OpenRaiser/Intern-Atlas huggingface dataset:
HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce
Authors: Xiaohui Zhao, Shuai Wang, Hai Wang, Tian He, Desheng Zhang, Guang Wang
Venue: CIKM
Year: 2023

Warehouse-distribution integration has been adopted by many e-commerce retailers (e.g., Amazon, TAOBAO, and JD) as an efficient business mode. In warehouse-distribution integration e-commerce, one of the most important problems is to estimate the full-link delivery time for better decision-making. Existing solutions for traditional warehouse-distribution separation mode are challenging to address this problem due to two unique features in the integration mode including (i) contextual influence caused by neighbor units in heterogeneous delivery networks, (ii) uncertain delivery time caused by the dynamic temporal data (e.g., online sales volume) and heterogeneity of delivery units. To incorporate these new factors, we propose Heterogeneous Spatial-Temporal Graph Transformer (HST-GT), a novel full-link delivery time estimation method under the warehouse-distribution integration mode, where we (i) develop heterogeneous graph transformers to capture hierarchical heterogeneous information; and (ii) design a set of spatial-temporal transformers based on heterogeneous features to fully exploit the correlation of spatial and temporal information. We extensively evaluate our method based on one-month real-world data consisting of hundreds of warehouses and sorting centers, and millions of historical orders collected from one of the largest e-commerce retailers in the world. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Authors: Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, Xiang-Rong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng
Venue: CIKM
Year: 2023

Conversion rate (CVR) prediction is an essential task for e-commerce platforms. However, refunds frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier services. This paper defines the probability of item purchasing without any subsequent refund as an effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to decompose it into two sub-tasks: CVR prediction and post-conversion refund rate (RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and sample selection bias (SSB) issues, as refund behaviors are only available after user purchase. Furthermore, there is delayed feedback in both sequentially dependent conversion and refund events, named cascade delayed feedback (CDF). Previous studies mainly focus on tackling DS and SSB or delayed feedback for a single event. To jointly tackle these issues in ECVR prediction, we propose an Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically, ECAD deals with DS and SSB by constructing two tasks including CVR and conversion&refund rate (CVRFR) predictions using the entire space modeling framework. In addition, it carefully schedules auxiliary tasks to leverage both conversion and refund time within data to alleviate CDF. Experiments on the offline industrial dataset and online A/B testing demonstrate the effectiveness of ECAD. ECAD has been deployed in the Xianyu recommender system of Alibaba, contributing to a significant improvement of ECVR.
Task-Difficulty-Aware Meta-Learning with Adaptive Update Strategies for User Cold-Start Recommendation
Authors: Xuhao Zhao, Yanmin Zhu, Chunyang Wang, Mengyuan Jing, Jiadi Yu, Feilong Tang
Venue: CIKM
Year: 2023

User cold-start recommendation is one of the most challenging problems that limit the effectiveness of recommender systems. Meta-learning-based methods are introduced to address this problem by learning initialization parameters for cold-start tasks. Recent studies attempt to enhance the initialization methods. They first represent each task by the cold-start user and interacted items. Then they distinguish tasks based on the task relevance to learn adaptive initialization. However, this manner is based on the assumption that user preferences can be reflected by the interacted items saliently, which is not always true in reality. In addition, we argue that previous approaches suffer from their adaptive framework (e.g., adaptive initialization), which reduces the adaptability in the process of transferring meta-knowledge to personalized RSs. In response to the issues, we propose a task-difficulty-aware meta-learning with adaptive update strategies (TDAS) for user cold-start recommendation. First, we design a task difficulty encoder, which can represent user preference salience, task relevance, and other task characteristics by modeling task difficulty information. Second, we adopt a novel framework with task-adaptive local update strategies by optimizing the initialization parameters with task-adaptive per-step and per-layer hyperparameters. Extensive experiments based on three real-world datasets demonstrate that our TDAS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/TDAS.
Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat
Authors: Jiawei Zheng, Hao Gu, Chonggang Song, Dandan Lin, Lingling Yi, Chuan Chen
Venue: CIKM
Year: 2023

