OpenRaiser/Intern-Atlas huggingface dataset:
STREAMER 3.0: Towards Online Monitoring and Distributed Learning
Authors: Baudouin Naline, Sandra Garcia-Rodriguez, Karine Zeitouni
Venue: CIKM
Year: 2023

Applications that generate continuous data have proliferated in recent years, and thus the challenge of processing those data streams has emerged. This requires Data Stream Processing frameworks with monitoring capabilities able to detect and react to any non-desired situation. Many streaming use cases deal with distributed sources of data which, for privacy and communication saving purposes, need to be tackled in a distributed manner. Based on the mentioned challenges, this paper presents STREAMER 3.0, an improvement on the former data stream framework with two new modules: (i) a monitoring manager with detection algorithms, alert raising and automatic model updater; and (ii) a distributed learning module relying on federated learning. We showcase these new functionalities with an example of remaining useful life estimation of turbofan engines using an LSTM.
Non-Uniform Adversarial Perturbations for Discrete Tabular Datasets
Authors: Jay Nandy, Jatin Chauhan, Rishi Saket, Aravindan Raghuveer
Venue: CIKM
Year: 2023

We study the problem of adversarial attack and robustness on tabular datasets with discrete features. The discrete features of a tabular dataset represent high-level meaningful concepts, with different sets of vocabularies, leading to requiring non-uniform robustness. Further, the notion of distance between tabular input instances is not well defined, making the problem of producing adversarial examples with minor perturbations qualitatively more challenging compared to existing methods. Towards this, our paper defines the notion of distance through the lens of feature embeddings, learnt to represent the discrete features. We then formulate the task of generating adversarial examples as abinary set selection problem under non-uniform feature importance. Next, we propose an efficient approximate gradient-descent based algorithm, calledDiscrete Non-uniform Approximation (DNA) attack, by reformulating the problem into a continuous domain to solve the original optimization problem for generating adversarial examples. We demonstrate the effectiveness of our proposed DNA attack using two large real-world discrete tabular datasets from e-commerce domains for binary classification, where the datasets are heavily biased for one-class. We also analyze challenges for existing adversarial training frameworks for such datasets under our DNA attack.
RePair: An Extensible Toolkit to Generate Large-Scale Datasets for Query Refinement via Transformers
Authors: Yogeswar Lakshmi Narayanan, Hossein Fani
Venue: CIKM
Year: 2023

Query refinement is the process of transforming users' queries into newrefined versions without semantic drift to enhance the relevance of search results. Prior query refiners were benchmarked on web query logs followingweak assumptions that users' input queries within a search session are about a single topic and improve gradually, which is not necessarily accurate in practice. In this paper, we contribute RePair, an open-source configurable toolkit to generatelarge-scale gold-standard benchmark datasets whose pairs of (original query, refined versions) arealmost surely guaranteed to be in the same semantic context. RePair takes a dataset of queries and their relevance judgements (e.g., msmarco or aol), a sparse or dense retrieval method (e.g., bm25 or colbert ), and an evaluation metric (e.g., map or mrr), and outputs refined versions of queries, each of which with the relevance improvement guarantees under the retrieval method in terms of the evaluation metric. RePair benefits from text-to-text-transfer-transformer (t5) to generate gold-standard datasets for any input query sets and is designed with extensibility in mind. Out of the box, RePair includes gold-standard datasets for aol and msmarco.passage as well as benchmark results of state-of-the-art supervised query suggestion methods on the generated datasets at https://github.com/fani-lab/RePair.
Differential Privacy in HyperNetworks for Personalized Federated Learning
Authors: Vaisnavi Nemala, Phung Lai, NhatHai Phan
Venue: CIKM
Year: 2023

Federated learning (FL) is a framework for collaborative learning among users through a coordinating server. A recent HyperNetwork-based personalized FL framework, called HyperNetFL, is used to generate local models using personalized descriptors optimized for each user independently. However, HyperNetFL introduces unknown privacy risks. This paper introduces a novel approach to preserve user-level differential privacy, dubbed User-level DP, by providing formal privacy protection for data owners in training a HyperNetFL model. To achieve that, our proposed algorithm, called UDP-Alg, optimizes the trade-off between privacy loss and model utility by tightening sensitivity bounds. An intensive evaluation using benchmark datasets shows that our proposed UDP-Alg significantly improves privacy protection at a modest cost in utility.
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models
Authors: Preben M. Ness, Dusica Marijan, Sunanda Bose
Venue: CIKM
Year: 2023

