| 文献作者 | Depin Chen,Yan Xiong, Jun Yan, Gui-Rong Xue, Gang Wang, Zheng Chen | ||||||||||
| 文献发表年限 | 2009 | 创建时间 | 2017-12-11 | ||||||||
| 文献关键字 | Information retrieval; Learning to rank; Knowledge transfer; Ranking SVM | ||||||||||
| 摘要描述 | Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training and test data are drawn from the same data distri- bution. However, this assumption does not always hold true in real world applications. For example, it can be violated when the labeled training data become outdated or originally come from another domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments, we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level transfer method comes out with varying performance depending on the dataset used. | ||||||||||
| 文献作者 | 黄震华, 张佳雯, 田春岐, 孙圣力, 向 阳 | ||||||||||
| 文献发表年限 | 2015 | 创建时间 | 2017-12-09 | ||||||||
| 文献关键字 | 排序学习;推荐算法;机器学习;兴趣模型;个性化服务; 从统计的角度考虑模型的构造和学习 | ||||||||||
| 摘要描述 | 排序学习技术尝试用机器学习的方法解决排序问题,已被深入研究并广泛应用于不同的领域,如信息检 索、文本挖掘、个性化推荐、生物医学等.将排序学习融入推荐算法中,研究如何整合大量用户和物品的特征,构建 更加贴合用户偏好需求的用户模型,以提高推荐算法的性能和用户满意度,成为基于排序学习推荐算法的主要任务. 对近些年基于排序学习的推荐算法研究进展进行综述,并对其问题定义、关键技术、效用评价、应用进展等进行概 括、比较和分析.最后,对基于排序学习的推荐算法的未来发展趋势进行探讨和展望. | ||||||||||
| 文献作者 | Peizhe Cheng; Shuaiqiang Wang | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-07-28 | ||||||||
| 文献关键字 | WWW; Diversity; Collaborative filtering; Recommender systems; Structural SVM | ||||||||||
| 摘要描述 | In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground- truth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled prob- lems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms. | ||||||||||
| 文献作者 | 毕达几何 | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-07-27 | ||||||||
| 文献关键字 | 激活函数; sigmoid; tanh; ReLu | ||||||||||
| 摘要描述 | sigmoid; tanh; ReLu 相关图像, 性质等 | ||||||||||
| 文献作者 | Xiangnan He; Hanwang Zhang | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-07-27 | ||||||||
| 文献关键字 | Collaborative Filtering, Neural Networks, Deep Learning, Matrix Factorization, Implicit Feedback; NeuMF | ||||||||||
| 摘要描述 | In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation — collaborative filtering — on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering — the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network- based Collaborative Filtering. NCF is generic and can ex- press and generalize matrix factorization under its frame- work. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user–item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance. | ||||||||||
| 文献作者 | 毕达几何 | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-07-21 | ||||||||
| 文献关键字 | 极大似然;极大后验;PMF与MF;LDA;生成模型;概率模型 | ||||||||||
| 摘要描述 | 总结MLE(极大/最大似然估计)和MAP(极大后验估计)相关概念和套路,并将此套路类比其他类似的概率模型. 从而试图找到这其中的规律,帮助以后更好的分析相类似的模型 | ||||||||||
| 文献作者 | Yao Wu Christopher DuBois Alice X. Zheng Martin Ester | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2017-06-22 | ||||||||
| 文献关键字 | Recommender Systems; Collaborative Filtering; Denoising Auto- Encoders; 有关于模型的分类总结; DAE | ||||||||||
| 摘要描述 | Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDAE), for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. We demonstrate that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components. Thorough experiments are conducted to understand the performance of CDAE under various component settings. Furthermore, experimental results on several public datasets demonstrate that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics. | ||||||||||
