Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining最新文献_第3页

Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation 面向可解释推荐的认知感知知识图推理

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570391

Qin Bing, Qiannan Zhu, Zhicheng Dou

{"title":"Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation","authors":"Qin Bing, Qiannan Zhu, Zhicheng Dou","doi":"10.1145/3539597.3570391","DOIUrl":"https://doi.org/10.1145/3539597.3570391","url":null,"abstract":"Knowledge graphs (KGs) have been widely used in recommendation systems to improve recommendation accuracy and interpretability effectively. Recent research usually endows KG reasoning to find the multi-hop user-item connection paths for explaining why an item is recommended. The existing path-finding process is well designed by logic-driven inference algorithms, while there exists a gap between how algorithms and users perceive the reasoning process. Factually, human thinking is a natural reasoning process that can provide more proper and convincing explanations of why particular decisions are made. Motivated by the Dual Process Theory in cognitive science, we propose a cognition-aware KG reasoning model CogER for Explainable Recommendation, which imitates the human cognition process and designs two modules, i.e., System~1 (making intuitive judgment) and System~2 (conducting explicit reasoning), to generate the actual decision-making process. At each step during the cognition-aware reasoning process, System~1 generates an intuitive estimation of the next-step entity based on the user's historical behavior, and System~2 conducts explicit reasoning and selects the most promising knowledge entities. These two modules work iteratively and are mutually complementary, enabling our model to yield high-quality recommendations and proper reasoning paths. Experiments on three real-world datasets show that our model achieves better recommendation results with explanations compared with previous methods.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"19 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114037421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Combining vs. Transferring Knowledge: Investigating Strategies for Improving Demographic Inference in Low Resource Settings 结合与转移知识:低资源环境下改善人口统计推断的调查策略

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570462

Yaguang Liu, Lisa Singh

{"title":"Combining vs. Transferring Knowledge: Investigating Strategies for Improving Demographic Inference in Low Resource Settings","authors":"Yaguang Liu, Lisa Singh","doi":"10.1145/3539597.3570462","DOIUrl":"https://doi.org/10.1145/3539597.3570462","url":null,"abstract":"For some learning tasks, generating a large labeled data set is impractical. Demographic inference using social media data is one such task. While different strategies have been proposed to mitigate this challenge, including transfer learning, data augmentation, and data combination, they have not been explored for the task of user level demographic inference using social media data. This paper explores two of these strategies: data combination and transfer learning. First, we combine labeled training data from multiple data sets of similar size to understand when the combination is valuable and when it is not. Using data set distance, we quantify the relationship between our data sets to help explain the performance of the combination strategy. Then, we consider supervised transfer learning, where we pretrain a model on a larger labeled data set, fine-tune the model on smaller data sets, and incorporate regularization as part of the transfer learning process. We empirically show the strengths and limitations of the proposed techniques on multiple Twitter data sets.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122814331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Classification of Different Participating Entities in the Rise of Hateful Content in Social Media 社交媒体中仇恨内容兴起的不同参与实体的分类

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572985

Mithun Das

引用次数: 0

Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning 基于离线约束深度强化学习的营销预算分配

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570486

Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang

{"title":"Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning","authors":"Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang","doi":"10.1145/3539597.3570486","DOIUrl":"https://doi.org/10.1145/3539597.3570486","url":null,"abstract":"We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132529755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Heterogeneous Graph Contrastive Learning for Recommendation 面向推荐的异构图对比学习

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570484

Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo

{"title":"Heterogeneous Graph Contrastive Learning for Recommendation","authors":"Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo","doi":"10.1145/3539597.3570484","DOIUrl":"https://doi.org/10.1145/3539597.3570484","url":null,"abstract":"Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133746449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Feature Missing-aware Routing-and-Fusion Network for Customer Lifetime Value Prediction in Advertising 面向广告客户终身价值预测的特征缺失感知路由融合网络

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570460

Xuejiao Yang, Binfeng Jia, Shuangyang Wang, Shijie Zhang

{"title":"Feature Missing-aware Routing-and-Fusion Network for Customer Lifetime Value Prediction in Advertising","authors":"Xuejiao Yang, Binfeng Jia, Shuangyang Wang, Shijie Zhang","doi":"10.1145/3539597.3570460","DOIUrl":"https://doi.org/10.1145/3539597.3570460","url":null,"abstract":"Nowadays, customer lifetime value (LTV) plays an important role in mobile game advertising, since it can be beneficial to adjust ad bids and ensure that the games are promoted to the most valuable users. Some neural models are utilized for LTV prediction based on the rich user features. However, in the advertising scenario, due to the privacy settings or limited length of log retention, etc, most of existing approaches suffer from the missing feature problem. Moreover, only a small fraction of purchase behaviours can be observed. The label sparsity inevitably limits model expressiveness. To tackle the aforementioned challenges, we propose a feature missing-aware routing-and-fusion network (MarfNet) to reduce the effect of the missing features while training. Specifically, we calculate the missing states of raw features and feature interactions for each sample. Based on the missing states, two missing-aware layers are designed to route samples into different experts, thus each expert can focus on the real features of samples assigned to it. Finally we get the missing-aware representation by the weighted fusion of the experts. To alleviate the label sparsity, we further propose a batch-in dynamic discrimination enhanced (Bidden) loss weight mechanism, which can automatically assign greater loss weights to difficult samples in the training process. Both offline experiments and online A/B tests have validated the superiority of our proposed Bidden-MarfNet.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134329994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Boosting Advertising Space: Designing Ad Auctions for Augment Advertising 提升广告空间:为增加广告设计广告拍卖

