ICML 2012 推荐系统部分文章小结及下载
ICML2012 paper下载地址，感谢丹柯提供: http://icml.cc/2012/papers/
个人比较感兴趣的，跟推荐系统相关的几篇文章:
1. 在有query的场景下，向用户推荐item
Latent Collaborative Retrieval
Jason Weston, Chong Wang, Ron Weiss, Adam Berenzweig
2. Yan Liu的新作, 通过层次bayesian模型融合topic model和矩阵分解，分析用户隐含喜好，然后做推荐
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems
Sanjay Purushotham, Yan Liu
In this paper, we are interested in examining the effectiveness of social network information to predict the user's ratings of items. We propose a novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks. A major advantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data.
3. 研究推荐系统中矩阵分解模型的稳定性和boundary:
Stability of matrix factorization for collaborative filtering
Yu-Xiang Wang, Huan Xu
4. 一种新的用图方法做推荐的算法
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
Aaron Defazio, Tiberio Caetano
This paper presents a new neighbourhood approach which we call item fields, whereby an undirected graphical model is formed over the item graph.
5. Zoubin的大作，从理论上分析了L_1约束在无监督学习（包括推荐系统）中的不足和优点，并提出了改进方法
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning
Shakir Mohamed, Katherine Heller, Zoubin Ghahramani
While existing work highlights the many advantages of L_1 methods, in this paper we find that L_1 regularisation often dramatically under-performs in terms of predictive performance when compared with other methods for inferring sparsity
