Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068699
Ekta Gujral, E. Papalexakis
In data mining, PARAFAC2 is a powerful and a multi-layer tensor decomposition method that is ideally suited for unsupervised modeling of data which forms “irregular” tensors, e.g., patient's diagnostic profiles, where each patient's recovery timeline does not necessarily align with other patients. In real-world applications, where no ground truth is available, how can we automatically choose how many components to analyze? Although extremely trivial, finding the number of components is very hard. So far, under traditional settings, to determine a reasonable number of components, when using PARAFAC2 data, is to compute decomposition with a different number of components and then analyze the outcome manually. This is an inefficient and time-consuming path, first, due to large data volume and second, the human evaluation makes the selection biased. In this paper, we introduce Aptera, a novel automatic PARAFAC2 tensor mining that is based on locating the L-curve corner. The automation of the PARAFAC2 model quality assessment helps both novice and qualified researchers to conduct detailed and advanced analysis. We extensively evaluate Aptera 's performance on synthetic data, outperforming existing state-of-the-art methods on this very hard problem. Finally, we apply Aptera to a variety of real-world datasets and demonstrate its robustness, scalability, and estimation reliability.
{"title":"Aptera: Automatic PARAFAC2 Tensor Analysis","authors":"Ekta Gujral, E. Papalexakis","doi":"10.1109/ASONAM55673.2022.10068699","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068699","url":null,"abstract":"In data mining, PARAFAC2 is a powerful and a multi-layer tensor decomposition method that is ideally suited for unsupervised modeling of data which forms “irregular” tensors, e.g., patient's diagnostic profiles, where each patient's recovery timeline does not necessarily align with other patients. In real-world applications, where no ground truth is available, how can we automatically choose how many components to analyze? Although extremely trivial, finding the number of components is very hard. So far, under traditional settings, to determine a reasonable number of components, when using PARAFAC2 data, is to compute decomposition with a different number of components and then analyze the outcome manually. This is an inefficient and time-consuming path, first, due to large data volume and second, the human evaluation makes the selection biased. In this paper, we introduce Aptera, a novel automatic PARAFAC2 tensor mining that is based on locating the L-curve corner. The automation of the PARAFAC2 model quality assessment helps both novice and qualified researchers to conduct detailed and advanced analysis. We extensively evaluate Aptera 's performance on synthetic data, outperforming existing state-of-the-art methods on this very hard problem. Finally, we apply Aptera to a variety of real-world datasets and demonstrate its robustness, scalability, and estimation reliability.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128213269","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068567
Pedro Ramaciotti, Zografoula Vagena
Ideological scaling is an ubiquitous tool for inferring political opinions of users in social networks, allowing to position a large number of users in left-right or liberal-conservative scales. More recent methods address the need, highlighted by social science research, to infer positions in additional social dimensions. These dimensions allow for the analysis of emerging divisions such as anti-elite sentiment, or attitudes towards globalization, among others. These methods propose to embed social networks in multi-dimensional attitudinal spaces, where dimensions stand as indicators of positive or negative attitudes towards several and separate issues of public debate. So far, these methods have been validated in the context of individual national settings. In this article we propose a method to embed a large number of social media users in multi-dimensional attitudinal spaces that are common to several countries, allowing for large-scale comparative studies. Additionally, we propose novel statistical benchmark validations that show the accuracy of the estimated positions. We illustrate our method on Twitter friendship networks in France, Germany, Italy, and Spain.
