诺库维普

AutoRec: Autoencoders Meet Collaborative Filtering 2017-06-17 21:35:05

This paper proposes AutoRec, a novel autoencoder frame- work for collaborative filtering (CF). Empirically, AutoRec’s compact and efficiently trainable model outperforms state- of-the-art CF techniques (biased matrix factorization, RBM- CF and LLORMA) on the Movielens and Netflix datasets.... || Recommender Systems; Collaborative Filtering; Autoencoders; 编码,解码; 生成模型; 判别模型; || Suvash Sedhain †∗ , Aditya Krishna Menon †∗ , Scott Sanner †∗ , Lexing Xie ∗...

Towards Improving Top-N Recommendation by Generalization of SLIM 2017-06-15 22:10:01

Sparse Linear Methods (SLIM) are state-of-the-art recommendation approaches based on matrix factorization, which rely on a regularized l 1 -norm and l 2 -norm optimization –an alternative optimization problem to the traditional Frobenious norm. Although they have shown outstanding performance in Top... || SLIM; GSLIM; latent factor vectors; encoding; prototype matrix; Orthogonal Matching Pursuit (OMP) algorithm; 原子; 信号分解; || Santiago Larraín, Denis Parra, Alvaro Soto...

Top-N Recommendation with Novel Rank Approximation 2017-06-15 20:01:42

The importance of accurate recommender systems has been widely recognized by academia and industry. How- ever, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approac... || 非凸秩估计; slim; || Zhao Kang Qiang Cheng...

Top-N Recommender System via Matrix Completion 2017-06-15 19:14:16

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommenda- tion quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item ma- trix based on a low-rank assumption and simultaneously keep the... || SLIM; nuclear; LorSLIM; augmented Lagrangian multiplier (ALM) method; logdet; 秩约束; nonconvex relaxation; convex relaxation; 非凸松弛;; || Zhao Kang Chong Peng Qiang Cheng...

LibRec 2017-06-08 14:51:27

一个很不错的推荐系统库 http://blog.csdn.net/cserchen/article/details/14231153... || 推荐系统开源软件列表汇总和点评 ...

Compression-Based Selective Sampling for Learning to Rank 2017-05-12 22:20:19

Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can be later used to rank new query results. These training sets are very costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query... || L2R; IR; AL; Active Learning (主动学习); 半监督学习; 直推学习(transductive learning); || Rodrigo M. Silva, Guilherme C. M. Gomes, Mário S. Alvim, Marcos A. Gonçalves...

LETOR: Benchmark Datasets for Learning to Rank 2017-05-10 22:03:39

主要介绍下L2R在IR领域中的应用,尤其区别于L2R在RecSys中的应用. Information Retriveal with Learning to Rank (problem setting)... || LETOR; L2R: IR; || Tie-Yan Liu and Hang Li...

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