Learn More
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(More)
Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than learning each task individually. The feature selection problem in multi-task setting has many applications in fields of computer vision, text classification and bio-informatics.(More)
One-class collaborative filtering or collaborative ranking with implicit feedback has been steadily receiving more attention, mostly due to the " one-class " characteristics of data in various services, e.g., " like " in Facebook and " bought " in Amazon. Previous works for solving this problem include pointwise regression methods based on absolute rating(More)
Data sparsity due to missing ratings is a major challenge for collaborative filtering (CF) techniques in recommender systems. This is especially true for CF domains where the ratings are expressed numerically. We observe that, while we may lack the information in numerical ratings, we may have more data in the form of binary ratings. This is especially true(More)
Regularized risk minimization often involves non-smooth optimization, either because of the loss function (e.g., hinge loss) or the regularizer (e.g., ℓ 1-regularizer). Gradient methods, though highly scalable and easy to implement, are known to converge slowly. In this paper, we develop a novel accelerated gradient method for stochastic optimization while(More)
Collaborative filtering aims to make use of users' feedbacks to improve the recommendation performance, which has been deployed in various industry recommender systems. Some recent works have switched from exploiting explicit feedbacks of numerical ratings to implicit feedbacks like browsing and shopping records, since such data are more abundant and easier(More)
To solve the sparsity problem in collaborative filtering, researchers have introduced transfer learning as a viable approach to make use of auxiliary data. Most previous transfer learning works in collaborative filtering have focused on exploiting point-wise ratings such as numerical ratings, stars, or binary ratings of likes/dislikes. However, in many(More)
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations according to the Hilbert Schmidt independence criterion (HSIC). It is unique in that structure information on the cluster outputs can be easily utilized in the clustering process.(More)
a r t i c l e i n f o a b s t r a c t A major challenge for collaborative filtering (CF) techniques in recommender systems is the data sparsity that is caused by missing and noisy ratings. This problem is even more serious for CF domains where the ratings are expressed numerically, e.g. as 5-star grades. We assume the 5-star ratings are unordered bins(More)
Sensor deployment is a fundamental issue in sensor networks. Studies on the deployment problem are concentrated deploying sensors to cover assigned areas or set of points efficiently. In this paper, we discuss the sensor deployment problem in the context of target tracking. Firstly, we propose a sensor deployment optimization strategy based on target(More)