{"title":"Embedding social graphs from multiple national settings in common empirical opinion spaces","authors":"Pedro Ramaciotti, Zografoula Vagena","doi":"10.1109/ASONAM55673.2022.10068567","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068567","url":null,"abstract":"Ideological scaling is an ubiquitous tool for inferring political opinions of users in social networks, allowing to position a large number of users in left-right or liberal-conservative scales. More recent methods address the need, highlighted by social science research, to infer positions in additional social dimensions. These dimensions allow for the analysis of emerging divisions such as anti-elite sentiment, or attitudes towards globalization, among others. These methods propose to embed social networks in multi-dimensional attitudinal spaces, where dimensions stand as indicators of positive or negative attitudes towards several and separate issues of public debate. So far, these methods have been validated in the context of individual national settings. In this article we propose a method to embed a large number of social media users in multi-dimensional attitudinal spaces that are common to several countries, allowing for large-scale comparative studies. Additionally, we propose novel statistical benchmark validations that show the accuracy of the estimated positions. We illustrate our method on Twitter friendship networks in France, Germany, Italy, and Spain.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128514520","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068715
Md Rayhan Kabir, Osmar R Zaiane
Associative classifiers have shown competitive performance with state-of-the-art methods for predicting class labels. In addition to accuracy performance, associative classifiers produce human readable rules for classification which provides an easier way to understand the decision process of the model. Early models of associative classifiers suffered from the limitation of selecting proper threshold values which are dataset specific. Recent work on associative classifiers eliminates that restriction by searching for statistically significant rules. However, a high dimensional feature vector in the training data impacts the performance of the model. Ensemble models like Random Forest are also very powerful tools for classification but the decision process of Random Forest is not easily understandable like the associative classifiers. In this study we propose Dynamic Ensemble Associative Learning (DEAL) where we use associative classifiers as base learners on feature sub-spaces. In our approach we select a subset of the feature vector to train each of the base learners. Instead of a random selection, we propose a dynamic feature sampling procedure which automatically defines the number of base learners and ensures diversity and completeness among the subset of feature vectors. We use 10 datasets from the UCI repository and evaluate the performance of the model in terms of accuracy and memory requirement. Our ensemble approach using the proposed sampling method largely decreases the memory requirement in the case of datasets having a large number of features and this without jeopardising accuracy. In fact, accuracy is also improved in most cases. Moreover, the decision process of our DEAL approach remains human interpretable by collecting and ranking the rules generated by the base learners predicting the final class label.
{"title":"Dynamic Ensemble Associative Learning","authors":"Md Rayhan Kabir, Osmar R Zaiane","doi":"10.1109/ASONAM55673.2022.10068715","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068715","url":null,"abstract":"Associative classifiers have shown competitive performance with state-of-the-art methods for predicting class labels. In addition to accuracy performance, associative classifiers produce human readable rules for classification which provides an easier way to understand the decision process of the model. Early models of associative classifiers suffered from the limitation of selecting proper threshold values which are dataset specific. Recent work on associative classifiers eliminates that restriction by searching for statistically significant rules. However, a high dimensional feature vector in the training data impacts the performance of the model. Ensemble models like Random Forest are also very powerful tools for classification but the decision process of Random Forest is not easily understandable like the associative classifiers. In this study we propose Dynamic Ensemble Associative Learning (DEAL) where we use associative classifiers as base learners on feature sub-spaces. In our approach we select a subset of the feature vector to train each of the base learners. Instead of a random selection, we propose a dynamic feature sampling procedure which automatically defines the number of base learners and ensures diversity and completeness among the subset of feature vectors. We use 10 datasets from the UCI repository and evaluate the performance of the model in terms of accuracy and memory requirement. Our ensemble approach using the proposed sampling method largely decreases the memory requirement in the case of datasets having a large number of features and this without jeopardising accuracy. In fact, accuracy is also improved in most cases. Moreover, the decision process of our DEAL approach remains human interpretable by collecting and ranking the rules generated by the base learners predicting the final class label.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128941132","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068628
R. S. Linhares, José Luís da Silva Rosa, C. H. G. Ferreira, Fabricio Murai, G. Nobre, J. Almeida
A large volume of content related to claims of election fraud, often associated with hate speech and extremism, was reported on Twitter during the 2020 US election, with evidence that coordinated efforts took place to promote such content on the platform. In response, Twitter announced the suspension of thousands of user accounts allegedly involved in such actions. Motivated by these events, we here propose a novel network-based approach to uncover evidence of coordination in a set of user interactions. Our approach is designed to address the challenges incurred by the often sheer volume of noisy edges in the network (i.e., edges that are unrelated to coordination) and the effects of data sampling. To that end, it exploits the joint use of two network backbone extraction techniques, namely Disparity Filter and Neighborhood Overlap, to reveal strongly tied groups of users (here referred to as communities) exhibiting repeatedly common behavior, consistent with coordination. We employ our strategy to a large dataset of tweets related to the aforementioned fraud claims, in which users were labeled as suspended, deleted or active, according to their accounts status after the election. Our findings reveal well-structured communities, with strong evidence of coordination to promote (i.e., retweet) the aforementioned fraud claims. Moreover, many of those communities are formed not only by suspended and deleted users, but also by users who, despite exhibiting very similar sharing patterns, remained active in the platform. This observation suggests that a significant number of users who were potentially involved in the coordination efforts went unnoticed by the platform, and possibly remained actively spreading this content on the system.
