Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of content makes it difficult for a user to find relevant tracks. Relevant recommendations are therefore crucial for a good user experience. Here we present a method to compute tr...

The robust principal component analysis (robust PCA) problem has been considered in many machine learning applications, where the goal is to decompose the data matrix to a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse structure, in many applications, side information of row and/or column entities may also be given, and ...

This paper presents conditional versions of Lempel-Ziv (LZ) algorithm for settings where compressor and decompressor have access to the same side information. We propose a fixed-length-parsing LZ algorithm with side information, motivated by the Willems algorithm, and prove the optimality for any stationary processes. In addition, we suggest strategies to improve the algorithm which lower the d...

Block-based image and video coding systems are used extensively in practice. In low bitrate applications, however, they suffer from annoying discontinuities, called blocking artifacts. Prior research shows that incorporating systems that reduce blocking artifacts into codecs is useful because visual quality is improved. Existing methods reduce blocking artifacts by applying various post-process...

Recommender systems have been widely studied in the literature as they have real world impacts in many E-commerce platforms and social networks. Most previous systems are based on the user-item recommendation matrix, which contains users’ history recommendation activities on items. In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially obs...

Supervised, semi-supervised, and unsupervised learning estimate a function given input/output samples. Generalization of the learned function to unseen data can be improved by incorporating side information into learning. Side information are data that are neither from the input space nor from the output space of the function, but include useful information for learning it. In this paper we sho...

Pairwise clustering, in general, partitions a set of items via a known similarityfunction. In our treatment, clustering is modeled as a transductive predictionproblem. Thus rather than beginning with a known similarity function, the functioninstead is hidden and the learner only receives a random sample consisting of asubset of the pairwise similarities. An additional set of pai...

Protection Objectives Sensitive information protection IPR protection Privacy protection Protection Architectures Server-side reference monitor Client-side reference monitor SRM & CRM Server-side Reference Monitor (SRM) Client-side Reference Monitor (CRM) Traditional Access Control Trust Management Usage Control Sensitive Information Protection Intellectual Property Rights Protection Privacy Pr...

This paper is a preliminary report which presents information filtering solutions designed within the scope of a collaboration between our laboratory and the company of broadcasting per satellite SES ASTRA. The latter have finalized a system sponsored by advertisement and supplying to users a high bandwidth access to hundreds of web sites for free. This project aims at highlighting the benefits...