Changchun Li, Ximing Li, Jihong Ouyang, Yiming Wang
Ambiguous Label Learning (ALL), as an emerging paradigm of weakly supervised learning, aims to induce the prediction model from training datasets with ambiguous supervision, where, specifically, each training instance is annotated with a set of candidate labels but only one is valid. To handle this task, the existing shallow methods mainly disambiguate the candidate labels by leveraging various regularization techniques. Inspired by the great success of deep generative adversarial networks, we apply it to perform effective candidate label disambiguation from a new instance-pivoted perspective. Specifically, for each ALL instance, we recombine its feature representation with each of candidate labels to generate a set of candidate instances, where only one is real and all others are fake. We formulate a unified adversarial objective with respect to three players, i.e., a discriminator, a generator, and a classifier. The discriminator is used to detect the fake candidate instances, so that the classifier can be trained without them. With this insight, we develop a novel ALL method, namely Adversarial Ambiguous Label Learning with Candidate Instance Detection (A2L2CID). Theoretically, we analyze that there is a global equilibrium point between the three players. Empirically, extensive experimental results indicate that A2L2CID outperforms the state-of-the-art ALL methods.
{"title":"Detecting the Fake Candidate Instances: Ambiguous Label Learning with Generative Adversarial Networks","authors":"Changchun Li, Ximing Li, Jihong Ouyang, Yiming Wang","doi":"10.1145/3459637.3482251","DOIUrl":"https://doi.org/10.1145/3459637.3482251","url":null,"abstract":"Ambiguous Label Learning (ALL), as an emerging paradigm of weakly supervised learning, aims to induce the prediction model from training datasets with ambiguous supervision, where, specifically, each training instance is annotated with a set of candidate labels but only one is valid. To handle this task, the existing shallow methods mainly disambiguate the candidate labels by leveraging various regularization techniques. Inspired by the great success of deep generative adversarial networks, we apply it to perform effective candidate label disambiguation from a new instance-pivoted perspective. Specifically, for each ALL instance, we recombine its feature representation with each of candidate labels to generate a set of candidate instances, where only one is real and all others are fake. We formulate a unified adversarial objective with respect to three players, i.e., a discriminator, a generator, and a classifier. The discriminator is used to detect the fake candidate instances, so that the classifier can be trained without them. With this insight, we develop a novel ALL method, namely Adversarial Ambiguous Label Learning with Candidate Instance Detection (A2L2CID). Theoretically, we analyze that there is a global equilibrium point between the three players. Empirically, extensive experimental results indicate that A2L2CID outperforms the state-of-the-art ALL methods.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849095","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}
Muhammad Shihab Rashid, Fuad Jamour, Vagelis Hristidis
Frequently Asked Questions (FAQ) are a form of semi-structured data that provides users with commonly requested information and enables several natural language processing tasks. Given the plethora of such question-answer pairs on the Web, there is an opportunity to automatically build large FAQ collections for any domain, such as COVID-19 or Plastic Surgery. These collections can be used by several information-seeking portals and applications, such as AI chatbots. Automatically identifying and extracting such high-utility question-answer pairs is a challenging endeavor, which has been tackled by little research work. For a question-answer pair to be useful to a broad audience, it must (i) provide general information -- not be specific to the Web site or Web page where it is hosted -- and (ii) must be self-contained -- not have references to other entities in the page or missing terms (ellipses) that render the question-answer pair ambiguous. Although identifying general, self-contained questions may seem like a straightforward binary classification problem, the limited availability of training data for this task and the countless domains make building machine learning models challenging. Existing efforts in extracting FAQs from the Web typically focus on FAQ retrieval without much regard to the utility of the extracted FAQ. We propose QuAX: a framework for extracting high-utility (i.e., general and self-contained) domain-specific FAQ lists from the Web. QuAX receives a set of keywords from a user, and works in a pipelined fashion to find relevant web pages and extract general and self-contained questions-answer pairs. We experimentally show how QuAX generates high-utility FAQ collections with little and domain-agnostic training data, and how the individual stages of the pipeline improve on the corresponding state-of-the-art.