Recently, cross-domain recommendation (CDR) has been widely studied in both research and industry since it can alleviate a long-standing challenge of traditional recommendation methods, i.e., data sparsity issue, by transferring the information from a relatively richer domain (termed source domain) to a sparser domain (termed target domain). To our best knowledge, most (if not all) existing CDR methods focus on transferring either the similar content information or the user preferences embedding from the source domain to the target domain. However, they fail to improve the recommendation performance in real-world recommendation scenarios where the items in the source domain are totally different from those in the target domain in terms of attributes. To solve the above issues, we analyzed the historical interactions of users from different domains in the WeChat platform, and found that if two users have similar interests (interactions) in one domain, they are very likely to have similar interests in another domain even though the items of these two domains are totally different in terms of attributes. Based on this observation, in this paper, we propose a novel model named Dual Interests-Aligned Graph Auto-Encoders (DIAGAE) by utilizing the inter-domain interest alignment of users. Besides, our proposed model DIAGAE also leverages graph decoding objectives to align intra-domain user interests, which makes the representation of two users who have similar interests in a single domain closer. Comprehensive experimental results demonstrate that our model DIAGAE outperforms state-of-the-art methods on both public benchmark datasets and online A/B tests in WeChat live-stream recommendation scenario. Our model DIAGAE now serves the major online traffic in WeChat live-streaming recommendation scenario.
FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning
Authors: Longfei Zheng, Yingting Liu, Xiaolong Xu, Chaochao Chen, Yuzhou Tang, Lei Wang, Xiaolong Hu
Venue: CIKM
Year: 2023

Federated learning (FL) is a popular distributed machine learning framework in which clients aggregate models' parameters instead of sharing individual data. In FL, clients frequently communicate with the server under limited network bandwidth, raising the communication challenge. Multiple compression methods have been proposed to reduce the transmitted parameters. However, these techniques show that the federated performance degrades significantly with Non-IID (non-identically independently distributed) datasets. To address this issue, we propose an effective method called FedPSE, which solves the efficiency challenge of FL with heterogeneous data. FedPSE compresses the local updates on clients using Top-K sparsification and aggregates these updates on the server by element-wise aggregation. Then clients download the personalized sparse updates from the server to update their individual local models. We then theoretically analyze the convergence of FedPSE under the non-convex setting. Moreover, extensive experiments on four benchmark tasks demonstrate that our FedPSE outperforms the state-of-the-art methods on Non-IID datasets in terms of efficiency and accuracy.
A Semi-Supervised Anomaly Network Traffic Detection Framework via Multimodal Traffic Information Fusion
Authors: Yu Zheng, Xinglin Lian, Zhangxuan Dang, Chunlei Peng, Chao Yang, Jianfeng Ma
Venue: CIKM
Year: 2023

Anomaly traffic detection is a crucial issue in the cyber-security field. Previously, many researchers regarded anomaly traffic detection as a supervised classification problem. However, in real scenarios, anomaly network traffic is unpredictable, dynamically changing and difficult to collect. To address these limitations, we employ anomaly detection setting to propose a novel semi-supervised anomaly network traffic detection framework. It only learns features of normal samples during the training phase. Our framework utilizes low-pass filtering to extract multi-scale low-frequency information from 2-D traffic image. Furthermore, we design a two-stage fusion scheme to incorporate information from original and multi-scale low-frequency traffic image modalities. We conduct experiments on two public datasets: ISCX Tor-nonTor and USTC-TFC2016. The experimental results show that our method outperforms current state-of-the-art anomaly detection methods.
Decentralized Graph Neural Network for Privacy-Preserving Recommendation
Authors: Xiaolin Zheng, Zhongyu Wang, Chaochao Chen, Jiashu Qian, Yao Yang
Venue: CIKM
Year: 2023

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework.
DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model
Authors: Yuhao Zheng, Lian Zhong, Senzhang Wang, Yu Yang, Weixi Gu, Junbo Zhang, Jianxin Wang
Venue: CIKM
Year: 2023

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected human/vehicle trajectory flows are usually rather unreliable, may contain various noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.
Personalized Location-Preference Learning for Federated Task Assignment in Spatial Crowdsourcing
Authors: Xiaolong Zhong, Hao Miao, Dazhuo Qiu, Yan Zhao, Kai Zheng
Venue: CIKM
Year: 2023

