| 文献作者 | Stephen E. Robertson; Evangelos Kanoulas; Emine Yilmaz | ||||||||||
| 文献发表年限 | 2010 | 创建时间 | 2017-03-02 | ||||||||
| 文献关键字 | information retrieval, effectiveness metrics, average precision, graded relevance, learning to rank, GAP | ||||||||||
| 摘要描述 | Evaluation metrics play a critical role both in the context of comparative evaluation of the performance of retrieval systems and in the context of learning-to-rank (LTR) as objective functions to be optimized. Many different evaluation metrics have been proposed in the IR literature, with average precision (AP) being the dominant one due a number of desirable properties it possesses. However, most of these measures, including average precision, do not incorporate graded relevance. In this work, we propose a new measure of retrieval effectiveness, the Graded Average Precision (GAP). GAP generalizes average precision to the case of multi-graded relevance and inherits all the desirable characteristics of AP: it has a nice probabilistic interpretation, it approximates the area under a graded precision-recall curve and it can be justified in terms of a simple but moderately plausible user model. We then evaluate GAP in terms of its informativeness and discriminative power. Finally, we show that GAP can reliably be used as an objective metric in learning to rank by illustrating that optimizing for GAP using SoftRank and LambdaRank leads to better performing ranking functions than the ones constructed by algorithms tuned to optimize for AP or NDCG even when using AP or NDCG as the test metrics. | ||||||||||
| 文献作者 | Suvash Sedhain; Aditya Krishna Menon; Scott Sanner; Lexing Xie; Darius Braziunas S | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-03-01 | ||||||||
| 文献关键字 | cold start; content-based; recommender system; low-rank | ||||||||||
| 摘要描述 | The cold-start problem involves recommendation of content to new users of a system, for whom there is no historical preference information available. This proves a challenge for collaborative filtering algorithms that inherently rely on such information. Recent work has shown that social metadata, such as users’ friend groups and page likes, can strongly mitigate the problem. However, such approaches either lack an interpretation (表达) as optimising some principled objective, involve iterative non-convex optimisation with limited scalability, or require tuning several hyperparameters. In this paper, we first show how three popular cold-start models are special cases of a linear content-based model, with implicit constraints on the weights. Leveraging this insight, we propose LoCo, a new model for cold-start recommendation based on three ingredients: (a) linear regression to learn an optimal weighting of social signals for preferences, (b) a low-rank parametrisation of the weights to overcome the high dimensionality common in social data, and (c) scalable learning of such low-rank weights using randomised SVD. Experiments on four real-world datasets show that LoCo yields significant improvements over state-of-the-art cold-start recommenders that exploit high-dimensional social network metadata. | ||||||||||
| 文献作者 | Chen CHEN; Jiakun XIAO; Chunyan HOU; Xiaojie YUAN | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-02-28 | ||||||||
| 文献关键字 | recommender system; behavior analysis; prediction; e- commerce; session; recsys | ||||||||||
| 摘要描述 | Purchase behavior prediction is one of the most important issues to promote both e-commerce companies’ sales and the consumers’ satisfaction. The prediction usually uses features based on the statistics of items. This kind of features can lead to the loss of detailed information of items. While all items are included, a large number of features has the negative impact on the efficiency of learning the predictive model. In this study, we propose to use the most popular items for improving the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. We also analyze the reason for the performance of the most popular items. In addition, our work also reveals if interactions among most popular items are taken into account, the further significant improvement can be achieved. One possible explanation is that online retailers usually use a variety of sales promotion methods and the interactions can help to predict the purchase behavior. | ||||||||||
| 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. | ||||||||||
