising performance of proposed methods in relevance learning.
Joint Clothes Detection and Attribution Prediction via Anchor-free Framework with Decoupled Representation Transformer
Authors: Fankai Zeng, Mingbo Zhao, Zhao Zhang, Shanchuan Gao, Lu Cheng
Venue: CIKM
Year: 2022

Clothes attribution prediction is the key technology for users to automatically describe clothing characteristics. Most current methods are first to detect the multiple clothes, and then crop out the clothes and feed to a certain network for clothes attribution prediction. But this two-stage approach is time- and resource- consuming; on the other hand, one-stage approach can provide an effective and efficient solution by integrating clothes detection and attribution prediction into an end-to-end framework. But the one-stage approach tends to explore anchor-based detectors causing high sensitivity to the hyperparameters and high computational complexity from dense anchors. In addition, it may also confront with optimization contradiction problem in the training procedure, as the clothes detection and attribution prediction branches demand diverse optimization. In this work, to handle the above problems, we aim to develop an end-to-end anchor-free framework by involving an additional branch for joint clothes detection and attribution prediction. To handle the optimization contradiction in two branches, we encode the backbone feature map as pixel-level dense queries and decode them via deformable transformer as the output features that are fed into detection and prediction branches, respectively. In this way, the features of detection and prediction branches can be decoupled and the optimization contradiction can be naturally solved. To further enhance the prediction accuracy, we in the prediction branch also develop a special attention strategy and loss function to adaptively integrate the peer attribution relationships into feature learning as well as to avoid mutual suppression for hierarchical attributions. Extensive simulation results verify the effectiveness of the proposed work.
The SimIIR 2.0 Framework: User Types, Markov Model-Based Interaction Simulation, and Advanced Query Generation
Authors: Saber Zerhoudi, Sebastian Günther, Kim Plassmeier, Timo Borst, Christin Seifert, Matthias Hagen, Michael Granitzer
Venue: CIKM
Year: 2022

Simulated user retrieval system interactions enable studies with controlled user behavior. To this end, the SimIIR framework offers static, rule-based methods. We present an extended SimIIR 2.0 version with new components for dynamic user type-specific Markov model-based interactions and more realistic query generation. A flexible modularization ensures that the SimIIR 2.0 framework can serve as a platform to implement, combine, and run the growing number of proposed search behavior and query simulation ideas.
Look Twice as Much as You Say: Scene Graph Contrastive Learning for Self-Supervised Image Caption Generation
Authors: Chunhui Zhang, Chao Huang, Youhuan Li, Xiangliang Zhang, Yanfang Ye, Chuxu Zhang
Venue: CIKM
Year: 2022

Images are commonly used for various information and knowledge applications, such as advertising and recommendation. Automating image caption generation will significantly improve image accessibility. This cross-modal task, which takes image as input and text as output, however, is difficult for learning. Though prior methods achieve good performance for image caption generation, they rely on either supervised learning which requires sufficient labeled data or unsupervised learning which needs external dataset as language pivot. In this paper, we propose SGCL, a novel Scene Graph Contrastive Learning model for self-supervised image caption generation. SGCL adopts the pre-training and fine-tuning pipeline. Specifically, we first apply scene graph generation and objection detection method to encode scene graph and visual information in the image as feature representation. Later, a decoder network based on graph attention network and recurrent neural network is further designed to generate sequential text as caption. To enable contrastive learning in SGCL, we design scene graph augmentations as contrastive views of images and train the model effectively without ground-truth labels through contrastive learning. Additionally, we introduce the pre-trained word embedding and the context projector to enrich the text representation in the decoder network, which benefits model pre-training. Once the pre-training phase is finished, we further fine-tune the model for the image caption generation task with limited labeled data. Extensive experiments on benchmark dataset demonstrate that SGCL outperforms state-of-the-art models (both supervised and unsupervised).
Deep Contrastive Multiview Network Embedding
Authors: Mengqi Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang
Venue: CIKM
Year: 2022

Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive learning approaches have shown promising performance in this task. However, they neglect the semantic consistency between fused and view representations and have difficulty in modeling complementary information between different views. To deal with these deficiencies, this work presents a novel Contrastive leaRning framEwork for Multiview network Embedding (CREME). In our work, different views can be obtained based on the various relations among nodes. Then, we generate view embeddings via proper view encoders and utilize an attentive multiview aggregator to fuse these representations. Particularly, we design two collaborative contrastive objectives, view fusion InfoMax and inter-view InfoMin, to train the model in a self-supervised manner. The former objective distills information from embeddings generated from different views, while the latter captures complementary information among views to promote distinctive view embeddings. We also show that the two objectives can be unified into one objective for model training. Extensive experiments on three real-world datasets demonstrate that our proposed CREME is able to consistently outperform state-of-the-art methods.
Handling RDF Streams: Harmonizing Subgraph Matching, Adaptive Incremental Maintenance, and Matching-free Updates Together
Authors: Qianzhen Zhang, Deke Guo, Xiang Zhao, Lailong Luo
Venue: CIKM
Year: 2022

