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

BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs 基于有偏邻域抽样的有向图神经网络

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570430

Srinivas Virinchi, Anoop Saladi

{"title":"BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs","authors":"Srinivas Virinchi, Anoop Saladi","doi":"10.1145/3539597.3570430","DOIUrl":"https://doi.org/10.1145/3539597.3570430","url":null,"abstract":"Directed graphs are ubiquitous and have applications across multiple domains including citation, website, social, and traffic networks. Yet, majority of research involving graph neural networks (GNNs) focus on undirected graphs. In this paper, we deal with the problem of node recommendation in directed graphs. Specifically, given a directed graph and query node as input, the goal is to recommend top- nodes that have a high likelihood of a link with the query node. Here we propose BLADE, a novel GNN to model directed graphs. In order to jointly capture link likelihood and link direction, we employ an asymmetric loss function and learn dual embeddings for each node, by appropriately aggregating features from its neighborhood. In order to achieve optimal performance on both low and high-degree nodes, we employ a biased neighborhood sampling scheme that generates locally varying neighborhoods which differ based on a node's connectivity structure. Extensive experimentation on several open-source and proprietary directed graphs show that BLADE outperforms state-of-the-art baselines by 6-230% in terms of HitRate and MRR for the node recommendation task and 10.5% in terms of AUC for the link direction prediction task. We perform ablation study to accentuate the importance of biased neighborhood sampling employed in generating higher quality recommendations for both low-degree and high-degree query nodes. Further, BLADE delivers significant improvement in revenue and sales as measured through an A/B experiment.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"97 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":"123061718","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 Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter 在Twitter上识别疫苗关键模因的多模式框架

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570450

Usman Naseem, Jinman Kim, Matloob Khushi, A. Dunn

{"title":"A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter","authors":"Usman Naseem, Jinman Kim, Matloob Khushi, A. Dunn","doi":"10.1145/3539597.3570450","DOIUrl":"https://doi.org/10.1145/3539597.3570450","url":null,"abstract":"Memes can be a useful way to spread information because they are funny, easy to share, and can spread quickly and reach further than other forms. With increased interest in COVID-19 vaccines, vaccination-related memes have grown in number and reach. Memes analysis can be difficult because they use sarcasm and often require contextual understanding. Previous research has shown promising results but could be improved by capturing global and local representations within memes to model contextual information. Further, the limited public availability of annotated vaccine critical memes datasets limit our ability to design computational methods to help design targeted interventions and boost vaccine uptake. To address these gaps, we present VaxMeme, which consists of 10,244 manually labelled memes. With VaxMeme, we propose a new multimodal framework designed to improve the memes' representation by learning the global and local representations of memes. The improved memes' representations are then fed to an attentive representation learning module to capture contextual information for classification using an optimised loss function. Experimental results show that our framework outperformed state-of-the-art methods with an F1-Score of 84.2%. We further analyse the transferability and generalisability of our framework and show that understanding both modalities is important to identify vaccine critical memes on Twitter. Finally, we discuss how understanding memes can be useful in designing shareable vaccination promotion, myth debunking memes and monitoring their uptake on social media platforms.","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":"116659465","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 Causal View for Item-level Effect of Recommendation on User Preference 商品推荐对用户偏好影响的因果分析