Causal Neural Network models have shown high levels of robustness to adversarial attacks as well as an increased capacity for generalisation tasks such as few-shot learning and rare-context classification compared to traditional Neural Networks. This robustness is argued to stem from the disentanglement of causal and confounder input signals. However, no quantitative study has yet measured the level of disentanglement achieved by these types of causal models or assessed how this relates to their adversarial robustness. Existing causal disentanglement metrics are not applicable to deterministic models trained on real-world datasets. We, therefore, utilise metrics of content/style disentanglement from the field of Computer Vision to measure different aspects of the causal disentanglement for four state-of-the-art causal Neural Network models. By re-implementing these models with a common ResNet18 architecture we are able to fairly measure their adversarial robustness on three standard image classification benchmarking datasets under seven common white-box attacks. We find a strong association (r=0.820, p=0.001) between the degree to which models decorrelate causal and confounder signals and their adversarial robustness. Additionally, we find a moderate negative association between the pixel-level information content of the confounder signal and adversarial robustness (r=-0.597, p=0.040).
Dual-Oriented Contrast for Recommendation with A Stop-Gradient Operation
Authors: Byungkook Oh, Yul Kim, Bumky Min
Venue: CIKM
Year: 2023

Recently, contrastive loss is adopted as a main objective of recommender systems. InfoNCE-like losses penalize hard negative items more and control the strength of penalties with a temperature, called hardness-aware sensitivity. However, since they leverageuser->item patterns in a non-symmetric way, negative items are pushed away from anchor users and attract semantically-similar items to each other, focusing on the distribution of item embeddings. We point out that user embeddings also have inherent semantic structures that can be captured fromitem->user patterns. This paper presents Dual-oriented Contrast(DuCo), a novel symmetric learning objective for recommendation to learn more comprehensive representations fromusereftrightarrowitem patterns. DuCo controls user-/item-centric hardness-aware sensitivities and simultaneously optimizes the score distributions over sampled items (user-oriented contrast) and users (item-oriented contrast). This aims to explore ideal user and item distributions that are locally clustered and globally uniform. However, since user-/item-side temperatures are interdependent, naive control over temperatures may break the underlying semantic structures of the other side. To this end, we employ a stop-gradient operation to preserve the individual characteristics of user/item embedding distributions. Furthermore, we balance user-/item-oriented contrasts during learning to maintain consistent high-rank performance (e.g., recall@1). Empirical results show that DuCo contributes to the top-k user and item prediction simultaneously, and outperforms state-of-the-art learning objectives across different backbones from ID-based to neighbor-based encoders.
Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation System
Authors: Rongqing Kenneth Ong, Wei Qiu, Andy W. H. Khong
Venue: CIKM
Year: 2023

Knowledge graph (KG) has recently emerged as a powerful source of auxiliary information in the realm of knowledge-aware recommendation (KGR) systems. However, due to the lack of supervision signals caused by the sparse nature of user-item interactions, existing supervised graph neural network (GNN) models suffer from performance degradation. Moreover, the over-smoothing issue further limits the number of GNN layers or hops required to propagate messages - these models ignore the non-local information concealed deep within the knowledge graph. We propose the Quad-Tier Entity Fusion Contrastive Representation Learning (QTEF-CRL) knowledge-aware framework to achieve learning of deep user preferences from four perspectives: the collaborative, semantic, preference, and structural view. Unlike existing methods, the proposed tri-local and single-global quad-tier architecture exploits the knowledge graph holistically to achieve effective self-supervised representation learning. The newly-introduced preference view constructed from the collaborative knowledge graph (CKG) comprises a preference graph and preference-guided GNN that are specifically designed to capture non-local information explicitly. Experiments conducted on three datasets highlight the efficacy of our proposed model.
Optimal Real-Time Bidding Strategy for Position Auctions in Online Advertising
Authors: Weitong Ou, Bo Chen, Weiwen Liu, Xinyi Dai, Weinan Zhang, Wei Xia, Xuan Li, Ruiming Tang, Yong Yu
Venue: CIKM
Year: 2023

