{"title":"Multi-Granular Text Classification with Minimal Supervision","authors":"Yunyi Zhang","doi":"10.1145/3616855.3635735","DOIUrl":"https://doi.org/10.1145/3616855.3635735","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140286091","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}
{"title":"Augmenting Keyword-based Search in Mobile Applications Using LLMs","authors":"Harikrishnan C, Giridhar Sreenivasa Murthy, Kumar Rangarajan","doi":"10.1145/3616855.3635745","DOIUrl":"https://doi.org/10.1145/3616855.3635745","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285677","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}
{"title":"The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)","authors":"Xin Luna Dong","doi":"10.1145/3616855.3638207","DOIUrl":"https://doi.org/10.1145/3616855.3638207","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"13 6‐7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285664","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}
{"title":"Automated Tailoring of Large Language Models for Industry-Specific Downstream Tasks","authors":"Shreya Saxena, Siva Prasad, Muneeswaran I, Advaith Shankar, Varun V, Saisubramaniam Gopalakrishnan, Vishal Vaddina","doi":"10.1145/3616855.3635743","DOIUrl":"https://doi.org/10.1145/3616855.3635743","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"101 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140286061","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}
{"title":"Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning","authors":"Mingyuan Fan, Yang Liu, Cen Chen, Chengyu Wang, Minghui Qiu, Wenmeng Zhou","doi":"10.1145/3616855.3635758","DOIUrl":"https://doi.org/10.1145/3616855.3635758","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285682","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}
Ghazaleh Haratinezhad Torbati, Anna Tigunova, G. Weikum
{"title":"SIRUP: Search-based Book Recommendation Playground","authors":"Ghazaleh Haratinezhad Torbati, Anna Tigunova, G. Weikum","doi":"10.1145/3616855.3635692","DOIUrl":"https://doi.org/10.1145/3616855.3635692","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285694","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}
Dynamic graphs play an important role in many fields like social relationship analysis, recommender systems and medical science, as graphs evolve over time. It is fundamental to capture the evolution patterns for dynamic graphs. Existing works mostly focus on constraining the temporal smoothness between neighbor snap-shots, however, fail to capture sharp shifts, which can be beneficial for graph dynamics embedding. To solve it, we assume the evolution of dynamic graph nodes can be split into temporal shift embedding and temporal consistency embedding. Thus, we propose the Self-supervised Temporal-aware Dynamic Graph representation Learning framework (STDGL) for disentangling the temporal shift embedding from temporal consistency embedding via a well-designed auxiliary task from the perspectives of both node local and global connectivity modeling in a self-supervised manner, further enhancing the learning of interpretable graph representations and improving the performance of various downstream tasks. Extensive experiments on link prediction, edge classification and node classification tasks demonstrate STDGL successfully learns the disentan-gled temporal shift and consistency representations. Furthermore, the results indicate significant improvements in our STDGL over the state-of-the-art methods, and appealing interpretability and transferability owing to the disentangled node representations.
{"title":"Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning","authors":"Lingwen Liu, Guangqi Wen, Peng Cao, Jinzhu Yang, Weiping Li, Osmar R. Zaiane","doi":"10.1145/3616855.3635765","DOIUrl":"https://doi.org/10.1145/3616855.3635765","url":null,"abstract":"Dynamic graphs play an important role in many fields like social relationship analysis, recommender systems and medical science, as graphs evolve over time. It is fundamental to capture the evolution patterns for dynamic graphs. Existing works mostly focus on constraining the temporal smoothness between neighbor snap-shots, however, fail to capture sharp shifts, which can be beneficial for graph dynamics embedding. To solve it, we assume the evolution of dynamic graph nodes can be split into temporal shift embedding and temporal consistency embedding. Thus, we propose the Self-supervised Temporal-aware Dynamic Graph representation Learning framework (STDGL) for disentangling the temporal shift embedding from temporal consistency embedding via a well-designed auxiliary task from the perspectives of both node local and global connectivity modeling in a self-supervised manner, further enhancing the learning of interpretable graph representations and improving the performance of various downstream tasks. Extensive experiments on link prediction, edge classification and node classification tasks demonstrate STDGL successfully learns the disentan-gled temporal shift and consistency representations. Furthermore, the results indicate significant improvements in our STDGL over the state-of-the-art methods, and appealing interpretability and transferability owing to the disentangled node representations.","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"27 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285669","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}
Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis
{"title":"Unbiased Learning to Rank: On Recent Advances and Practical Applications","authors":"Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis","doi":"10.1145/3616855.3636451","DOIUrl":"https://doi.org/10.1145/3616855.3636451","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"96 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140286063","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}
{"title":"Automatic Extraction of Patterns in Digital News Articles of Femicides occurred in Mexico by Text Mining Techniques","authors":"Jonathan Zárate-Cartas, Alejandro Molina-Villegas","doi":"10.1145/3616855.3636503","DOIUrl":"https://doi.org/10.1145/3616855.3636503","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140286123","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}
{"title":"WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data","authors":"Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif","doi":"10.1145/3616855.3635698","DOIUrl":"https://doi.org/10.1145/3616855.3635698","url":null,"abstract":"","PeriodicalId":517585,"journal":{"name":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","volume":"14 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285687","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：