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570461

Wei Cai, Fuli Feng, Qifan Wang, Tian Yang, Zhenguang Liu, Congfu Xu

{"title":"A Causal View for Item-level Effect of Recommendation on User Preference","authors":"Wei Cai, Fuli Feng, Qifan Wang, Tian Yang, Zhenguang Liu, Congfu Xu","doi":"10.1145/3539597.3570461","DOIUrl":"https://doi.org/10.1145/3539597.3570461","url":null,"abstract":"Recommender systems not only serve users but also affect user preferences through personalized recommendations. Recent researches investigate the effects of the entire recommender system on user preferences, i.e., system-level effects, and find that recommendations may lead to problems such as echo chambers and filter bubbles. To properly alleviate the problems, it is necessary to estimate the effects of recommending a specific item on user preferences, i.e., item-level effects. For example, by understanding whether recommending an item aggravates echo chambers, we can better decide whether to recommend it or not. This work designs a method to estimate the item-level effects from the causal perspective. We resort to causal graphs to characterize the average treatment effect of recommending an item on the preference of another item. The key to estimating the effects lies in mitigating the confounding bias of time and user features without the costly randomized control trials. Towards the goal, we estimate the causal effects from historical observations through a method with stratification and matching to address the two confounders, respectively. Nevertheless, directly implementing stratification and matching is intractable, which requires high computational cost due to the large sample size. We thus propose efficient approximations of stratification and matching to reduce the computation complexity. Extensive experimental results on two real-world datasets validate the effectiveness and efficiency of our method. We also show a simple example of using the item-level effects to provide insights for mitigating echo chambers.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"7 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":"116771816","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

DOCoR: Document-level OpenIE with Coreference Resolution DOCoR:具有共同参考分辨率的文档级OpenIE

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573038

S. Yong, Kuicai Dong, Aixin Sun

引用次数: 0

Knowledge-Adaptive Contrastive Learning for Recommendation 推荐的知识适应对比学习

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570483

Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, Zhiyuan Liu

{"title":"Knowledge-Adaptive Contrastive Learning for Recommendation","authors":"Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, Zhiyuan Liu","doi":"10.1145/3539597.3570483","DOIUrl":"https://doi.org/10.1145/3539597.3570483","url":null,"abstract":"By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"16 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":"115096383","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: 11

Beyond Individuals: Modeling Mutual and Multiple Interactions for Inductive Link Prediction between Groups 超越个体:为群体间的归纳链接预测建立相互和多重互动的模型

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570448

Gong Yin, Xing Wang, Hongli Zhang, Chao Meng, Yuchen Yang, Kun Lu, Yi Luo

{"title":"Beyond Individuals: Modeling Mutual and Multiple Interactions for Inductive Link Prediction between Groups","authors":"Gong Yin, Xing Wang, Hongli Zhang, Chao Meng, Yuchen Yang, Kun Lu, Yi Luo","doi":"10.1145/3539597.3570448","DOIUrl":"https://doi.org/10.1145/3539597.3570448","url":null,"abstract":"Link prediction is a core task in graph machine learning with wide applications. However, little attention has been paid to link prediction between two group entities. This limits the application of the current approaches to many real-life problems, such as predicting collaborations between academic groups or recommending bundles of items to group users. Moreover, groups are often ephemeral or emergent, forcing the predicting model to deal with challenging inductive scenes. To fill this gap, we develop a framework composed of a GNN-based encoder and neural-based aggregating networks, namely the Mutual Multi-view Attention Networks (MMAN). First, we adopt GNN-based encoders to model multiple interactions among members and groups through propagating. Then, we develop MMAN to aggregate members' node representations into multi-view group representations and compute the final results by pooling pairwise scores between views. Specifically, several view-guided attention modules are adopted when learning multi-view group representations, thus capturing diversified member weights and multifaceted group characteristics. In this way, MMAN can further mimic the mutual and multiple interactions between groups. We conduct experiments on three datasets, including two academic group link prediction datasets and one bundle-to-group recommendation dataset. The results demonstrate that the proposed approach can achieve superior performance on both tasks compared with plain GNN-based methods and other aggregating methods.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"8 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":"122376047","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

Relation Preference Oriented High-order Sampling for Recommendation 面向关系偏好的高阶推荐抽样

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570424

Mukun Chen, Xiuwen Gong, YH Jin, Wenbin Hu

{"title":"Relation Preference Oriented High-order Sampling for Recommendation","authors":"Mukun Chen, Xiuwen Gong, YH Jin, Wenbin Hu","doi":"10.1145/3539597.3570424","DOIUrl":"https://doi.org/10.1145/3539597.3570424","url":null,"abstract":"The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results. Therefore, we propose a Relation Preference oriented High-order Sampling (RPHS) model to dynamically sample subgraphs based on relation preference and hard negative samples for user-item pairs. We design a path sampling strategy based on relation preference, which can encode the critical paths between specific user-item pairs to sample the paths in the high-order message passing subgraphs. Next, we design a mixed sampling strategy and define a new propagation operation to further enhance RPHS's ability to distinguish negative signals. Through the above sampling strategies, our model can better aggregate local relation preference information and reveal the internal relationship between users' preferences. Experiments show that our model outperforms the state-of-the-art models on three datasets by 14.98%, 5.31%, and 8.65%, and also performs well in terms of interpretability. The codes are available at https://github.com/RPHS/RPHS.git","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"25 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":"122217077","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

