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- W4367310625 abstract "The research on multimedia retrieval has lasted for several decades. However, past efforts generally focused on single-media retrieval, where the queries and retrieval results belong to the same media (platform) type, such as social media platforms or search engines. In single-media retrieval, users have to select search media or options based on search characteristics such as contents, time, or spatial distance, they might be unable to retrieve correct results mixed in other media if they carelessly forget to select. In this study, we propose a cross-media retrieval system using suggestion generation methods to integrate three search characteristics of the Web (textual content-based retrieval), SNS (timeliness), and map (spatial distance-aware retrieval). In our previous research, we attempted to improve search efficiency using clustering methods to provide search results to users through related terms, etc. In this paper, we focus on the search efficiency of multiple search media. We utilize Google search engine to obtain the retrieval content from the Web, Twitter to obtain timely information from SNSs, and Google Maps to get geographical information from maps. We apply the obtained retrieval results to analyze the similarities between them by clustering. Then, we generate relevant suggestions and provide them to users. Moreover, we validate the effectiveness of the search results generated by our proposed system." @default.
- W4367310625 created "2023-04-29" @default.
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- W4367310625 date "2023-04-30" @default.
- W4367310625 modified "2023-10-17" @default.
- W4367310625 title "A Cross-Media Retrieval System for Web-SNS-Map Using Suggested Keywords Generating and Ranking Method Based on Search Characteristics" @default.
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- W4367310625 doi "https://doi.org/10.1145/3543873.3587344" @default.
- W4367310625 hasPublicationYear "2023" @default.
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