Pub Date : 2024-02-15DOI: 10.1108/ijwis-12-2023-0242
Xinyu Liu, Kun Ma, Ke Ji, Zhenxiang Chen, Bo Yang
Purpose Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection. Design/methodology/approach G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them. Findings G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively. Originality/value An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.
{"title":"Graph-based multi-information integration network with external news environment perception for Propaganda detection","authors":"Xinyu Liu, Kun Ma, Ke Ji, Zhenxiang Chen, Bo Yang","doi":"10.1108/ijwis-12-2023-0242","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0242","url":null,"abstract":"\u0000Purpose\u0000Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.\u0000\u0000\u0000Design/methodology/approach\u0000G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.\u0000\u0000\u0000Findings\u0000G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.\u0000\u0000\u0000Originality/value\u0000An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775683","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 : 2024-02-15DOI: 10.1108/ijwis-12-2023-0249
Songlin Bao, Tiantian Li, Bin Cao
Purpose In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task. Design/methodology/approach To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning. Findings Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets. Originality/value This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.
{"title":"Chain-of-event prompting for multi-document summarization by large language models","authors":"Songlin Bao, Tiantian Li, Bin Cao","doi":"10.1108/ijwis-12-2023-0249","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0249","url":null,"abstract":"\u0000Purpose\u0000In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.\u0000\u0000\u0000Design/methodology/approach\u0000To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.\u0000\u0000\u0000Findings\u0000Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.\u0000\u0000\u0000Originality/value\u0000This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835058","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 : 2024-02-15DOI: 10.1108/ijwis-12-2023-0249
Songlin Bao, Tiantian Li, Bin Cao
Purpose In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task. Design/methodology/approach To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning. Findings Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets. Originality/value This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.
{"title":"Chain-of-event prompting for multi-document summarization by large language models","authors":"Songlin Bao, Tiantian Li, Bin Cao","doi":"10.1108/ijwis-12-2023-0249","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0249","url":null,"abstract":"\u0000Purpose\u0000In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.\u0000\u0000\u0000Design/methodology/approach\u0000To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.\u0000\u0000\u0000Findings\u0000Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.\u0000\u0000\u0000Originality/value\u0000This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775617","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 : 2024-02-15DOI: 10.1108/ijwis-12-2023-0242
Xinyu Liu, Kun Ma, Ke Ji, Zhenxiang Chen, Bo Yang
Purpose Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection. Design/methodology/approach G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them. Findings G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively. Originality/value An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.
{"title":"Graph-based multi-information integration network with external news environment perception for Propaganda detection","authors":"Xinyu Liu, Kun Ma, Ke Ji, Zhenxiang Chen, Bo Yang","doi":"10.1108/ijwis-12-2023-0242","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0242","url":null,"abstract":"\u0000Purpose\u0000Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.\u0000\u0000\u0000Design/methodology/approach\u0000G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.\u0000\u0000\u0000Findings\u0000G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.\u0000\u0000\u0000Originality/value\u0000An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835359","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 : 2024-02-06DOI: 10.1108/ijwis-12-2023-0243
Lin Xue, Feng Zhang
Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.
目的随着网络服务数量的不断增加，正确有效的网络服务分类对于提高服务发现的效率至关重要。然而，现有的 Web 服务分类方法忽略了 Web 服务中的类重叠，导致实际分类的准确性不高。本文提出了一种基于标签混淆和先验校正的 Web 服务分类方法。首先，基于 BERT 获取 Web 服务描述的功能语义表征。然后，利用标签混淆学习技术增强模型识别和分类重叠实例的能力；最后，根据标签先验分布对预测结果进行校正，以进一步提高服务分类的有效性。基于 ProgrammableWeb 数据集的实验表明，与 ServeNet-BERT、BERT-DPCNN 和 CARL-NET 相比，所提出的模型在 Macro-F1 值上分别提高了 4.3%、3.2% 和 1%。
{"title":"LCPCWSC: a Web service classification approach based on label confusion and priori correction","authors":"Lin Xue, Feng Zhang","doi":"10.1108/ijwis-12-2023-0243","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0243","url":null,"abstract":"\u0000Purpose\u0000With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.\u0000\u0000\u0000Design/methodology/approach\u0000This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.\u0000\u0000\u0000Findings\u0000Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.\u0000\u0000\u0000Originality/value\u0000This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800730","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 : 2024-02-06DOI: 10.1108/ijwis-12-2023-0243
Lin Xue, Feng Zhang
Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.