Position auctions are widely studied in the context of sponsored search advertising, where multiple ad slots are sold in a single auction. In traditional sponsored search, bids are submitted at the keyword level, while recent works have explored transitioning to impression-level bidding using Real-Time Bidding (RTB) techniques to achieve finer bidding. However, position auctions introduce varying user appeal across different positions and more dynamic auction landscape, which RTB, originally devised for single-slot display advertising, fails to address adequately. In this work, we are the first to study the optimal bidding strategy for position auctions in RTB. The position auctions we study belong to a broader class, including both sponsored search and display advertising with multiple ad slots. In particular, we aim at maximizing the advertising value within the budget constraint for an advertiser. We mathematically formulate the problem with explicit modeling of position effects. By leveraging the Lagrange multiplier, we derive the optimal bid price and prove its uniqueness under mild assumptions. Efficient numeric methods are applied to obtain the solution practically. Extensive experiments are conducted on a public semi-synthetic dataset and a private industry dataset to validate the effectiveness and feasibility in practice.
Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model
Authors: Wentao Ouyang, Xiuwu Zhang, Chaofeng Guo, Shukui Ren, Yupei Sui, Kun Zhang, Jinmei Luo, Yunfeng Chen, Dongbo Xu, Xiangzheng Liu, Yanlong Du
Venue: CIKM
Year: 2023

In real-world advertising systems, conversions have different types in nature and ads can be shown in different display scenarios, both of which highly impact the actual conversion rate (CVR). This results in the multi-type and multi-scenario CVR prediction problem. A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario. 2) Scalability: the model parameter size should be affordable. 3) Convenience: the model should not require a large amount of effort in data partitioning, subset processing and separate storage. Existing approaches cannot simultaneously satisfy these requirements. For example, building a separate model for each (conversion type, display scenario) pair is neither scalable nor convenient. Building a unified model trained on all the data with conversion type and display scenario included as two features is not accurate enough. In this paper, we propose the Masked Multi-domain Network (MMN) to solve this problem. To achieve the accuracy requirement, we model domain-specific parameters and propose a dynamically weighted loss to account for the loss scale imbalance issue within each mini-batch. To achieve the scalability requirement, we propose a parameter sharing and composition strategy to reduce model parameters from a product space to a sum space. To achieve the convenience requirement, we propose an auto-masking strategy which can take mixed data from all the domains as input. It avoids the overhead caused by data partitioning, individual processing and separate storage. Both offline and online experimental results validate the superiority of MMN for multi-type and multi-scenario CVR prediction. MMN is now the serving model for real-time CVR prediction in UC Toutiao.
Camaraderie: Content-based Knowledge Transfer for Medical Image Labelling using Supervised Autoencoders in a Decentralized Setting
Authors: Advait Padhye, Shreeja Bhakat, Humaira Firdowse, Atharv Savarkar, Ganesh Ramakrishnan, Kshitij S. Jadhav
Venue: CIKM
Year: 2023

Deep neural networks for medical imaging require large high-quality labelled data, a huge bottleneck for resource poor settings. Given the privacy requirements of medical data, institutes are un-willing to share data, causing an hindrance in resource poor settings. In the present paper, (Camaraderie: Content-based Knowledge Transfer for Medical Image Labelling using Supervised Autoencoders in a Decentralized Setting) we propose to use Discrete Classifier Supervised Autoencoder (DC-SAE) to generate low-dimensional representations of a few annotated images at the Donor client and transfer both the DC-SAE's encoder part and the latent space representations to the Recipient client without sharing raw data. We then pass the unlabelled images of the Recipient Client through this encoder to obtain their latent space representation. In a supervised setting, using latent space representation of Donor client's labelled images, we accurately annotate images of Recipient client. Camaraderie demonstrates that DC-SAE outperforms Recipient end label accuracy beyond classical VAE based classification and anomaly detection based VAE. Thus, given a limited amount of labelled data in a decentralized privacy preserving scenario, one can transfer latent space representation across clients to annotate large number of unlabelled images with high accuracy.
Graph Learning for Exploratory Query Suggestions in an Instant Search System
Authors: Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan
Venue: CIKM
Year: 2023

Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).
Satisfaction-Aware User Interest Network for Click-Through Rate Prediction
Authors: Mao Pan, Wen Shi, Kun Zhou, Zheng Li, Dongyue Wang, Zhuoye Ding, Xiwei Zhao, Sulong Xu
Venue: CIKM
Year: 2023

Click-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications, including online advertising and recommender systems. Existing approaches primarily focus on modeling the correlation between user interests and candidate items. However, we argue that personalized user preferences for candidate items depend not only on correlation but also on the satisfaction of associated interests. To address this limitation, we propose SUIN, a novel CTR model that integrates satisfaction factors into user interest modeling for enhanced click-through rate prediction. Specifically, we employ a user interest satisfaction-aware network to capture the degree of satisfaction for each interest, thereby enabling adaptation of the user's personalized preference based on satisfaction levels. Additionally, we leverage the exposure-unclicked signal (recommended to the user but not clicked) as supervision during training, facilitating the interest satisfaction module to better model the satisfaction degree of user interests. Besides, this module serves as a foundational building block suitable for integration into mainstream sequential-based CTR models. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed model, outperforming state-of-the-art methods across various evaluation metrics. Furthermore, an online A/B test deployed on large-scale recommender systems shows significant improvements achieved by our model in diverse evaluation metrics.
IUI: Intent-Enhanced User Interest Modeling for Click-Through Rate Prediction
Authors: Mao Pan, Tao Yu, Kun Zhou, Zheng Li, Dongyue Wang, Zhuoye Ding, Xiwei Zhao, Sulong Xu
Venue: CIKM
Year: 2023

Click-Through Rate (CTR) prediction is becoming increasingly vital in many industrial applications, such as recommendations and online advertising. How to precisely capture users' dynamic and evolving interests from previous interactions (e.g., clicks, purchases, etc.) is a challenging task in CTR prediction. Mainstream approaches focus on disentangling user interests in a heuristic way or modeling user interests into a static representation. However, these approaches overlook the importance of users' current intent and the complex interactions between their current intent and global interests. To address these concerns, in this paper, we propose a novel intent-enhanced user interest modeling for click-through rate prediction in large-scale e-commerce recommendations, abbreviated as IUI. Methodologically, different from existing works, we consider users' recent interactions to be inspired by their implicit intent and then leverage an intent-aware network to model their current local interests in a more precise and fine-grained manner. In addition, to obtain a more stable co-dependent global and local interest representation, we employ a co-attention network capable of activating the corresponding interest in global-level interactions and capturing the dynamic interactions between global- and local-level interaction behaviors. Finally, we incorporate self-supervised learning into the model training by maximizing the mutual information between the global and local representations obtained via the above two networks to enhance the CTR prediction performance. Compared with existing methods, IUI benefits from the different granularity of user interest to generate a more accurate and comprehensive preference representation. Experimental results demonstrate that the proposed model outperforms previous state-of-the-art methods in various metrics on three real-world datasets. In addition, an online A/B test deployed on the JD recommendation platforms shows a promising improvement across multiple evaluation metrics.
KID34K: A Dataset for Online Identity Card Fraud Detection
Authors: Eun-Ju Park, Seung-Yeon Back, Jeongho Kim, Simon S. Woo
Venue: CIKM
Year: 2023

Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. Our dataset is available at https://github.com/DASH-Lab/idcard_fraud_detection.
Quantum Split Learning for Privacy-Preserving Information Management
Authors: Soohyun Park, Hankyul Baek, Joongheon Kim
Venue: CIKM
Year: 2023

Recently, research on quantum neural network (QNN) architectures has been attracted in various fields. Among them, the distributed computation of QNN has been actively discussed for privacy-preserving information management due to data and model distribution over multiple computing devices. Based on this concept, this paper proposes quantum split learning (QSL) which splits a single QNN architecture across multiple distributed computing devices to avoid entire QNN architecture exposure. In order to realize QSL design, this paper also proposes cross-channel pooling, which utilizes quantum state tomography. Our evaluation results verifies that QSL preserves privacy in classification tasks and also improves accuracy at most by 6.83% compared to existing methods.