کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4944709 | 1438008 | 2017 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Collaborative filtering using multiple binary maximum margin matrix factorizations
ترجمه فارسی عنوان
فیلتر کردن همکاری با استفاده از چندین مرتبه ماتریس حاشیه حداکثر باینری
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کلمات کلیدی
فیلتر کردن همگانی، تکمیل ماتریس، تقسیم ماتریس،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Maximum Margin Matrix Factorization (MMMF) has been a successful learning method in collaborative filtering research. For a partially observed ordinal rating matrix, the focus is on determining low-norm latent factor matrices U (of users) and V (of items) so as to simultaneously approximate the observed entries under some loss measure and predict the unobserved entries. When the rating matrix contains only two levels (±1), rows of V can be viewed as points in k-dimensional space and rows of U as decision hyperplanes in this space separating +1 entries from â1 entries. When hinge/smooth hinge loss is the loss function, the hyperplanes act as maximum-margin separator. In MMMF, rating matrix with multiple discrete values is treated by specially extending hinge loss function to suit multiple levels. We view this process as analogous to extending two-class classifier to a unified multi-class classifier. Alternatively, multi-class classifier can be built by arranging multiple two-class classifiers in a hierarchical manner. In this paper, we investigate this aspect for collaborative filtering and propose an efficient and novel framework of multiple bi-level MMMFs. There is substantial saving in computational overhead. We compare our method with nine well-known algorithms on two benchmark datasets and show that our method outperforms these methods on NMAE measure. We also show that our method yields latent factors of lower ranks and the trade-off between empirical and generalization error is low.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 380, 20 February 2017, Pages 1-11
Journal: Information Sciences - Volume 380, 20 February 2017, Pages 1-11
نویسندگان
Vikas Kumar, Arun K. Pujari, Sandeep Kumar Sahu, Venkateswara Rao Kagita, Vineet Padmanabhan,