Recommender systems have become ubiquitous, transforming user interactions with products, services and content in a wide variety of domains. In content recommendation, recommenders generally surface relevant and/or novel personalized content based on learned models of user preferences (e.g., as in collaborative filtering [Breese et al., 1998, Konstan et al., 1997, Srebro et al., 2004, Salakhutdinov and Mnih, 2007]) or predictive models of user responses to specific recommendations. Well-known applications of recommender systems include video recommendations on YouTube [Covington et al., 2016], movie recommendations on Netflix [Gomez-Uribe and Hunt, 2016] and playlist construction on Spotify [Jacobson et al., 2016]. It is increasingly common to train deep neural networks (DNNs) [van den Oord et al., 2013, Wang et al., 2015, Covington et al., 2016, Cheng et al., 2016] to predict user responses (e.g., click-through rates, content engagement, ratings, likes) to generate, score and serve candidate recommendations. Practical recommender systems largely focus on myopic prediction--estimating a user's immediate response to a recommendation--without considering the long-term impact on subsequent user behavior. This can be limiting: modeling a recommendation's stochastic impact on the future affords opportunities to trade off user engagement in the near-term for longer-term benefit (e.g., by probing a user's interests, or improving satisfaction).

WHEN I was growing up, nobody promised me a flying car. But I was promised an AI apocalypse. Those shiny machines were going to crush our skulls underfoot, and we were all going to welcome our new robot overlords. Many people still seem to think it is likely to happen. But we might still get a deadly AI nightmare.

The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have been made to model neural-based reasoning as an iterative decision-making process based on recurrent networks and reinforcement learning. Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow). Aiming to bridge the gap between deep learning systems and reasoning, we propose an attentive computation framework with a three-layer architecture, which consists of an unconsciousness flow layer, a consciousness flow layer, and an attention flow layer. We implement the NeuCFlow model with graph neural networks (GNNs) and conditional transition matrices. Our attentive computation greatly reduces the complexity of vanilla GNN-based methods, capable of running on large-scale graphs. We validate our model for knowledge graph reasoning by solving a series of knowledge base completion (KBC) tasks. The experimental results show NeuCFlow significantly outperforms previous state-of-the-art KBC methods, including the embedding-based and the path-based. The reproducible code can be found by the link below.

Several complex tasks that arise in organizations can be simplified by mapping them into a matrix completion problem. In this paper, we address a key challenge faced by our company: predicting the efficiency of artists in rendering visual effects (VFX) in film shots. We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0, 1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment. Our first approach, expertise matrix factorization (EMF), is an interpretable method that structures the latent factors as weighted user-item interplay. The second one, survival matrix factorization (SMF), is instead a probabilistic model for the underlying process defining employees' efficiencies. We show the effectiveness of our proposed models by extensive numerical tests on our VFX dataset and two additional datasets with values that are also bounded in the [0, 1] interval.

The film world is full of intriguing what-ifs. Will Smith famously turned down the role of Neo in The Matrix. Nicolas Cage was cast as the lead in Tim Burton's Superman Lives, but he only had time to try on the costume before the film was canned. Actors and directors are forever glancing off projects that never get made or that get made by someone else, and fans are left wondering what might have been. For the people who make money from movies, that isn't good enough.

How difficult can it be to detect rectangles on an image? Well… it can be nearly impossible if there are no actual rectangles at all. Edges between a YouTube video's background and overlaid GoT scenes are visible only due to the optical illusion called illusory contours. In this task, we had to rely a little bit on the incredible vision of humans and make some annotations. Thankfully, it was enough to manually mark coordinates only on a single frame per YouTube video.

How difficult can it be to detect rectangles on an image? Well… it can be nearly impossible if there are no actual rectangles at all. Edges between a YouTube video's background and overlaid GoT scenes are visible only due to the optical illusion called illusory contours. In this task, we had to rely a little bit on the incredible vision of humans and make some annotations. Thankfully, it was enough to manually mark coordinates only on a single frame per YouTube video.

The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and feature extraction from MP3 audio files. The metric learning process considers the learning of a set of parameterized distances employing a structured prediction approach from a set of MP3 audio files containing several music genres. The main objective of this work is to make possible learning a personalized metric for each customer. To extract the acoustic information we use the Mel-Frequency Cepstral Coefficient (MFCC) and make a dimensionality reduction with the use of Principal Components Analysis. We attest the model validity performing a set of experiments and comparing the training and testing results with baseline algorithms, such as K-means and Soft Margin Linear Support Vector Machine (SVM). Experiments show promising results and encourage the future development of an online version of the learning model.

Music has the power of inducing emotions, and human beings exploit such a phenomenon in order to empower a variety of mental states and activities, both positively and negatively. The study of emotions and music has a long and still vibrant tradition. New findings and changes of perspective in the field are not uncommon. More recent is the field investigating music emotion recognition through computational means. Music emotion recognition (MER) is an emerging and cross-disciplinary field spanning information retrieval (audio, symbolic and metadata) and machine learning, on a strong backing of music cognition (semiology of music and psychology) and music theory.

A'selfie robot' that is set to replace instant cameras and photo booths at parties has made its debut at at a UK wedding. The robot, designed by Birmingham-based firm, allowed wedding goers at a local reception to send photos it to themselves by email or text. Armed with AI software that can detect faces, the robot can'roam freely' around a room and stops to ask people if they would like their photo taken. It also has an infrared sensor that prevents it smashing into people and obstacles. Eva Photography Robot has been developed by a computer programmer in Birmingham to take selfies of partygoers and made its first debut at a wedding.