In this paper, we examine the model section decision for a medical diagnostic decision support system (MDSS). Our purpose in doing this is to understand how model selection affects the accuracy of the decision support system. We explore two related research questions: (1) Do ensembles of models, act... || Model selection; Medical diagnosis; Neural networks; Bootstrap aggregating models; Diverse ensembles; Baseline ensembles; Bagging models; || Paul Mangiameli...
This paper contributes improvements on both the effective- ness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of ex- isting works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight... || Matrix Factorization, Implicit Feedback, Item Recommen- dation, Online Learning, ALS, Coordinate Descent ; || Xiangnan He; Hanwang Zhang...
This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse LInear Method ( SLIM ) is proposed, which generates top- N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SL... || Top-N Recommender Systems, Sparse Linear Meth- ods, l1 -norm Regularization; || Xia Ning and George Karypis...
The increasing amount of side information associated with the items in E-commerce applications has provided a very rich source of information that, once properly exploited and incorporated, can significantly improve the performance of the conventional recommender systems. This paper focuses
on dev... || 实验详细; RecSys; 指标; metric; regularization norm; MF; linear model; implicit feedback; ; || George Karypis; Xia Ning...
Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way – instead there exist subsets of li... || RecSys 2016 best paper; SLIM; global-local;GLSLIM; || Evangelia Christakopoulou and George Karypis...
We investigate using gradient descent meth- ods for learning ranking functions; we pro- pose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on da... || RankNet; NN; 神经网络; neural network; L2R; 1500 citation; 利用协同关系; 利用属性值; || Christopher J.C. Burges: Microsoft Research...
Many aspects and properties of Recommender Systems have been well studied in the past decade, however, the impact of User Fatigue has been mostly ignored in the literature. User fatigue represents the phenomenon that a user quickly loses the interest on the recommended item if the same item has been... || Recommender Systems, User Fatigue, News Recommendation, Click Prediction, User Modeling; || Hao Ma; Xueqing Liu; Zhihong Shen...
In the real world, concept drift happens in various scenarios including medical treatment recommendation, where the relation between features and the target class changes over time in unforeseen ways. Nearest neighbors(k-NN) is a simple non-parametric classification model, yet it is effective
in va... || knn classifier; concept drift; mdt; || Nengjun zhu; zhang yan; cao jian...
Most Collaborative Filtering (CF) algorithms are optimized using a dataset of isolated user-item tuples. However, in commercial applications recommended items are usually served as an ordered list of several items and not as isolated items. In this setting, inter-item interactions have an effect on ... || Collaborative Filtering, Click prediction; www; GBT; || Oren Sar Shalom; Noam Koenigstein...