{"title":"QuAX","authors":"Muhammad Shihab Rashid, Fuad Jamour, Vagelis Hristidis","doi":"10.1145/3459637.3482289","DOIUrl":"https://doi.org/10.1145/3459637.3482289","url":null,"abstract":"Frequently Asked Questions (FAQ) are a form of semi-structured data that provides users with commonly requested information and enables several natural language processing tasks. Given the plethora of such question-answer pairs on the Web, there is an opportunity to automatically build large FAQ collections for any domain, such as COVID-19 or Plastic Surgery. These collections can be used by several information-seeking portals and applications, such as AI chatbots. Automatically identifying and extracting such high-utility question-answer pairs is a challenging endeavor, which has been tackled by little research work. For a question-answer pair to be useful to a broad audience, it must (i) provide general information -- not be specific to the Web site or Web page where it is hosted -- and (ii) must be self-contained -- not have references to other entities in the page or missing terms (ellipses) that render the question-answer pair ambiguous. Although identifying general, self-contained questions may seem like a straightforward binary classification problem, the limited availability of training data for this task and the countless domains make building machine learning models challenging. Existing efforts in extracting FAQs from the Web typically focus on FAQ retrieval without much regard to the utility of the extracted FAQ. We propose QuAX: a framework for extracting high-utility (i.e., general and self-contained) domain-specific FAQ lists from the Web. QuAX receives a set of keywords from a user, and works in a pipelined fashion to find relevant web pages and extract general and self-contained questions-answer pairs. We experimentally show how QuAX generates high-utility FAQ collections with little and domain-agnostic training data, and how the individual stages of the pipeline improve on the corresponding state-of-the-art.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115848260","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}
In recent years, graph contrastive learning has achieved promising node classification accuracy using graph neural networks (GNNs), which can learn representations in an unsupervised manner. However, such representations cannot be generalized to unseen novel classes with only few-shot labeled samples in spite of exhibiting good performance on seen classes. In order to assign generalization capability to graph contrastive learning, we propose multimodal graph meta contrastive learning (MGMC) in this paper, which integrates multimodal meta learning into graph contrastive learning. On one hand, MGMC accomplishes effectively fast adapation on unseen novel classes by the aid of bilevel meta optimization to solve few-shot problems. On the other hand, MGMC can generalize quickly to a generic dataset with multimodal distribution by inducing the FiLM-based modulation module. In addition, MGMC incorporates the lastest graph contrastive learning method that does not rely on the onstruction of augmentations and negative examples. To our best knowledge, this is the first work to investigate graph contrastive learning for few-shot problems. Extensieve experimental results on three graph-structure datasets demonstrate the effectiveness of our proposed MGMC in few-shot node classification tasks.
{"title":"Multimodal Graph Meta Contrastive Learning","authors":"Feng Zhao, Donglin Wang","doi":"10.1145/3459637.3482151","DOIUrl":"https://doi.org/10.1145/3459637.3482151","url":null,"abstract":"In recent years, graph contrastive learning has achieved promising node classification accuracy using graph neural networks (GNNs), which can learn representations in an unsupervised manner. However, such representations cannot be generalized to unseen novel classes with only few-shot labeled samples in spite of exhibiting good performance on seen classes. In order to assign generalization capability to graph contrastive learning, we propose multimodal graph meta contrastive learning (MGMC) in this paper, which integrates multimodal meta learning into graph contrastive learning. On one hand, MGMC accomplishes effectively fast adapation on unseen novel classes by the aid of bilevel meta optimization to solve few-shot problems. On the other hand, MGMC can generalize quickly to a generic dataset with multimodal distribution by inducing the FiLM-based modulation module. In addition, MGMC incorporates the lastest graph contrastive learning method that does not rely on the onstruction of augmentations and negative examples. To our best knowledge, this is the first work to investigate graph contrastive learning for few-shot problems. Extensieve experimental results on three graph-structure datasets demonstrate the effectiveness of our proposed MGMC in few-shot node classification tasks.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"427 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124229682","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}
Collaborative filtering (CF) methods are making an impact on our daily lives in a wide range of applications, including recommender systems and personalization. Latent factor methods, e.g., matrix factorization (MF), have been the state-of-the-art in CF, however they lack interpretability and do not provide a straightforward explanation for their predictions. Explainability is gaining momentum in recommender systems for accountability, and because a good explanation can swing an undecided user. Most recent explainable recommendation methods require auxiliary data such as review text or item content on top of item ratings. In this paper, we address the case where no additional data are available and propose augmenting the classical MF framework for CF with a prior that encodes each user's embedding as a sparse linear combination of item embeddings, and vice versa for each item embedding. Our XPL-CF approach automatically reveals these user-item relationships, which underpin the latent factors and explain how the resulting recommendations are formed. We showcase the effectiveness of XPL-CF on real data from various application domains. We also evaluate the explainability of the user-item relationship obtained from XPL-CF through numeric evaluation and case study examples.