With the proliferation of wireless and mobile devices, Spatial Crowdsourcing (SC) attracts increasing attention, where task assignment plays a critically important role. However, recent task assignment solutions in SC often assume that data is stored in a central station while ignoring the issue of privacy leakage. To enable decentralized training and privacy protection, we propose a federated task assignment framework with personalized location-preference learning, which performs efficient task assignment while keeping the data decentralized and private in each platform center (e.g., a delivery center of an SC company). The framework consists of two phases: personalized federated location-preference learning and task assignment. Specifically, in the first phase, we design a personalized location-preference learning model for each platform center by simultaneously considering the location information and data heterogeneity across platform centers. Based on workers' location preference, the task assignment phase aims to achieve effective and efficient task assignment by means of the Kuhn-Munkres (KM) algorithm and the newly proposed conditional degree-reduction algorithm. Extensive experiments on real-world data show the effectiveness of the proposed framework.
Demonstration of ViTA: Visualizing, Testing and Analyzing Index Advisors
Authors: Wei Zhou, Chen Lin, Xuanhe Zhou, Guoliang Li, Tianqing Wang
Venue: CIKM
Year: 2023

Index advisors have become an essential tool to optimize index selection and accelerate query processing. Various index advisors have been developed in recent years, and comprehensively assessing their performance from multiple aspects is necessary. In this demonstration, we introduce VITA, a user-friendly and informative tool for interactively Visualizing, Testing, and Analyzing index advisors. For a user-given workload, VITA can visualize the main steps of the index selection procedure in ten existing index advisors to facilitate the management of index advisors. Moreover, VITA can assess the index advisor's robustness w.r.t. workload drift by generating testing workloads, i.e., potentially future workloads that may damage the index advisor's performance. Finally, VITA provides a comparative analysis across index advisors on four aspects, including the index advisor's utility (i.e., the ratio of the reduced workload cost), robustness (i.e., the performance under dynamic workload), overhead (i.e., the time to acquire the final configuration), and scalability (i.e., the volume of the enumerated index candidates). Therefore, VITA can thoroughly compare existing index advisors to help users determine the most suitable index advisor that meets their requirements. VITA is now being integrated into the openGauss platform as a plug-in.
Target-oriented Few-shot Transferring via Measuring Task Similarity
Authors: Zhipeng Zhou, Wei Gong, Haoquan Zhou
Venue: CIKM
Year: 2023

Despite significant progress in recent years, few-shot learning (FSL) still faces two critical challenges. Firstly, most FSL solutions in the training phase rely on exploiting auxiliary tasks, while target tasks are underutilized. Secondly, current benchmarks sample numerous target tasks, each with only an N-way C-shot shot query set in the evaluation phase, which is not representative of real-world scenarios. To address these issues, we propose Guidepost, a target-oriented FSL method that can implicitly learn task similarities using a task-level learn-to-learn mechanism and then re-weight auxiliary tasks. Additionally, we introduce a new FSL benchmark that satisfies realistic needs and aligns with our target-oriented approach. Mainstream FSL methods struggle under this new experimental setting. Extensive experiments demonstrate that Guidepost outperforms two classical few-shot learners, i.e., MAML and ProtoNet, and one state-of-the-art few-shot learner, i.e., RENet, on several FSL image datasets. Furthermore, we implement Guidepost as a domain adaptor to achieve high accuracy wireless sensing on our collected WiFi-based human activity recognition dataset.
Learning Node Abnormality with Weak Supervision
Authors: Qinghai Zhou, Kaize Ding, Huan Liu, Hanghang Tong
Venue: CIKM
Year: 2023

Graph anomaly detection aims to identify the atypical substructures and has attracted an increasing amount of research attention due to its profound impacts in a variety of application domains, including social network analysis, security, finance, and many more. The lack of prior knowledge of the ground-truth anomaly has been a major obstacle in acquiring fine-grained annotations (e.g., anomalous nodes), therefore, a plethora of existing methods have been developed either with a limited number of node-level supervision or in an unsupervised manner. Nonetheless, annotations for coarse-grained graph elements (e.g., a suspicious group of nodes), which often require marginal human effort in terms of time and expertise, are comparatively easier to obtain. Therefore, it is appealing to investigate anomaly detection in a weakly-supervised setting and to establish the intrinsic relationship between annotations at different levels of granularity. In this paper, we tackle the challenging problem of weakly-supervised graph anomaly detection with coarse-grained supervision by (1) proposing a novel architecture of graph neural network with attention mechanism named WEDGE that can identify the critical node-level anomaly given a few labels of anomalous subgraphs, and (2) designing a novel objective with contrastive loss that facilitates node representation learning by enforcing distinctive representations between normal and abnormal graph elements. Through extensive evaluations on real-world datasets, we corroborate the efficacy of our proposed method, improving AUC-ROC by up to 16.48% compared to the best competitor.
A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation
Authors: Yuqing Zhou, Tianshu Feng, Mingrui Liu, Ziwei Zhu
Venue: CIKM
Year: 2023