A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping 基于个性化邻域的杂货购物篮内推荐模型

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570417

Mozhdeh Ariannezhad, Ming Li, Sebastian Schelter, M. de Rijke

{"title":"A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping","authors":"Mozhdeh Ariannezhad, Ming Li, Sebastian Schelter, M. de Rijke","doi":"10.1145/3539597.3570417","DOIUrl":"https://doi.org/10.1145/3539597.3570417","url":null,"abstract":"Users of online shopping platforms typically purchase multiple items at a time in the form of a shopping basket. Personalized within-basket recommendation is the task of recommending items to complete an incomplete basket during a shopping session. In contrast to the related task of session-based recommendation, where the goal is to complete an ongoing anonymous session, we have access to the shopping history of the user in within-basket recommendation. Previous studies have shown the superiority of neighborhood-based models for session-based recommendation and the importance of personal history in the grocery shopping domain. But their applicability in within-basket recommendation remains unexplored. We propose PerNIR, a neighborhood-based model that explicitly models the personal history of users for within-basket recommendation in grocery shopping. The main novelty of PerNIR is in modeling the short-term interests of users, which are represented by the current basket, as well as their long-term interest, which is reflected in their purchasing history. In addition to the personal history, user neighbors are used to capture the collaborative purchase behavior. We evaluate PerNIR on two public and proprietary datasets. The experimental results show that it outperforms 10 state-of-the-art competitors with a significant margin, i.e., with gains of more than 12% in terms of hit rate over the second best performing approach. Additionally, we showcase an optimized implementation of our method, which computes recommendations fast enough for real-world production scenarios.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"51 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":"129291783","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

Simulating Humans at Scale to Evaluate Voice Interfaces for TVs: the Round-Trip System at Comcast 大规模模拟人类以评估电视语音接口:康卡斯特的往返系统

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3575787

B. Baldwin, Lauren Reese, LIming Zhang, Jan Neumann, Taylor Cassidy, Michael Pereira, G. C. Murray, Kishorekumar Sundararajan, Yidnekachew Endale, Pramod Kadagattor, Paul Wolfe, Brian Aiken, Tony Braskich, Donte Jiggetts, Adam Sloan, Esther Vaturi, Crystal Pender, Ferhan Ture

引用次数: 0

A Synthetic Search Session Generator for Task-Aware Information Seeking and Retrieval 面向任务感知信息搜索与检索的综合搜索会话生成器

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573041

Shawon Sarkar, C. Shah

{"title":"A Synthetic Search Session Generator for Task-Aware Information Seeking and Retrieval","authors":"Shawon Sarkar, C. Shah","doi":"10.1145/3539597.3573041","DOIUrl":"https://doi.org/10.1145/3539597.3573041","url":null,"abstract":"For users working on a complex search task, it is common to address different goals at various stages of the task through query iterations. While addressing these goals, users go through different task states as well. Understanding these task states latent under users' interactions is crucial in identifying users' changing intents and search behaviors to simulate and achieve real-time adaptive search recommendations and retrievals. However, the availability of sizeable real-world web search logs is scarce due to various ethical and privacy concerns, thus often challenging to develop generalizable task-aware computation models. Furthermore, session logs with task state labels are rarer. For many researchers who lack the resources to directly and at scale collect data from users and conduct a time-consuming data annotation process, this becomes a considerable bottleneck to furthering their research. Synthetic search sessions have the potential to address this gap. This paper shares a parsimonious model to simulate synthetic web search sessions with task state information, which interactive information retrieval (IIR) and search personalization studies could utilize to develop and evaluate task-based search and retrieval systems.","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":"130049857","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