{"title":"XPL-CF: Explainable Embeddings for Feature-based Collaborative Filtering","authors":"Faisal M. Almutairi, N. Sidiropoulos, Bo Yang","doi":"10.1145/3459637.3482221","DOIUrl":"https://doi.org/10.1145/3459637.3482221","url":null,"abstract":"Collaborative filtering (CF) methods are making an impact on our daily lives in a wide range of applications, including recommender systems and personalization. Latent factor methods, e.g., matrix factorization (MF), have been the state-of-the-art in CF, however they lack interpretability and do not provide a straightforward explanation for their predictions. Explainability is gaining momentum in recommender systems for accountability, and because a good explanation can swing an undecided user. Most recent explainable recommendation methods require auxiliary data such as review text or item content on top of item ratings. In this paper, we address the case where no additional data are available and propose augmenting the classical MF framework for CF with a prior that encodes each user's embedding as a sparse linear combination of item embeddings, and vice versa for each item embedding. Our XPL-CF approach automatically reveals these user-item relationships, which underpin the latent factors and explain how the resulting recommendations are formed. We showcase the effectiveness of XPL-CF on real data from various application domains. We also evaluate the explainability of the user-item relationship obtained from XPL-CF through numeric evaluation and case study examples.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116276788","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}
Radin Hamidi Rad, A. Mitha, Hossein Fani, M. Kargar, Jaroslaw Szlichta, E. Bagheri
We present PyTFL, a library written in Python for the team formation task. In team formation task, the main objective is to form a team of experts given a set of skills. We demonstrate an efficient and well-structured open-source toolkit that can easily be imported into Python. Our toolkit incorporates state-of-the-art approaches for team formation, e.g., neural-based team formation, and supports team formation sub-tasks such as collaboration graph preparation, model training and validation, systematic evaluation based on qualitative and quantitative team metrics, and efficient team formation and prediction. While there are strong research papers on the team formation problem, PyTFL is the first toolkit to be publicly released for this purpose.
{"title":"PyTFL","authors":"Radin Hamidi Rad, A. Mitha, Hossein Fani, M. Kargar, Jaroslaw Szlichta, E. Bagheri","doi":"10.1145/3459637.3481992","DOIUrl":"https://doi.org/10.1145/3459637.3481992","url":null,"abstract":"We present PyTFL, a library written in Python for the team formation task. In team formation task, the main objective is to form a team of experts given a set of skills. We demonstrate an efficient and well-structured open-source toolkit that can easily be imported into Python. Our toolkit incorporates state-of-the-art approaches for team formation, e.g., neural-based team formation, and supports team formation sub-tasks such as collaboration graph preparation, model training and validation, systematic evaluation based on qualitative and quantitative team metrics, and efficient team formation and prediction. While there are strong research papers on the team formation problem, PyTFL is the first toolkit to be publicly released for this purpose.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122034350","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}
Kunxun Qi, Ruoxu Wang, Qikai Lu, Xuejiao Wang, Ning Jing, Di Niu, Haolan Chen
Query feeds recommendation is a new recommended paradigm in mobile search applications, where a stream of queries need to be recommended to improve user engagement. It requires a great quantity of attractive queries for recommendation. A conventional solution is to retrieve queries from a collection of past queries recorded in user search logs. However, these queries usually have poor readability and limited coverage of article content, and are thus not suitable for the query feeds recommendation scenario. Furthermore, to deploy the generated queries for recommendation, human validation, which is costly in practice, is required to filter unsuitable queries. In this paper, we propose TitIE, a query mining system to generate valuable queries using the titles of documents. We employ both an extractive text generator and an abstractive text generator to generate queries from titles. To improve the acceptance rate during human validation, we further propose a model-based scoring strategy to pre-select the queries that are more likely to be accepted during human validation. Finally, we propose a novel dual learning approach to jointly learn the generation model and the selection model by making full use of the unlabeled corpora under a semi-supervised scheme, thereby simultaneously improving the performance of both models. Results from both offline and online evaluations demonstrate the superiority of our approach.
