Learning to Rank Using Localized Geometric Mean Metrics

Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query instance and documents. However, existing algorithms do not consider local structures in query-document feature space, and are fragile to irrelevant noise features. In this paper, we propose a novel Riemannian metric learning algorithm to capture the local structures and develop a robust LtR algorithm. First, we design a concept called \textit{ideal candidate document} to introduce metric learning algorithm to query-independent model. Previous metric learning algorithms aiming to find an optimal metric space are only suitable for query-dependent model, in which query instance and documents belong to the same feature space and the similarity is directly computed from the metric space. Then we extend the new and extremely fast global Geometric Mean Metric Learning (GMML) algorithm to develop a localized GMML, namely L-GMML. Based on the combination of local learned metrics, we employ the popular Normalized Discounted Cumulative Gain~(NDCG) scorer and Weighted Approximate Rank Pairwise (WARP) loss to optimize the \textit{ideal candidate document} for each query candidate set. Finally, we can quickly evaluate all candidates via the similarity between the \textit{ideal candidate document} and other candidates. By leveraging the ability of metric learning algorithms to describe the complex structural information, our approach gives us a principled and efficient way to perform LtR tasks. The experiments on real-world datasets demonstrate that our proposed L-GMML algorithm outperforms the state-of-the-art metric learning to rank methods and the stylish query-independent LtR algorithms regarding accuracy and computational efficiency.Comment: To appear in SIGIR'1

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN

Attitudes to depression and its treatment in primary care

Background Undertreatment of depression in primary care is common. Efforts to address this tend to overlook the role of patient attitudes. Our aim was to validate and describe responses to a questionnaire about attitudes to depression and its treatment in a sample with experience of moderate and severe depressive episodes. Method Cross-sectional survey of 866 individuals with a confirmed history of an ICD-10 depressive episode in the 12 months preceding interview, recruited from 7271 consecutive general practitioner (GP) attendees in 36 general practices in England and Wales. Attitudes to and beliefs about depression were assessed using a 19-item self-report questionnaire. Results Factor analysis resulted in a three-factor solution: factor 1, depression as a disabling, permanent state; factor 2, depression as a medical condition responsive to support; and factor 3, antidepressants are addictive and ineffective. Participants who received and adhered to antidepressant medication and disclosed their depression to family and friends had significantly lower scores on factors 1 and 3 but higher scores on factor 2. Conclusions People with moderate or severe depressive episodes have subtle and divergent views about this condition, its outcome, and appropriate help. Such beliefs should be considered in primary care as they may significantly impact on help seeking and adherence to treatment