Zhu Sun

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The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries,(More)
In e-commerce systems, user preference can be inferred from multivariate implicit feedback (i.e., actions). However, most methods merely focus on homogeneous implicit feedback (i.e., purchase). In this paper, we adopt another two typical actions, i.e., view and like, as auxiliaries to enhance purchase recommendation, whereby a trinity Bayesian personalized(More)
Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g.(More)
Many features like spin-orbit coupling, bias and magnetic fields applied, and so on, can strongly influence the Kondo effect. One of the consequences is Kondo peak splitting. However, Kondo peak splitting led by a local moment has not been investigated systematically. In this research we study theoretically electronic transport through a single-level(More)
Collaborative filtering inherently suffers from the data spar-sity and cold start problems. Social networks have been shown useful to help alleviate these issues. However, social connections may not be available in many real systems, whereas implicit item relationships are lack of study. In this paper, we propose a novel matrix factorization model by taking(More)
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