Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining最新文献_第5页

CoQEx: Entity Counts Explained CoQEx:实体计数解释

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573021

Shrestha Ghosh, S. Razniewski, G. Weikum

引用次数: 0

Cooperative Explanations of Graph Neural Networks 图神经网络的协同解释

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570378

Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-seng Chua

{"title":"Cooperative Explanations of Graph Neural Networks","authors":"Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-seng Chua","doi":"10.1145/3539597.3570378","DOIUrl":"https://doi.org/10.1145/3539597.3570378","url":null,"abstract":"With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations. In this work, we explore the explainability of GNNs at the granularity of both input features and hidden neurons. To this end, we propose an explainer-agnostic framework, Cooperative GNN Explanation (CGE) to generate the explanatory subgraph and subnetwork simultaneously, which jointly explain how the GNN model arrived at its prediction. Specifically, it first initializes the importance scores of input features and hidden neurons with masking networks. Then it iteratively retrains the importance scores, refining the salient subgraph and subnetwork by discarding low-scored features and neurons in each iteration. Through such cooperative learning, CGE not only generates faithful and concise explanations, but also exhibits how the salient information flows by activating and deactivating neurons. We conduct extensive experiments on both synthetic and real-world datasets, validating the superiority of CGE over state-of-the-art approaches. Code is available at https://github.com/MangoKiller/CGE_demo.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"23 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":"134255528","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: 4

Web of Conferences: A Conference Knowledge Graph 会议网络:会议知识图谱

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573024

Shuo Yu, Ciyuan Peng, Chengchuan Xu, Chen Zhang, Feng Xia

{"title":"Web of Conferences: A Conference Knowledge Graph","authors":"Shuo Yu, Ciyuan Peng, Chengchuan Xu, Chen Zhang, Feng Xia","doi":"10.1145/3539597.3573024","DOIUrl":"https://doi.org/10.1145/3539597.3573024","url":null,"abstract":"Academic conferences have been proven to be significant in facilitating academic activities. To promote information retrieval specific to academic conferences, building complete, systematic, and professional conference knowledge graphs is a crucial task. However, many related systems mainly focus on general knowledge of overall academic information or concentrate services on specific domains. Aiming at filling this gap, this work demonstrates a novel conference knowledge graph, namely Web of Conferences. The system accommodates detailed conference profiles, conference ranking lists, intelligent conference queries, and personalized conference recommendations. Web of Conferences supports detailed conference information retrieval while providing the ranking of conferences based on the most recent data. Conference queries in the system can be implemented via precise search or fuzzy search. Then, according to users' query conditions, personalized conference recommendations are available. Web of Conferences is demonstrated with a user-friendly visualization interface and can be served as a useful information retrieval system for researchers.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"57 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":"132430413","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

Compliance Analyses of Australia's Online Household Appliances 澳大利亚在线家电产品的合规性分析

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3575788

Chang How Tan, V. C. Lee, Jessie Nghiem, Priya Laxman

{"title":"Compliance Analyses of Australia's Online Household Appliances","authors":"Chang How Tan, V. C. Lee, Jessie Nghiem, Priya Laxman","doi":"10.1145/3539597.3575788","DOIUrl":"https://doi.org/10.1145/3539597.3575788","url":null,"abstract":"Commercially sold electrical or gas products must comply with the safety standards imposed within a country and get registered and certified by a regulated body. However, with the increasing transition of businesses to e-commerce platforms, it becomes challenging to govern the compliance status of online products. This can increase the risk of purchasing non-compliant products which may be unsafe to use. Additionally, examining the compliance status before purchasing can be strenuous because the relevant compliance information can be ambiguous and not always directly available. Therefore, we collaborated with a regulated body from Australia, Energy Safe Victoria, and conducted compliance analyses for household appliances sold on multiple online platforms. A fully autonomous method shown in this public repository is also introduced to check the compliance status of any online product. In this talk, we discuss the compliance check process, which incorporates fuzzy logic for textual matching and a Convolutional Neural Network (CNN) model to classify the product listing based on the images listed. Subsequently, we studied the results with the business users and found that many online listings are non-compliant, signifying that online-shopping consumers are highly susceptible to buying unsafe products. We hope this talk can inspire more follow-up works that collaborate with regulated bodies to introduce a user-friendly compliance check platform that assists in educating consumers to purchase compliant products.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"99 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":"130344396","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

Self-Supervised Group Graph Collaborative Filtering for Group Recommendation 群体推荐的自监督群图协同过滤

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570400

Kang Li, Changdong Wang, J. Lai, Huaqiang Yuan

{"title":"Self-Supervised Group Graph Collaborative Filtering for Group Recommendation","authors":"Kang Li, Changdong Wang, J. Lai, Huaqiang Yuan","doi":"10.1145/3539597.3570400","DOIUrl":"https://doi.org/10.1145/3539597.3570400","url":null,"abstract":"Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"76 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":"127424235","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: 3

A Framework for Detecting Frauds from Extremely Few Labels 从极少量标签中检测欺诈的框架

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573022

Ya-Lin Zhang, Yifang Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wen Wang, Longfei Li, Jun Zhou, Jinghua Feng

