| 文献作者 | Simon Rogers; Mark Girolami | ||||||||||
| 文献发表年限 | 2012 | 创建时间 | 2017-02-22 | ||||||||
| 文献关键字 | Linear Model; least square; Leave one out cross validation | ||||||||||
| 摘要描述 | 机器学习入门书籍第一章，从最简单的线性模型讲起。 | ||||||||||
| 文献作者 | Joonseok Lee; Mingxuan Sun; Guy Lebanon | ||||||||||
| 文献发表年限 | 2012 | 创建时间 | 2017-02-20 | ||||||||
| 文献关键字 | tootkit; datasets; explicit feedback; exrecommender systems; collaborative filtering; evaluation metric;库;开源软件 | ||||||||||
| 摘要描述 | Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms. | ||||||||||
| 文献作者 | Joonseok Lee; Seungyeon Kim; Guy Lebanon | ||||||||||
| 文献发表年限 | 2013 | 创建时间 | 2017-02-16 | ||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks. | ||||||||||
| 文献作者 | Joonseok Lee; Samy Bengio; Seungyeon Kim | ||||||||||
| 文献发表年限 | 2014 | 创建时间 | 2017-02-15 | ||||||||
| 文献关键字 | recommender systems; collaborative filtering; ranking | ||||||||||
| 摘要描述 | Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low- rank. In this paper, we examine an alternative approachin which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems. | ||||||||||
| 文献作者 | Shanshan Huang; Shuaiqiang Wang; Tie-Yan Liu | ||||||||||
| 文献发表年限 | 2015 | 创建时间 | 2017-02-15 | ||||||||
| 文献关键字 | Recommender systems; Collaborative filtering; Ranking- oriented collaborative filtering; 肯德尔; Kendall; Cross Entroy; 交叉熵 | ||||||||||
| 摘要描述 | Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users’ probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting the pairwise preferences between items. One important advantage of ListCF lies in its ability of reducing the computational complexity of the training and prediction procedures while achieving the same or better ranking performances as compared to previous ranking-oriented memory-based CF algorithms. Extensive experiments on three benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal. | ||||||||||
| 文献作者 | 赵晨婷; 马春娥 | ||||||||||
| 文献发表年限 | 2012 | 创建时间 | 2017-02-15 | ||||||||
| 文献关键字 | 推荐; 机器学习; 协同过滤 | ||||||||||
| 摘要描述 | 在现今的推荐技术和算法中，最被大家广泛认可和采用的就是基于协同过滤的推荐方法。本文将带你深入了解协同过滤的秘密。 | ||||||||||