RDF stream processing (RSP) has become a vibrant area of research in the Semantic Web community, which guarantees interoperability and opens up important applications. There have been efforts to extend RDF data and SPARQL query for representing streaming information and continuous querying functionalities. However, existing solutions will incur significant low throughput due to the recomputation of the results from scratch as the window slides. In this paper, we propose a novel graph-based framework, referred as IncTreeRDF, towards continuous SPARQL query evaluation over RDF data streams. Under the framework, the RDF data streams are modeled as streaming graphs; the SPARQL queries are translated into graph patterns and evaluated via continuous sub-graph pattern-matching over streaming RDF graphs. IncTreeRDF employs a query-centric auxiliary data structure called TStore to store some intermediate results, which supports fast incremental maintenance. Based on TStore, we can not only avoid re-computing matches of the query but also prune invalid updates. Besides, we define matching-free update, in which subgraph matching calculation can be avoided under this scenario. Extensive experimental results show that IncTreeRDF significantly outperforms existing competitors.
Disentangled Representation for Long-tail Senses of Word Sense Disambiguation
Authors: Junwei Zhang, Ruifang He, Fengyu Guo, Jinsong Ma, Mengnan Xiao
Venue: CIKM
Year: 2022

The long-tailed distribution, also called the heavy-tailed distribution, is common in nature. Since both words and their senses in natural language have long-tailed phenomenon in usage frequency, the Word Sense Disambiguation (WSD) task faces serious data imbalance. The existing learning strategies or data augmentation methods are difficult to deal with the lack of training samples caused by the single application scenario of long-tail senses, and the word sense representations caused by unique word sense definitions. Considering that the features extracted from the Disentangled Representation (DR) independently describe the essential properties of things, and DR does not require deep feature extraction and fusion processes, it alleviates the dependence of the representation learning on the training samples. We propose a novel DR by constraining the covariance matrix of a multivariate Gaussian distribution, which can enhance the strength of independence among features compared to β-VAE. The WSD model implemented by the reinforced DR outperforms the baselines on the English all-words WSD evaluation framework, the constructed long-tail word sense datasets, and the latest cross-lingual datasets.
Selectively Expanding Queries and Documents for News Background Linking
Authors: Lirong Zhang, Hideo Joho, Sumio Fujita, Hai-Tao Yu
Venue: CIKM
Year: 2022

Background articles are crucial for readers to grasp the context of news stories fully. However, existing approaches of background article search tend to apply a single ranking method to all types of search topics. In this paper, we focus on exploring search topics on news articles by classifying them into two types:time-sensitive andnon-time-sensitive. To verify whether or not these two types of search topics can benefit from different retrieving methods, we examined a suite of strategies such as document expansion, query rewriting, and semantic re-ranking. Moreover, the relationship between background articles and topics is verified by the two strategies of document expansion (specificity and diversity). The experimental results demonstrate that the optimal usage of the aforementioned strategies is indeed different between the two types of search topics. Furthermore, our in-depth analysis of topics and search results verified that: time-sensitive topics benefit from background articles that can provide more specific knowledge, while non-time-sensitive topics benefit from diversified retrieved documents.
Control-based Bidding for Mobile Livestreaming Ads with Exposure Guarantee
Authors: Haoqi Zhang, Junqi Jin, Zhenzhe Zheng, Fan Wu, Haiyang Xu, Jian Xu
Venue: CIKM
Year: 2022

Mobile livestreaming ads are becoming a popular approach for brand promotion and product marketing. However, a large number of advertisers fail to achieve their desired advertising performance due to the lack of ad exposure guarantee in the dynamic advertising environment. In this work, we propose a bidding-based ad delivery algorithm for mobile livestreaming ads that can provide advertisers with bidding strategies for optimizing diverse marketing objectives under general ad performance guaranteed constraints, such as ad exposure and cost-efficiency constraints. By modeling the problem as an online integer programming and applying primal-dual theory, we can derive the bidding strategy from solving the optimal dual variables. The initialization of the dual variables is realized through a deep neural network that captures the complex relation between dual variables and dynamic advertising environments. We further propose a control-based bidding algorithm to adjust the dual variables in an online manner based on the real-time advertising performance feedback and constraints. Experiments on a real-world industrial dataset demonstrate the effectiveness of our bidding algorithm in terms of optimizing marketing objectives and guaranteeing ad constraints.
Co-Training with Validation: A Generic Framework for Semi-Supervised Relation Extraction
Authors: Shun Zhang, Xiangkui Lu, Jun Wu
Venue: CIKM
Year: 2022

In the scenarios of low-resource natural language applications, Semi-supervised Relation Extraction (SRE) plays a key role in mitigating the scarcity of labelled sentences by harnessing a large amount of unlabeled corpus. Current SRE methods are mainly designed based on the paradigm of Self-Training with Validation (STV), which employs two learners and each of them plays the single role of annotator or validator. However, such a single role setting under-utilizes the potential of learners in promoting new labelled instances from unlabeled corpus. In this paper, we propose a generic SRE paradigm, called Co-Training with Validation (CTV), for making full use of learners to benefit more from unlabeled corpus. In CTV, each learner alternately plays the roles of annotator and validator to generate and validate pseudo-labelled instances. Thus, more high-quality instances are exploited and two learners can be reinforced by each other during the learning process. Experimental results on two public datasets show that our CTV considerably outperforms the state-of-the-art SRE techniques, and works well with different kinds of learners for relation extraction.