引用次数: 0

Generating Explainable Product Comparisons for Online Shopping 为网上购物产生可解释的产品比较

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570489

Nikhita Vedula, M. Collins, Eugene Agichtein, Oleg Rokhlenko

{"title":"Generating Explainable Product Comparisons for Online Shopping","authors":"Nikhita Vedula, M. Collins, Eugene Agichtein, Oleg Rokhlenko","doi":"10.1145/3539597.3570489","DOIUrl":"https://doi.org/10.1145/3539597.3570489","url":null,"abstract":"An essential part of making shopping purchase decisions is to compare and contrast products based on key differentiating features, but doing this manually can be overwhelming. Prior methods offer limited product comparison capabilities, e.g., via pre-defined common attributes that may be difficult to understand, or irrelevant to a particular product or user. Automatically generating an informative, natural-sounding, and factually consistent comparative text for multiple product and attribute types is a challenging research problem. We describe HCPC (Human Centered Product Comparison), to tackle two kinds of comparisons for online shopping: (i) product-specific, to describe and compare products based on their key attributes; and (ii) attribute-specific comparisons, to compare similar products on a specific attribute. To ensure that comparison text is faithful to the input product data, we introduce a novel multi-decoder, multi-task generative language model. One decoder generates product comparison text, and a second one generates supportive, explanatory text in the form of product attribute names and values. The second task imitates a copy mechanism, improving the comparison generator, and its output is used to justify the factual accuracy of the generated comparison text, by training a factual consistency model to detect and correct errors in the generated comparative text. We release a new dataset (https://registry.opendata.aws/) of ~15K human generated sentences, comparing products on one or more attributes (the first such data we know of for product comparison). We demonstrate on this data that HCPC significantly outperforms strong baselines, by ~10% using automatic metrics, and ~5% using human evaluation.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"2 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":"127700629","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: 4

Under the Hood of Social Media Advertising: How Do We use AI Responsibly for Advertising Targeting and Creative Evaluation 社交媒体广告的背后:我们如何负责任地使用人工智能进行广告定位和创造性评估

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3575791

Aleksandr Farseev

{"title":"Under the Hood of Social Media Advertising: How Do We use AI Responsibly for Advertising Targeting and Creative Evaluation","authors":"Aleksandr Farseev","doi":"10.1145/3539597.3575791","DOIUrl":"https://doi.org/10.1145/3539597.3575791","url":null,"abstract":"Digital Advertising is historically one of the most developed areas where Machine Learning and AI have been applied since its origination. From smart bidding to creative content generation and DCO, AI is well-demanded in the modern digital marketing industry and partially serves as a backbone of most of the state-of-the-art computational advertising systems, making them impossible for the AI tech and the programmatic systems to exist apart from one another. At the same time, given the drastic growth of the available AI technology nowadays, the issue of responsible AI utilization as well as the balance between the opportunity of deploying AI systems and the possible borderline etic and privacy-related consequences are still yet to be discussed comprehensively in both business and research communities. Particularly, an important issue of automatic User Profiling use in modern Programmatic systems like Meta Ads as well as the need for responsible application of the creative assessment models to fit into the business etic guidelines is yet to be described well. Therefore, in this talk, we are going to discuss the technology behind modern programmatic bidding and content scoring systems and the responsible application of AI by SoMin.ai to manage the Advertising targeting and Creative Validation process.","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":"129681567","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

Counterfactual Collaborative Reasoning 反事实协同推理

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570464

Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang

{"title":"Counterfactual Collaborative Reasoning","authors":"Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang","doi":"10.1145/3539597.3570464","DOIUrl":"https://doi.org/10.1145/3539597.3570464","url":null,"abstract":"Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability--counterfactual reasoning and (neural) logical reasoning--we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate \"difficult\" counterfactual training examples for data augmentation, which--together with the original training examples--can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on \"implicit data augmentation\" over users' implicit feedback, while our framework conducts \"explicit data augmentation\" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations.","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":"129682388","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: 6

Weakly Supervised Entity Alignment with Positional Inspiration 具有位置启发的弱监督实体对齐

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570394

Wei Tang, Fenglong Su, Haifeng Sun, Q. Qi, Jingyu Wang, Shimin Tao, Hao Yang

{"title":"Weakly Supervised Entity Alignment with Positional Inspiration","authors":"Wei Tang, Fenglong Su, Haifeng Sun, Q. Qi, Jingyu Wang, Shimin Tao, Hao Yang","doi":"10.1145/3539597.3570394","DOIUrl":"https://doi.org/10.1145/3539597.3570394","url":null,"abstract":"The current success of entity alignment (EA) is still mainly based on large-scale labeled anchor links. However, the refined annotation of anchor links still consumes a lot of manpower and material resources. As a result, an increasing number of works based on active learning, few-shot learning, or other deep network learning techniques have been developed to address the performance bottleneck caused by a lack of labeled data. These works focus either on the strategy of choosing more informative labeled data or on the strategy of model training, while it remains opaque why existing popular EA models (e.g., GNN-based models) fail the EA task with limited labeled data. To overcome this issue, this paper analyzes the problem of weakly supervised EA from the perspective of model design and proposes a novel weakly supervised learning framework, Position Enhanced Entity Alignment (PEEA). Besides absorbing structural and relational information, PEEA aims to increase the connections between far-away entities and labeled ones by incorporating positional information into the representation learning with a Position Attention Layer (PAL). To fully utilize the limited anchor links, we further introduce a novel position encoding method that considers both anchor links and relational information from a global view. The proposed position encoding will be fed into PEEA as additional entity features. Extensive experiments on public datasets demonstrate the effectiveness of PEEA.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"77 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":"121109973","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