Improving maximum margin matrix factorization
نویسندگان
چکیده
منابع مشابه
Maximum-Margin Matrix Factorization
We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margin linear discrimination. We show how to learn low-norm factorizations by solving a semi-definite program, and discuss generalization error bounds for them.
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In this paper, an algorithm for sparse learning via Maximum Margin Matrix Factorization(MMMF) is proposed. The algorithm is based on L1 penality and Alternating Direction Method of Multipliers. It shows that with sparse factors, sparse factors method can obtain result as good as dense factors.
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Maximum Margin Matrix Factorization (MMMF), a collaborative filtering method, was recently introduced in [7] followed by an iterative solution presented in [6]. In this paper we analyze the performance of MMMF on a subset of the Netflix data based on RMSE and classification rate. We also present several modifications to improve the performance of the algorithm on the Netflix problem.
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User groups on photo sharing websites, such as Flickr, are self-organized communities to share photos and conversations with similar interest and have gained massive popularity. However, the huge volume of groups brings troubles for users to decide which group to choose. Further, directly applying collaborative filtering techniques to group recommendation will suffer from cold start problem sin...
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Collaborative prediction (CP) is a problem of predicting unobserved entries in sparsely observed matrices, e.g. product ratings by different users in online recommender systems. However, the quality of prediction may be quite sensitive to the choice of available samples, which motivates active sampling approaches. In this paper, we suggest an active sampling method based on the recently propose...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2008
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-008-5073-7