{"title":"Uncovering Coordinated Communities on Twitter During the 2020 U.S. Election","authors":"R. S. Linhares, José Luís da Silva Rosa, C. H. G. Ferreira, Fabricio Murai, G. Nobre, J. Almeida","doi":"10.1109/ASONAM55673.2022.10068628","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068628","url":null,"abstract":"A large volume of content related to claims of election fraud, often associated with hate speech and extremism, was reported on Twitter during the 2020 US election, with evidence that coordinated efforts took place to promote such content on the platform. In response, Twitter announced the suspension of thousands of user accounts allegedly involved in such actions. Motivated by these events, we here propose a novel network-based approach to uncover evidence of coordination in a set of user interactions. Our approach is designed to address the challenges incurred by the often sheer volume of noisy edges in the network (i.e., edges that are unrelated to coordination) and the effects of data sampling. To that end, it exploits the joint use of two network backbone extraction techniques, namely Disparity Filter and Neighborhood Overlap, to reveal strongly tied groups of users (here referred to as communities) exhibiting repeatedly common behavior, consistent with coordination. We employ our strategy to a large dataset of tweets related to the aforementioned fraud claims, in which users were labeled as suspended, deleted or active, according to their accounts status after the election. Our findings reveal well-structured communities, with strong evidence of coordination to promote (i.e., retweet) the aforementioned fraud claims. Moreover, many of those communities are formed not only by suspended and deleted users, but also by users who, despite exhibiting very similar sharing patterns, remained active in the platform. This observation suggests that a significant number of users who were potentially involved in the coordination efforts went unnoticed by the platform, and possibly remained actively spreading this content on the system.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124569840","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}
Pub Date : 2022-11-10DOI: 10.1109/asonam55673.2022.10068632
{"title":"ASONAM 2022 Program Committee","authors":"","doi":"10.1109/asonam55673.2022.10068632","DOIUrl":"https://doi.org/10.1109/asonam55673.2022.10068632","url":null,"abstract":"","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127856421","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068665
Tuğrulcan Elmas, Thomas Romain Ibanez, Alexandre Hutter, R. Overdorf, K. Aberer
Contrary to celebrities who owe their popularity online to their activity offline, malicious users such as trolls have to gain fame on social media through the social media itself. The exact reasons that a certain user has become popular are often obscure especially when the popularity was gained illicitly through means such as fake amplification of content. In this paper, we develop a methodology for uncovering why an account has become popular and present an open source tool that encapsulates this methodology. This tool aims to aid others in uncovering malicious accounts which have artificially gained many followers and to distinguish such accounts from those which gained followers and popularity honestly.
