Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction

A dual framework for low-rank tensor completion

One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization. However, most existing works in this direction learn a sparse combination of tensors. In this work, we fill this gap by proposing a variant of the latent trace norm that helps in learning a non-sparse combination of tensors. We develop a dual framework for solving the low-rank tensor completion problem. We first show a novel characterization of the dual solution space with an interesting factorization of the optimal solution. Overall, the optimal solution is shown to lie on a Cartesian product of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian optimization framework for proposing computationally efficient trust region algorithm. The experiments illustrate the efficacy of the proposed algorithm on several real-world datasets across applications.Comment: Aceepted to appear in Advances of Nueral Information Processing Systems (NIPS), 2018. A shorter version appeared in the NIPS workshop on Synergies in Geometric Data Analysis 201

Orbitofrontal cortex, emotional decision-making and response to cognitive behavioural therapy for psychosis

Grey matter volume (GMV) in the orbitofrontal cortex (OFC) may relate to better response to cognitive behavioural therapy for psychosis (CBTp) because of the region's role in emotional decision-making and cognitive flexibility. This study aimed to determine the relation between pre-therapy OFC GMV or asymmetry and CBTp responsiveness and emotional decision-making as measured by the Iowa Gambling Task (IGT). Thirty patients received CBTp + standard care (CBTp+SC; 25 completers) for 6-8 months. All patients (before receiving CBTp) and 25 healthy participants underwent structural magnetic resonance imaging and performed the IGT. Patients' symptoms were assessed before and after therapy. Pre-therapy OFC GMV, measured using a region-of-interest approach, and IGT performance, measured as overall learning, attention to reward, memory for past outcomes and choice consistency, were comparable between patient and healthy groups. In the CBTp+SC group, greater OFC GMV was correlated with positive symptom improvement, specifically hallucinations and persecution. Greater rightward OFC asymmetry correlated with improvement in several negative and general psychopathology symptoms. Greater left OFC GMV was associated with lower IGT attention to reward. The findings suggest that greater OFC volume and rightward asymmetry, which maintain the OFC's function in emotional decision-making and cognitive flexibility, are beneficial for CBTp responsiveness