Massimo Guarascio, Marco Minici, Francesco Sergio Pisani, Erika De Francesco, Pasquale Lambardi
{"title":"电影标签预测:基于转换器的极端多标签多模态解决方案及说明","authors":"Massimo Guarascio, Marco Minici, Francesco Sergio Pisani, Erika De Francesco, Pasquale Lambardi","doi":"10.1007/s10844-023-00836-7","DOIUrl":null,"url":null,"abstract":"<p>Providing rich and accurate metadata for indexing media content is a crucial problem for all the companies offering streaming entertainment services. These metadata are commonly employed to enhance search engine results and feed recommendation algorithms to improve the matching with user interests. However, the problem of labeling multimedia content with informative tags is challenging as the labeling procedure, manually performed by domain experts, is time-consuming and prone to error. Recently, the adoption of AI-based methods has been demonstrated to be an effective approach for automating this complex process. However, developing an effective solution requires coping with different challenging issues, such as data noise and the scarcity of labeled examples during the training phase. In this work, we address these challenges by introducing a Transformer-based framework for multi-modal multi-label classification enriched with model prediction explanation capabilities. These explanations can help the domain expert to understand the system’s predictions. Experimentation conducted on two real test cases demonstrates its effectiveness.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"4 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Movie tag prediction: An extreme multi-label multi-modal transformer-based solution with explanation\",\"authors\":\"Massimo Guarascio, Marco Minici, Francesco Sergio Pisani, Erika De Francesco, Pasquale Lambardi\",\"doi\":\"10.1007/s10844-023-00836-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Providing rich and accurate metadata for indexing media content is a crucial problem for all the companies offering streaming entertainment services. These metadata are commonly employed to enhance search engine results and feed recommendation algorithms to improve the matching with user interests. However, the problem of labeling multimedia content with informative tags is challenging as the labeling procedure, manually performed by domain experts, is time-consuming and prone to error. Recently, the adoption of AI-based methods has been demonstrated to be an effective approach for automating this complex process. However, developing an effective solution requires coping with different challenging issues, such as data noise and the scarcity of labeled examples during the training phase. In this work, we address these challenges by introducing a Transformer-based framework for multi-modal multi-label classification enriched with model prediction explanation capabilities. These explanations can help the domain expert to understand the system’s predictions. Experimentation conducted on two real test cases demonstrates its effectiveness.</p>\",\"PeriodicalId\":56119,\"journal\":{\"name\":\"Journal of Intelligent Information Systems\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10844-023-00836-7\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-023-00836-7","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Movie tag prediction: An extreme multi-label multi-modal transformer-based solution with explanation
Providing rich and accurate metadata for indexing media content is a crucial problem for all the companies offering streaming entertainment services. These metadata are commonly employed to enhance search engine results and feed recommendation algorithms to improve the matching with user interests. However, the problem of labeling multimedia content with informative tags is challenging as the labeling procedure, manually performed by domain experts, is time-consuming and prone to error. Recently, the adoption of AI-based methods has been demonstrated to be an effective approach for automating this complex process. However, developing an effective solution requires coping with different challenging issues, such as data noise and the scarcity of labeled examples during the training phase. In this work, we address these challenges by introducing a Transformer-based framework for multi-modal multi-label classification enriched with model prediction explanation capabilities. These explanations can help the domain expert to understand the system’s predictions. Experimentation conducted on two real test cases demonstrates its effectiveness.
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.