诺库维普

Fast Matrix Factorization for Online Recommendation with Implicit Feedback 2017-04-12 16:57:49

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...

SLIM : Sparse Linear Methods for Top-N Recommender Systems 2017-04-12 16:16:58

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...

Sparse Linear Methods with Side Information for Top-N Recommendations 2017-04-12 15:56:35

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...

Local Item-Item Models for Top-N Recommendation (GLSLIM) 2017-04-12 15:51:28

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...

Learning to Rank using Gradient Descent 2017-04-09 18:28:38

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...

User Fatigue in Online News Recommendation 2017-04-08 21:17:41

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...

An Adaptive k-NN Classifier for Medical Treatment Recommendation under Concept Drift 2017-04-05 17:13:22

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...

WWW 23

Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

SIGIR2022

Madrid, Spain, July 11th to 15th, 2022

WSDM2023

TBD,

IJCAI2022

Austria, July 23-29, 2022