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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/142228


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/142228


    題名: Item Concept Network: Towards Concept-based Item Representation Learning
    作者: 蔡銘峰
    Tsai, Ming-Feng
    Wang, Ting-Hsiang;Yang, Hsiu-Wei;Chen, Chih-Ming;Wang, Chuan-Ju
    貢獻者: 資科系
    關鍵詞: Information networks;distributed representations;concept learning;network embedding;concept retrieval
    日期: 2022-03
    上傳時間: 2022-10-07
    摘要: Item concept modeling is commonly achieved by leveraging textual information. However, many existing models do not leverage the inferential property of concepts to capture word meanings, which therefore ignores the relatedness between correlated concepts, a phenomenon which we term conceptual “correlation sparsity.” In this paper, we distinguish between word modeling and concept modeling and propose an item concept modeling framework centering around the item concept network (ICN). ICN models and further enriches item concepts by leveraging the inferential property of concepts and thus addresses the correlation sparsity issue. Specifically, there are two stages in the proposed framework: ICN construction and embedding learning. In the first stage, we propose a generalized network construction method to build ICN, a structured network which infers expanded concepts for items via matrix operations. The second stage leverages neighborhood proximity to learn item and concept embeddings. With the proposed ICN, the resulting embedding facilitates both homogeneous and heterogeneous tasks, such as item-to-item and concept-to-item retrieval, and delivers related results which are more diverse than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, the framework encodes useful conceptual information and thus outperforms traditional methods in various item classification and retrieval tasks.
    關聯: IEEE Transactions on Knowledge and Data Engineering, 34(3), 1258-1274
    資料類型: article
    DOI 連結: https://doi.org/10.1109/TKDE.2020.2995859
    DOI: 10.1109/TKDE.2020.2995859
    顯示於類別:[資訊科學系] 期刊論文

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