{"title":"WayPop Machine: A Wayback Machine to Investigate Popularity and Root Out Trolls","authors":"Tuğrulcan Elmas, Thomas Romain Ibanez, Alexandre Hutter, R. Overdorf, K. Aberer","doi":"10.1109/ASONAM55673.2022.10068665","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068665","url":null,"abstract":"Contrary to celebrities who owe their popularity online to their activity offline, malicious users such as trolls have to gain fame on social media through the social media itself. The exact reasons that a certain user has become popular are often obscure especially when the popularity was gained illicitly through means such as fake amplification of content. In this paper, we develop a methodology for uncovering why an account has become popular and present an open source tool that encapsulates this methodology. This tool aims to aid others in uncovering malicious accounts which have artificially gained many followers and to distinguish such accounts from those which gained followers and popularity honestly.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921008","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068589
Guillaume Lachaud, Patricia Conde Céspedes, M. Trocan
Graph data is present everywhere and has vast ranging applications from finding the common interests of people to the optimization of road traffic. Due to the interconnectedness of nodes in graphs, training neural networks on graphs can be done in two settings: in transductive learning, the model can have access to the test features in the training phase; in the inductive setting, the test data remains unseen. We explore the differences between inductive and transductive learning on real citation networks when the graphs are converted to undirected graphs. We find that the models achieve better accuracy in the transductive setting than in the inductive setting, but that the gap between validation and test accuracy is also higher, which indicates the models trained in an inductive setting have better generalization capabilities.
{"title":"Comparison between Inductive and Transductive Learning in a Real Citation Network using Graph Neural Networks","authors":"Guillaume Lachaud, Patricia Conde Céspedes, M. Trocan","doi":"10.1109/ASONAM55673.2022.10068589","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068589","url":null,"abstract":"Graph data is present everywhere and has vast ranging applications from finding the common interests of people to the optimization of road traffic. Due to the interconnectedness of nodes in graphs, training neural networks on graphs can be done in two settings: in transductive learning, the model can have access to the test features in the training phase; in the inductive setting, the test data remains unseen. We explore the differences between inductive and transductive learning on real citation networks when the graphs are converted to undirected graphs. We find that the models achieve better accuracy in the transductive setting than in the inductive setting, but that the gap between validation and test accuracy is also higher, which indicates the models trained in an inductive setting have better generalization capabilities.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129962787","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068593
T. K. Biswas, A. Abbasi, R. Chakrabortty
Identification of a set of influential spreaders in a network, called the Influence Maximization (IM) problem, has gained much popularity due to its immense practicality. In real-life applications, not only the influence spread size, but also some other criteria such as the selection cost and the size of the seed set play an important role in selecting the optimal solution. However, majority of the existing works have treated this issue as a single-objective optimization problem, where decision-makers are forced to make their choices regarding other variables in advance despite having a thorough understanding of them. This research formulates a multi-objective version of the IM problem (referred to as MOIMP), which considers three competing objectives while subject to certain practical restrictions. Theoretical analysis reveals that the influence spreading function under the suggested MOIMP framework is no longer monotone, but submodular. We also considered three well-established multi-objective evolutionary algorithms to solve the proposed MOIMP. Since the proposed MOIMP addresses varying-size seeds, all the considered algorithms are significantly modified to fit into it. Experimental results on four real-life datasets, evaluating and comparing the performance of the considered algorithms, demonstrate the effectiveness of the proposed MOIMP.
{"title":"Multi-Objective Influence Maximization Under Varying-Size Solutions and Constraints","authors":"T. K. Biswas, A. Abbasi, R. Chakrabortty","doi":"10.1109/ASONAM55673.2022.10068593","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068593","url":null,"abstract":"Identification of a set of influential spreaders in a network, called the Influence Maximization (IM) problem, has gained much popularity due to its immense practicality. In real-life applications, not only the influence spread size, but also some other criteria such as the selection cost and the size of the seed set play an important role in selecting the optimal solution. However, majority of the existing works have treated this issue as a single-objective optimization problem, where decision-makers are forced to make their choices regarding other variables in advance despite having a thorough understanding of them. This research formulates a multi-objective version of the IM problem (referred to as MOIMP), which considers three competing objectives while subject to certain practical restrictions. Theoretical analysis reveals that the influence spreading function under the suggested MOIMP framework is no longer monotone, but submodular. We also considered three well-established multi-objective evolutionary algorithms to solve the proposed MOIMP. Since the proposed MOIMP addresses varying-size seeds, all the considered algorithms are significantly modified to fit into it. Experimental results on four real-life datasets, evaluating and comparing the performance of the considered algorithms, demonstrate the effectiveness of the proposed MOIMP.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130114668","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068703
Kun Wu, Jacob Erickson, Wendy Hui Wang, Yue Ning
Graph neural networks (GNNs) have been widely used for recommender systems over knowledge graphs. An important issue of GNN-based recommender systems is individual user fairness in recommendations (i.e., similar users should be treated similarly by the systems). In this paper, we make the following contributions to enable recommender systems to be equipped with individual user fairness. First, we define new similarity metrics for individual fairness, where these metrics take knowledge graphs into consideration by incorporating both first-order proximity in direct user-item interactions and second-order proximity in knowledge graphs. Second, we design a novel graph neural network (GNN) named SKIPHop for fair recommendations over knowledge graphs. By passing latent representations from both first-order and second-order neighbors at every message passing step, SKIPHop learns user embed dings that capture their latent interests present in the second-order networks. Furthermore, to realize individual user fairness, we add fairness as a regularization to the loss function of recommendation models. Finally, through experiments on two real-world datasets, we demonstrate the effectiveness of SKIPHop in terms of fairness and recommendation accuracy.
