| 文献作者 | Christopher J.C. Burges: Microsoft Research | ||||||||||
| 文献发表年限 | 2006 | 创建时间 | 2017-04-09 | ||||||||
| 文献关键字 | RankNet; NN; 神经网络; neural network; L2R; 1500 citation; 利用协同关系; 利用属性值 | ||||||||||
| 摘要描述 | 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 data from a commercial internet search engine. | ||||||||||
| 文献作者 | Hao Ma; Xueqing Liu; Zhihong Shen | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2017-04-08 | ||||||||
| 文献关键字 | Recommender Systems, User Fatigue, News Recommendation, Click Prediction, User Modeling | ||||||||||
| 摘要描述 | 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 presented to this user multiple times before. The direct impact caused by the user fatigue is the dramatic decrease of the Click Through Rate (CTR, i.e., the ratio of clicks to impressions). In this paper, we present a comprehensive study on the research of the user fatigue in online recommender systems. By analyzing user behavioral logs from Bing Now news recommendation, we find that user fatigue is a severe problem that greatly affects the user experience. We also notice that different users engage differently with repeated recommendations. Depending on the previous users’ interaction with repeated recommendations, we illustrate that under certain condition the previously seen items should be demoted, while some other times they should be promoted. We demonstrate how statistics about the analysis of the user fatigue can be incorporated into ranking algorithms for personalized recommendations. Our experimental results indicate that significant gains can be achieved by introducing features that reflect users’ interaction with previously seen recommendations (up to 15% enhancement on all users and 34% improvement on heavy users). | ||||||||||
| 文献作者 | Nengjun zhu; zhang yan; cao jian | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2017-04-05 | ||||||||
| 文献关键字 | knn classifier; concept drift; mdt | ||||||||||
| 摘要描述 | 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 various situations, even for training data with an unknown distribution. While categorical attributes limit the application of many learning algorithms, k-NN classifier still behaves well in such scenarios given a reasonable similarity measure between categorical attributes. To alleviate deterioration in prediction accuracy introduced by concept drift, sliding windows and temporal weights are often used, simply to eliminate/dilute the effect of outdated samples on the prediction, leading to a less confident (based on fewer samples) prediction and a waste of undiscovered information contained in past samples. With the knowledge of how concepts change, outdated samples can be adapted for up-to-date prediction, which improves the confidence of prediction, especially for medical data sets of which the scale is relatively small. In this paper we present an adaptive k-NN classifier which can detect the occurrence of target concept drift and update past samples according to the knowledge of the drift for better prediction, and evaluate its performance over both simulated and real categorical medical data sets. The experiment results show our classifier achieves better performance under concept drift. | ||||||||||
| 文献作者 | Oren Sar Shalom; Noam Koenigstein | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2017-04-04 | ||||||||
| 文献关键字 | Collaborative Filtering, Click prediction; www; GBT | ||||||||||
| 摘要描述 | Most Collaborative Filtering (CF) algorithms are optimized using a dataset of isolated user-item tuples. However, in commercial applications recommended items are usually served as an ordered list of several items and not as isolated items. In this setting, inter-item interactions have an effect on the list’s Click-Through Rate (CTR) that is unaccounted for using traditional CF approaches. Most CF approaches also ignore additional important factors like click propensity variation, item fatigue, etc. In this work, we introduce the list recommendation problem. We present useful insights gleaned from user behavior and consumption patterns from a large scale real world recommender system. We then propose a novel two-layered framework that builds upon existing CF algorithms to optimize a list’s click probability. Our approach accounts for inter-item interactions as well as additional information such as item fatigue, trendiness patterns, contextual information etc. Finally, we evaluate our approach using a novel adaptation of Inverse Propensity Scoring (IPS) which facilitates off-policy estimation of our method’s CTR and showcases its effectiveness in real-world settings. | ||||||||||
| 文献作者 | Authors of DASFAA2017 | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-03-28 | ||||||||
| 文献关键字 | dasfaa; 2017 | ||||||||||
| 摘要描述 | 选取几篇听过的文章，简述该文章主要思想和要解决的问题 | ||||||||||
| 文献作者 | Tamas Motajcsek; Jean-Yves Le Moine; Martha Larson | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2017-03-25 | ||||||||
| 文献关键字 | Personalization; recommendation engine; machine learning | ||||||||||
| 摘要描述 | In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys. | ||||||||||
| 文献作者 | ruby on rails | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-03-17 | ||||||||
| 文献关键字 | rails; bundler; gem; ruby; | ||||||||||
| 摘要描述 | Rails#环境配置(从rvm到rails) | ||||||||||
| 文献作者 | cnblogs | ||||||||||
| 文献发表年限 | 2012 | 创建时间 | 2017-03-10 | ||||||||
| 文献关键字 | nginx; https; http; ssl | ||||||||||
| 摘要描述 | 配置ssl和nginx, 使得服务器进行https传输, 保证传输安全 | ||||||||||
| 文献作者 | しょ のぅくぅ | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-03-06 | ||||||||
| 文献关键字 | 2016; 推荐系统; recommender system; conferences | ||||||||||
| 摘要描述 | 将2016年推荐领域相关的论文做一个系统的整理, 这些论文主要来源于几个核心会议: WWW, AAAI, SIGIR, Recsys, KDD, VLDB, IJCAI, CIKM, ICDM, ICDE | ||||||||||
| 文献作者 | Yue Shi; Alexandros Karatzoglou; Linas Baltrunas | ||||||||||
| 文献发表年限 | 2013 | 创建时间 | 2017-03-02 | ||||||||
| 文献关键字 | Collaborative filtering, graded average precision, latent factor model, recommender systems, top-n recommendation, ranking | ||||||||||
| 摘要描述 | Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. Current ranking techniques are based on one of two sub-optimal approaches: either they optimize for a binary metric such as Average Precision, which discards information on relevance levels, or they optimize for Normalized Discounted Cumulative Gain (NDCG), which ignores the dependence of an item’s contribution on the relevance of more highly ranked items. We address the short-comings of existing approaches by proposing GAPfm, the Graded Average Precision factor model, which is a latent factor model for top-N recommendation in domains with graded relevance data. The model optimizes the Graded Average Precision metric that has been proposed recently for assessing the quality of ranked results lists for graded relevance. GAPfm’s advantages are twofold: it maintains full information about graded relevance and also addresses the limitations of models that optimize NDCG. Experimental results show that GAPfm achieves substantial improvements on the top-N recommendation task, compared to several state-of-the-art approaches. | ||||||||||