{"title":"Dual Learning for Query Generation and Query Selection in Query Feeds Recommendation","authors":"Kunxun Qi, Ruoxu Wang, Qikai Lu, Xuejiao Wang, Ning Jing, Di Niu, Haolan Chen","doi":"10.1145/3459637.3481910","DOIUrl":"https://doi.org/10.1145/3459637.3481910","url":null,"abstract":"Query feeds recommendation is a new recommended paradigm in mobile search applications, where a stream of queries need to be recommended to improve user engagement. It requires a great quantity of attractive queries for recommendation. A conventional solution is to retrieve queries from a collection of past queries recorded in user search logs. However, these queries usually have poor readability and limited coverage of article content, and are thus not suitable for the query feeds recommendation scenario. Furthermore, to deploy the generated queries for recommendation, human validation, which is costly in practice, is required to filter unsuitable queries. In this paper, we propose TitIE, a query mining system to generate valuable queries using the titles of documents. We employ both an extractive text generator and an abstractive text generator to generate queries from titles. To improve the acceptance rate during human validation, we further propose a model-based scoring strategy to pre-select the queries that are more likely to be accepted during human validation. Finally, we propose a novel dual learning approach to jointly learn the generation model and the selection model by making full use of the unlabeled corpora under a semi-supervised scheme, thereby simultaneously improving the performance of both models. Results from both offline and online evaluations demonstrate the superiority of our approach.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841855","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}
Xiaosu Wang, Yun Xiong, Hao Niu, Jingwen Yue, Yangyong Zhu, Philip S. Yu
Chinese characters are often composed of subcharacter components which are also semantically informative, and the component-level internal semantic features of a Chinese character inherently bring with additional information that benefits the semantic representation of the character. Therefore, there have been several studies that utilized subcharacter component information (e.g. radical, fine-grained components and stroke n-grams) to improve Chinese character representation. However we argue that it has not been fully explored what would be the best way of modeling and encoding a Chinese character. For improving the representation of a Chinese character, existing methods introduce more component-level internal semantic features as well as more semantic irrelevant subcharacter component information, and these semantic irrelevant subcharacter component will be noisy for representing a Chinese character. Moreover, existing methods suffer from the inability of discriminating the importance of the introduced subcharacter components, accordingly they can not filter out introduced noisy subcharacter component information. In this paper, we first decompose Chinese characters into components according to their formations, then model a Chinese character and its decomposed components as a graph structure named Chinese character formation graph; Chinese character formation graph can reserve the azimuth relationship among subcharacter components, and be advantageous to explicitly model the component-level internal semantic features of a Chinese character. Furtherly, we propose a novel model Chinese Character Formation Graph Attention Network (FGAT) which is able to discriminate the importance of the introduced subcharacter components and extract component-level internal semantic features of a Chinese character efficiently. To demonstrate the effectiveness of our research, we have conducted extensive experiments. The experimental results show that our model achieves better results than state-of-the-art (SOTA) approaches.
{"title":"Improving Chinese Character Representation with Formation Graph Attention Network","authors":"Xiaosu Wang, Yun Xiong, Hao Niu, Jingwen Yue, Yangyong Zhu, Philip S. Yu","doi":"10.1145/3459637.3482265","DOIUrl":"https://doi.org/10.1145/3459637.3482265","url":null,"abstract":"Chinese characters are often composed of subcharacter components which are also semantically informative, and the component-level internal semantic features of a Chinese character inherently bring with additional information that benefits the semantic representation of the character. Therefore, there have been several studies that utilized subcharacter component information (e.g. radical, fine-grained components and stroke n-grams) to improve Chinese character representation. However we argue that it has not been fully explored what would be the best way of modeling and encoding a Chinese character. For improving the representation of a Chinese character, existing methods introduce more component-level internal semantic features as well as more semantic irrelevant subcharacter component information, and these semantic irrelevant subcharacter component will be noisy for representing a Chinese character. Moreover, existing methods suffer from the inability of discriminating the importance of the introduced subcharacter components, accordingly they can not filter out introduced noisy subcharacter component information. In this paper, we first decompose Chinese characters into components according to their formations, then model a Chinese character and its decomposed components as a graph structure named Chinese character formation graph; Chinese character formation graph can reserve the azimuth relationship among subcharacter components, and be advantageous to explicitly model the component-level internal semantic features of a Chinese character. Furtherly, we propose a novel model Chinese Character Formation Graph Attention Network (FGAT) which is able to discriminate the importance of the introduced subcharacter components and extract component-level internal semantic features of a Chinese character efficiently. To demonstrate the effectiveness of our research, we have conducted extensive experiments. The experimental results show that our model achieves better results than state-of-the-art (SOTA) approaches.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124062101","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}
Jiayu Song, Jiajie Xu, Rui Zhou, Lu Chen, Jianxin Li, Chengfei Liu
Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches.