{"title":"Equipping Recommender Systems with Individual Fairness via Second-order Proximity Embedding","authors":"Kun Wu, Jacob Erickson, Wendy Hui Wang, Yue Ning","doi":"10.1109/ASONAM55673.2022.10068703","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068703","url":null,"abstract":"Graph neural networks (GNNs) have been widely used for recommender systems over knowledge graphs. An important issue of GNN-based recommender systems is individual user fairness in recommendations (i.e., similar users should be treated similarly by the systems). In this paper, we make the following contributions to enable recommender systems to be equipped with individual user fairness. First, we define new similarity metrics for individual fairness, where these metrics take knowledge graphs into consideration by incorporating both first-order proximity in direct user-item interactions and second-order proximity in knowledge graphs. Second, we design a novel graph neural network (GNN) named SKIPHop for fair recommendations over knowledge graphs. By passing latent representations from both first-order and second-order neighbors at every message passing step, SKIPHop learns user embed dings that capture their latent interests present in the second-order networks. Furthermore, to realize individual user fairness, we add fairness as a regularization to the loss function of recommendation models. Finally, through experiments on two real-world datasets, we demonstrate the effectiveness of SKIPHop in terms of fairness and recommendation accuracy.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134267444","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}
Pub Date : 2022-11-10DOI: 10.1109/ASONAM55673.2022.10068654
Yusuf Mücahit Çetinkaya, I. H. Toroslu, H. Davulcu
Social media has become an important place for online marketing like never before. Businesses use various techniques to identify and reach potential customers across multiple platforms and deliver a message to grab their attention. A notable post could attract potential customers to the product landing page. However, the acquisition is only the beginning. The landing page should respond to the visitor's need for persuasion to increase conversion rates. Showing every visitor the same page is far from that goal. Even if the product meets everyone's needs, their priorities may differ. In this study, we propose a pipeline that includes gathering and identifying potential customers from Twitter, determining their priorities by understanding the context of their message, and creating a coherent paragraph that addresses the issue to display on the landing page.
{"title":"Coherent Personalized Paragraph Generation for a Successful Landing Page","authors":"Yusuf Mücahit Çetinkaya, I. H. Toroslu, H. Davulcu","doi":"10.1109/ASONAM55673.2022.10068654","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068654","url":null,"abstract":"Social media has become an important place for online marketing like never before. Businesses use various techniques to identify and reach potential customers across multiple platforms and deliver a message to grab their attention. A notable post could attract potential customers to the product landing page. However, the acquisition is only the beginning. The landing page should respond to the visitor's need for persuasion to increase conversion rates. Showing every visitor the same page is far from that goal. Even if the product meets everyone's needs, their priorities may differ. In this study, we propose a pipeline that includes gathering and identifying potential customers from Twitter, determining their priorities by understanding the context of their message, and creating a coherent paragraph that addresses the issue to display on the landing page.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134412985","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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