The common matrix completion methods minimize the rank of the matrix to be completed in addition to the Hamming loss between the incomplete and completed matrices. The rank of matrix measures the linear relation among the vectors of matrix, which may introduce ambiguity for data recovery. To cope with this issue, we extend multi-label ranking loss into matrix completion, and employ multi-label ranking loss minimization (MLRM) in this paper to exploit the relative correlation among matrix vectors. In MLRM, the original incomplete matrix is converted into a pairwise ranking matrix, and the approximation on this newly generated matrix can be viewed as a surrogate of multi-label ranking loss to replace the Hamming loss pattern in the existing methods. Extensive experiments demonstrate that MLRM outperforms the state-of-the-art matrix completion methods in varies of applications, including movie recommendation, drug-target interaction prediction and multi-label learning.

Abstract Session‐based recommendation that uses sequence of items clicked by anonymous users to make recommendations has drawn the attention of many researchers, and a lot of approaches have been proposed. However, there are still problems that have not been well addressed: (1) Time information is either ignored or exploited with a fixed time span and granularity, which fails to understand the personalized interest transfer pattern of users with different clicking speeds; (2) Category information is either omitted or considered independent of the items, which defies the fact that the relationships between categories and items are helpful for the recommendation. To solve these problems, we propose a new session‐based recommendation method, TCSR (self‐attention with time and category for session‐based recommendation). TCSR uses a non‐linear normalized time embedding to perceive user interest transfer patterns on variable granularity and employs a heterogeneous SAN to make full use of both items and categories. Moreover, a cross‐recommendation unit is adapted to adjust recommendations on the item and category sides. Extensive experiments on four real datasets show that TCSR significantly outperforms state‐of‐the‐art approaches.

During the last decades, multi-label classification (MLC) has attracted the attention of more and more researchers due to its wide real-world applications. Many boosting methods for MLC have been proposed and achieved great successes. However, these methods only extend existing boosting frameworks to MLC and take loss functions in multi-label version to guide the iteration. These loss functions generally give a comprehensive evaluation on the label set entirety, and thus the characteristics of different labels are ignored. In this paper, we propose a multi-path AdaBoost framework specific to MLC, where each boosting path is established for distinct label and the combination of them is able to provide a maximum optimization to Hamming Loss. In each iteration, classifiers chain is taken as the base classifier to strengthen the connection between multiple AdaBoost paths and exploit the label correlation. Extensive experiments demonstrate the effectiveness of the proposed method.