目的随着网络服务数量的不断增加，正确有效的网络服务分类对于提高服务发现的效率至关重要。然而，现有的 Web 服务分类方法忽略了 Web 服务中的类重叠，导致实际分类的准确性不高。本文提出了一种基于标签混淆和先验校正的 Web 服务分类方法。首先，基于 BERT 获取 Web 服务描述的功能语义表征。然后，利用标签混淆学习技术增强模型识别和分类重叠实例的能力；最后，根据标签先验分布对预测结果进行校正，以进一步提高服务分类的有效性。基于 ProgrammableWeb 数据集的实验表明，与 ServeNet-BERT、BERT-DPCNN 和 CARL-NET 相比，所提出的模型在 Macro-F1 值上分别提高了 4.3%、3.2% 和 1%。
{"title":"LCPCWSC: a Web service classification approach based on label confusion and priori correction","authors":"Lin Xue, Feng Zhang","doi":"10.1108/ijwis-12-2023-0243","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0243","url":null,"abstract":"\u0000Purpose\u0000With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.\u0000\u0000\u0000Design/methodology/approach\u0000This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.\u0000\u0000\u0000Findings\u0000Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.\u0000\u0000\u0000Originality/value\u0000This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139860488","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}
Purpose With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed. Design/methodology/approach In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked. Findings Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR. Originality/value In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.
{"title":"TN-MR: topic-aware neural network-based mobile application recommendation","authors":"Junyi Chen, Buqing Cao, Zhenlian Peng, Ziming Xie, Shanpeng Liu, Qian Peng","doi":"10.1108/ijwis-10-2023-0205","DOIUrl":"https://doi.org/10.1108/ijwis-10-2023-0205","url":null,"abstract":"\u0000Purpose\u0000With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.\u0000\u0000\u0000Design/methodology/approach\u0000In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.\u0000\u0000\u0000Findings\u0000Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.\u0000\u0000\u0000Originality/value\u0000In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683038","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}
Purpose This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian. Design/methodology/approach This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content. Findings Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer. Originality/value The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.
{"title":"Cross-lingual speaker transfer for Cambodian based on feature disentangler and time-frequency attention adaptive normalization","authors":"Yuanzhang Yang, Linqin Wang, Shengxiang Gao, Zhengtao Yu, Ling Dong","doi":"10.1108/ijwis-09-2023-0162","DOIUrl":"https://doi.org/10.1108/ijwis-09-2023-0162","url":null,"abstract":"\u0000Purpose\u0000This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.\u0000\u0000\u0000Design/methodology/approach\u0000This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.\u0000\u0000\u0000Findings\u0000Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.\u0000\u0000\u0000Originality/value\u0000The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595432","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 : 2024-01-26DOI: 10.1108/ijwis-06-2023-0089
Merly Thomas, Meshram B.B.
Purpose Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible. Design/methodology/approach This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks. Findings The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6. Originality/value The introduced detection approach effectively detects DoS attacks available on the internet.
目的拒绝服务（DoS）攻击通过建立流量，同时创建多个请求，使用户无法使用系统，从而对各种网络服务和用户信息进行未经授权的访问。保护互联网服务需要有效的 DoS 攻击检测，以监控通过受保护网络的流量，使受保护的互联网服务器免受监控威胁，并确保它们能够专注于以尽可能短的响应时间提供高质量的服务。研究结果所设计的 Aquila 猎鹿优化深度信念网络技术提高了性能，准确率达到 92.8%，真阳性率达到 92.8%，真阴性率达到 93.6%。
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Pub Date : 2024-01-18DOI: 10.1108/ijwis-12-2023-0247
Yahan Xiong, Xiaodong Fu
Purpose Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly. Design/methodology/approach In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility. Findings Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy. Originality/value The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.
{"title":"User credibility evaluation for reputation measurement of online service","authors":"Yahan Xiong, Xiaodong Fu","doi":"10.1108/ijwis-12-2023-0247","DOIUrl":"https://doi.org/10.1108/ijwis-12-2023-0247","url":null,"abstract":"\u0000Purpose\u0000Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.\u0000\u0000\u0000Design/methodology/approach\u0000In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.\u0000\u0000\u0000Findings\u0000Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.\u0000\u0000\u0000Originality/value\u0000The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.\u0000","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526226","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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