کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10998007 | 1365117 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Search result diversification on attributed networks via nonnegative matrix factorization
ترجمه فارسی عنوان
تنوع نتایج جستجو در شبکه های اختصاص داده شده از طریق تقسیم بندی ماتریس غیر انتزاعی
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کلمات کلیدی
جستجوی گراف تنوع فاکتورسازی ماتریس غیر انتزاعی، شبکه اختصاصی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Processing & Management - Volume 54, Issue 6, November 2018, Pages 1277-1291
Journal: Information Processing & Management - Volume 54, Issue 6, November 2018, Pages 1277-1291
نویسندگان
Zaiqiao Meng, Hong Shen, Huimin Huang, Wei Liu, Jing Wang, Arun Kumar Sangaiah,