| 文献作者 | Weike Pan, Evan W. Xiang, Nathan N. Liu and Qiang Yang | ||||||||||
| 文献发表年限 | 2010 | 创建时间 | 2018-07-12 | ||||||||
| 文献关键字 | cross domain; learn and transfer the latent vectors; principled matrix; heterogeneous； intrinsic preference structure; Y_{u:}行,Y_{:i}列，y_{ui}实体; 将ratings 处理成implicit feedback； coordinate systems | ||||||||||
| 摘要描述 | Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem. | ||||||||||
| 文献作者 | Hyekyoung Lee, Jiho Yoo, and Seungjin Choi | ||||||||||
| 文献发表年限 | 2010 | 创建时间 | 2018-07-11 | ||||||||
| 文献关键字 | semi-supervised NMF； 分解方法求解NMF | ||||||||||
| 摘要描述 | Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, pro- viding a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are la- beled, the performance of clustering or classification is improved if the information on class labels is incorporated into NMF. To this end, we present semi-supervised NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label ma- trix into NMF. We develop multiplicative updates for SSNMF to minimize a sum of weighted residuals, each of which involves the nonnegative 2-factor decomposition of the data matrix or the label matrix, sharing a common factor matrix. Experiments on docu- ment datasets and EEG datasets in BCI competition confirm that our method improves clustering as well as classification perfor- mance, compared to the standard NMF, stressing that semi-super- vised NMF yields semi-supervised feature extraction. | ||||||||||
| 文献作者 | Chung-Yi Li | ||||||||||
| 文献发表年限 | 2014 | 创建时间 | 2018-07-11 | ||||||||
| 文献关键字 | Collaborative Filtering; Transfer Learning; Matrix Factorization; cross domain； constraint;奇异值分解；CBT；RMGM | ||||||||||
| 摘要描述 | Given two homogeneous rating matrices with some overlapped users/items whose mappings are unknown, this paper aims at an- swering two questions. First, can we identify the unknown map- ping between the users and/or items? Second, can we further utilize the identified mappings to improve the quality of recommendation in either domain? Our solution integrates a latent space match- ing procedure and a refining process based on the optimization of prediction to identify the matching. Then, we further design a transfer-based method to improve the recommendation perfor- mance. Using both synthetic and real data, we have done extensive experiments given different real life scenarios to verify the effec- tiveness of our models. The code and other materials are available at http://www.csie.ntu.edu.tw/~r00922051/matching/ | ||||||||||
| 文献作者 | Bin Li；Qiang Yang; Xiangyang Xue | ||||||||||
| 文献发表年限 | 2009 | 创建时间 | 2018-07-11 | ||||||||
| 文献关键字 | cross-domain; transfer；codebook；CBT | ||||||||||
| 摘要描述 | The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviat- ing the sparsity problem in CF by transferring user- item rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rat- ing website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two do- mains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster- level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task do- main. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient al- gorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transfer- ring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods. | ||||||||||
| 文献作者 | Meng Jiang, Peng Cui | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2018-07-11 | ||||||||
| 文献关键字 | cross-domain; overlapped; transfer；semi- supervised NMF | ||||||||||
| 摘要描述 | People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fully- overlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially over- lapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPT RANS . To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user’s behaviors in different platforms. Extensive experiments across two real social networks show that XPT RANS significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPT RANS can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms. | ||||||||||
| 文献作者 | Chih-Jen Lin | ||||||||||
| 文献发表年限 | 2007 | 创建时间 | 2018-07-04 | ||||||||
| 文献关键字 | 非负矩阵求法；NMF解释; VQ (vector quantization->K-means) | ||||||||||
| 摘要描述 | Nonnegative matrix factorization (NMF) can be formulated as a mini- mization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided. | ||||||||||
| 文献作者 | Chris Ding | ||||||||||
| 文献发表年限 | 2006 | 创建时间 | 2018-06-30 | ||||||||
| 文献关键字 | 3-factor NMF; semi-NMF; 带有正交约束的NMF求解方法；证明NMF等同于k-means； Nonnegative, non-negative; trace, 矩阵的迹trace；矩阵分解自由度； the degree of freedom | ||||||||||
| 摘要描述 | Currently, most research on nonnegative matrix factorization (NMF) focus on 2-factor X = FG T factorization. We provide a systematic analysis of 3-factor X = FSG T NMF. While unconstrained 3-factor NMF is equivalent to unconstrained 2-factor NMF, constrained 3- factor NMF brings new features to constrained 2-factor NMF. We study the orthogonality constraint because it leads to rigorous clus- tering interpretation. We provide new rules for updating F, S, G and prove the convergence of these algorithms. Experiments on 5 datasets and a real world case study are performed to show the capability of bi-orthogonal 3-factor NMF on simultaneously clus- tering rows and columns of the input data matrix. We provide a new approach of evaluating the quality of clustering on words us- ing class aggregate distribution and multi-peak distribution. We also provide an overview of various NMF extensions and examine their relationships. | ||||||||||
| 文献作者 | Patrik O. Hoyer | ||||||||||
| 文献发表年限 | 2004 | 创建时间 | 2018-06-28 | ||||||||
| 文献关键字 | non-negative matrix factorization, sparseness, data-adaptive representations；稀疏矩阵分解； | ||||||||||
| 摘要描述 | Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems. | ||||||||||
| 文献作者 | Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, Qiang Yang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2018-06-27 | ||||||||
| 文献关键字 | bandit policy; 线性 ；linear；exploitation; exploration | ||||||||||
| 摘要描述 | Traditional recommendation systems (RecSys) suffer from two problems: the exploitation-exploration dilemma and the cold-start problem. One solution to solving the exploitation- exploration dilemma is the contextual bandit policy, which adaptively exploits and explores user interests. As a result, the contextual bandit policy achieves increased rewards in the long run. The contextual bandit policy, however, may cause the system to explore more than needed in the cold-start situations, which can lead to worse short-term rewards. Cross-domain RecSys methods adopt transfer learning to leverage prior knowledge in a source RecSys domain to jump start the cold-start target RecSys. To solve the two problems together, in this paper, we propose the first applicable transferable contextual bandit (TCB) policy for the cross-domain recommendation. TCB not only benefits the exploitation but also accelerates the exploration in the target RecSys. TCB’s exploration, in turn, helps to learn how to transfer between different domains. TCB is a general algorithm for both homogeneous and heterogeneous domains. We perform both theoretical regret analysis and empirical experiments. The empirical results show that TCB outperforms the state-of-the-art algorithms over time. | ||||||||||
| 文献作者 | Himan Abdollahpouri; Robin Burke; Bamshad Mobasher | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2018-06-19 | ||||||||
| 文献关键字 | Recommender systems; long-tail; Recommendation evaluation; Coverage; Learning to rank; 长尾；diversity；多样性 | ||||||||||
| 摘要描述 | Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-tail coverage of recommendation lists in a learning-to-rank algorithm. We show that regularization provides a tunable mechanism for controlling the trade-off between accuracy and coverage. Moreover, the experimental results using two data sets show that it is possible to improve coverage of long tail items without substantial loss of ranking performance. | ||||||||||