This paper addresses the critical gap in the unbiased estimation of post-click conversion rate (CVR) in recommender systems. Existing CVR prediction methods, such as Inverse Propensity Score (IPS) and various Doubly Robust (DR) based estimators, overlook the impact of propensity estimation on the model bias and variance, thus leading to a debiasing performance gap. We propose a Generalized Propensity Learning (GPL) framework to directly minimize the bias and variance in CVR prediction models. The proposed method works as a complement to existing methods like IPS, DR, MRDR, and DRMSE to improve prediction performance by reducing their bias and variance. Extensive experiments on real-world datasets and semi-synthetic datasets demonstrate the significant performance promotion brought by our proposed method. Data and code can be found at: https://github.com/yuqing-zhou/GPL.
Privacy-Preserving Federated Learning via Disentanglement
Authors: Wenjie Zhou, Piji Li, Zhaoyang Han, Xiaozhen Lu, Juan Li, Zhaochun Ren, Zhe Liu
Venue: CIKM
Year: 2023

The trade-off between privacy and accuracy presents a challenge for current federated learning (FL) frameworks, hindering their progress from theory to application. The main issues with existing FL frameworks stem from a lack of interpretability and targeted privacy protections. To cope with these, we proposed Disentangled Federated Learning for Privacy (DFLP) which employes disentanglement, one of interpretability techniques, in private FL frameworks. Since sensitive properties are client-specific in nature, our main idea is to turn this feature into a tool that strikes the balance between data privacy and FL model performance, enabling the sensitive attributes to be private. DFLP disentangles the client-specific and class-invariant attributes to mask the sensitive attributes precisely. To our knowledge, this is the first work that successfully integrates disentanglement and the nature of sensitive attributes to achieve privacy protection while ensuring high FL model performance. Extensive experiments validate that disentanglement is an effective method for accuracy-aware privacy protection in FL frameworks.
All about Sample-Size Calculations for A/B Testing: Novel Extensions & Practical Guide
Authors: Jing Zhou, Jiannan Lu, Anas Shallah
Venue: CIKM
Year: 2023

While there exists a large amount of literature on the general challenges and best practices for trustworthy online A/B testing, there are limited studies on sample size estimation, which plays a crucial role in trustworthy and efficient A/B testing that ensures the resulting inference has a sufficient power and type I error control. For example, when sample size is under-estimated, the statistical inference, even with the correct analysis methods, will not be able to detect the true significant improvement leading to misinformed and costly decisions. This paper addresses this fundamental gap by developing new sample size calculation methods for correlated data, as well as absolute vs. relative treatment effects, both ubiquitous in online experiments. Additionally, we address a practical question of the minimal observed difference that will be statistically significant and how it relates to average treatment effect and sample size calculation. All proposed methods are accompanied by mathematical proofs, illustrative examples, and simulations. We end by sharing some best practices on various practical topics on sample size calculation and experimental design.
LEAD-ID: Language-Enhanced Denoising and Intent Distinguishing Graph Neural Network for Sponsored Search Broad Retrievals
Authors: Xiao Zhou, Ran Wang, Haorui Li, Qiang Liu, Xingxing Wang, Dong Wang
Venue: CIKM
Year: 2023

As a local-based service (LBS), search ad retrieval in online meal delivery platforms should be broader to bridge the gap between vague consumption intentions of users and shortage of ad candidates limited by users' queries and positions. Recently, graph neural networks (GNNs) have been successfully applied to search ad retrieval task. However, directly applying GNNs suffer from noisy interactions and intents indistinguishability, which seriously degrades systems' effectiveness in the broad retrieval. In this paper, we propose a Language-EnhAnced Denoising and Intent Distinguishing graph neural network, LEAD-ID, which is developed and deployed at Meituan for sponsored search broad retrieval. To denoise interaction data, LEAD-ID designs hard- and soft- denoising strategies for GNNs based on a pretrained language model. A variational EM method is also employed to reduce high computational complexity of combining LMs and GNNs jointly. To distinguish various intents, LEAD-ID generates intent-aware node representations based on meticulously crafted LMs (language model) and GNNs; and then, it is guided by a contrastive learning object in an explicit and effective manner. According to offline experiments and online A/B tests, our framework significantly outperforms baselines in terms of recall and revenue.