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570381

Yangsu Liu, Dagui Chen, Zhenzhe Zheng, Zhilin Zhang, Chuan Yu, Fan Wu, Guihai Chen

{"title":"Boosting Advertising Space: Designing Ad Auctions for Augment Advertising","authors":"Yangsu Liu, Dagui Chen, Zhenzhe Zheng, Zhilin Zhang, Chuan Yu, Fan Wu, Guihai Chen","doi":"10.1145/3539597.3570381","DOIUrl":"https://doi.org/10.1145/3539597.3570381","url":null,"abstract":"In online e-commerce platforms, sponsored ads are always mixed with non-sponsored organic content (recommended items). To guarantee user experience, online platforms always impose strict limitations on the number of ads displayed, becoming the bottleneck for advertising revenue. To boost advertising space, we introduce a novel advertising business paradigm called Augment Advertising, where once a user clicks on a leading ad on the main page, instead of being shown the corresponding products, a collection of mini-detail ads relevant to the clicked ad is displayed. A key component for augment advertising is to design ad auctions to jointly select leading ads on the main page and mini-detail ads on the augment ad page. In this work, we decouple the ad auction into a two-stage auction, including a leading ad auction and a mini-detail ad auction. We design the Potential Generalized Second Price (PGSP) auction with Symmetric Nash Equilibrium (SNE) for leading ads, and adopt GSP auction for mini-detail ads. We have deployed augment advertising on Taobao advertising platform, and conducted extensive offline evaluations and online A/B tests. The evaluation results show that augment advertising could guarantee user experience while improving the ad revenue and the PGSP auction outperforms baselines in terms of revenue and user experience in augment advertising.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114656537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alleviating Structural Distribution Shift in Graph Anomaly Detection 缓解图异常检测中的结构分布偏移

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570377

Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

{"title":"Alleviating Structural Distribution Shift in Graph Anomaly Detection","authors":"Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang","doi":"10.1145/3539597.3570377","DOIUrl":"https://doi.org/10.1145/3539597.3570377","url":null,"abstract":"Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes --- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes. Furthermore, due to various time factors and the annotation preferences of human experts, the heterophily and homophily can change across training and testing data, which is called structural distribution shift (SDS) in this paper. The mainstream methods are built on graph neural networks (GNNs), benefiting the classification of normals from aggregating homophilous neighbors, yet ignoring the SDS issue for anomalies and suffering from poor generalization. This work solves the problem from a feature view. We observe that the degree of SDS varies between anomalies and normal nodes. Hence to address the issue, the key lies in resisting high heterophily for anomalies meanwhile benefiting the learning of normals from homophily. Since different labels correspond to the difference of critical anomaly features which make great contributions to the GAD, we tease out the anomaly features on which we constrain to mitigate the effect of heterophilous neighbors and make them invariant. However, the prior distribution of anomaly features is dynamic and hard to estimate, we thus devise a prototype vector to infer and update this distribution during training. For normal nodes, we constrain the remaining features to preserve the connectivity of nodes and reinforce the influence of the homophilous neighborhood. We term our proposed framework asGraph Decomposition Network (GDN). Extensive experiments are conducted on two benchmark datasets, and the proposed framework achieves a remarkable performance boost in GAD, especially in an SDS environment where anomalies have largely different structural distribution across training and testing environments. Codes are open-sourced in https://github.com/blacksingular/wsdm_GDN.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114597835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling 第四届人群科学研讨会- CANDLE:数据标注中人类与学习算法的合作

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572703

Dmitry Ustalov, Saiph Savage, N. V. Berkel, Yang Liu

引用次数: 0

Disentangled Negative Sampling for Collaborative Filtering 协同滤波的解纠缠负采样

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570419

Riwei Lai, L. Chen, Yuhan Zhao, R. Chen, Qilong Han

{"title":"Disentangled Negative Sampling for Collaborative Filtering","authors":"Riwei Lai, L. Chen, Yuhan Zhao, R. Chen, Qilong Han","doi":"10.1145/3539597.3570419","DOIUrl":"https://doi.org/10.1145/3539597.3570419","url":null,"abstract":"Negative sampling is essential for implicit collaborative filtering to generate negative samples from massive unlabeled data. Unlike existing strategies that consider items as a whole when selecting negative items, we argue that normally user interactions are mainly driven by some relevant, but not all, factors of items, leading to a new direction of negative sampling. In this paper, we introduce a novel disentangled negative sampling (DENS) method. We first disentangle the relevant and irrelevant factors of positive and negative items using a hierarchical gating module. Next, we design a factor-aware sampling strategy to identify the best negative samples by contrasting the relevant factors while keeping irrelevant factors similar. To ensure the credibility of the disentanglement, we propose to adopt contrastive learning and introduce four pairwise contrastive tasks, which enable to learn better disentangled representations of the relevant and irrelevant factors and remove the dependency on ground truth. Extensive experiments on five real-world datasets demonstrate the superiority of DENS against several state-of-the-art competitors, achieving over 7% improvement over the strongest baseline in terms of Recall@20 and NDCG@20. Our code is publically available at https://github.com/Riwei-HEU/DENS .","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125952565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2