{"title":"CBML","authors":"Jiayu Song, Jiajie Xu, Rui Zhou, Lu Chen, Jianxin Li, Chengfei Liu","doi":"10.1145/3459637.3482239","DOIUrl":"https://doi.org/10.1145/3459637.3482239","url":null,"abstract":"Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124075465","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}
Paper-publication venue prediction aims to predict candidate publication venues that effectively suit given submissions. This technology is developing rapidly with the popularity of machine learning models. However, most previous methods ignore the structure information of papers, while modeling them with graphs can naturally solve this drawback. Meanwhile, they either use hand-crafted or bag-of-word features to represent the papers, ignoring the ones that involve high-level semantics. Moreover, existing methods assume that the venue where a paper is published as a correct venue for the data annotation, which is unrealistic. One paper can be relevant to many venues. In this paper, we attempt to address these problems above and develop a novel prediction model, namelyVenue Prediction with Abstract-Level Graph (Vpalg xspace), which can serve as an effective decision-making tool for venue selections. Specifically, to achieve more discriminative paper abstract representations, we construct each abstract as a semantic graph and perform a dual attention message passing neural network for representation learning. Then, the proposed model can be trained over the learned abstract representations with their labels and generalized via self-training. Empirically, we employ the PubMed dataset and further collect two new datasets from the top journals and conferences in computer science. Experimental results indicate the superior performance of Vpalg xspace, consistently outperforming the existing baseline methods.
{"title":"VPALG","authors":"Renchu Guan, Yonghao Liu, Xiaoyue Feng, Ximing Li","doi":"10.1145/3459637.3482490","DOIUrl":"https://doi.org/10.1145/3459637.3482490","url":null,"abstract":"Paper-publication venue prediction aims to predict candidate publication venues that effectively suit given submissions. This technology is developing rapidly with the popularity of machine learning models. However, most previous methods ignore the structure information of papers, while modeling them with graphs can naturally solve this drawback. Meanwhile, they either use hand-crafted or bag-of-word features to represent the papers, ignoring the ones that involve high-level semantics. Moreover, existing methods assume that the venue where a paper is published as a correct venue for the data annotation, which is unrealistic. One paper can be relevant to many venues. In this paper, we attempt to address these problems above and develop a novel prediction model, namelyVenue Prediction with Abstract-Level Graph (Vpalg xspace), which can serve as an effective decision-making tool for venue selections. Specifically, to achieve more discriminative paper abstract representations, we construct each abstract as a semantic graph and perform a dual attention message passing neural network for representation learning. Then, the proposed model can be trained over the learned abstract representations with their labels and generalized via self-training. Empirically, we employ the PubMed dataset and further collect two new datasets from the top journals and conferences in computer science. Experimental results indicate the superior performance of Vpalg xspace, consistently outperforming the existing baseline methods.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124486599","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}
Nowadays, it is common for a person to possess different identities on multiple social platforms. Social network alignment aims to match the identities that from different networks. Recently, unsupervised network alignment methods have received significant attention since no identity anchor is required. However, to capture the relevance between identities, the existing unsupervised methods generally rely heavily on user profiles, which is unobtainable and unreliable in real-world scenarios. In this paper, we propose an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile. The embedding module of LSNA, named Cross Network Embedding Model (CNEM), aims to integrate the topology information and the network correlation to simultaneously guide the embedding process. Moreover, in order to adapt LSNA to large-scale networks, we propose a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems. The proposed method is evaluated over multiple real-world social network datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.
{"title":"Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding","authors":"Zhehan Liang, Yu Rong, Chenxin Li, Yunlong Zhang, Yue Huang, Tingyang Xu, Xinghao Ding, Junzhou Huang","doi":"10.1145/3459637.3482310","DOIUrl":"https://doi.org/10.1145/3459637.3482310","url":null,"abstract":"Nowadays, it is common for a person to possess different identities on multiple social platforms. Social network alignment aims to match the identities that from different networks. Recently, unsupervised network alignment methods have received significant attention since no identity anchor is required. However, to capture the relevance between identities, the existing unsupervised methods generally rely heavily on user profiles, which is unobtainable and unreliable in real-world scenarios. In this paper, we propose an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile. The embedding module of LSNA, named Cross Network Embedding Model (CNEM), aims to integrate the topology information and the network correlation to simultaneously guide the embedding process. Moreover, in order to adapt LSNA to large-scale networks, we propose a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems. The proposed method is evaluated over multiple real-world social network datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982728","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}
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