Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering

In most existing recommender systems, implicit or explicit interactions are treated as positive links and all unknown interactions are treated as negative links. The goal is to suggest new links that will be perceived as positive by users. However, as signed social networks and newer content services become common, it is important to distinguish between positive and negative preferences. Even in existing applications, the cost of a negative recommendation could be high when people are looking for new jobs, friends, or places to live. In this work, we develop novel probabilistic latent factor models to recommend positive links and compare them with existing methods on five different openly available datasets. Our models are able to produce better ranking lists and are effective in the task of ranking positive links at the top, with fewer negative links (flops). Moreover, we find that modeling signed social networks and user preferences this way has the advantage of increasing the diversity of recommendations. We also investigate the effect of regularization on the quality of recommendations, a matter that has not received enough attention in the literature. We find that regularization parameter heavily affects the quality of recommendations in terms of both accuracy and diversity

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

An Anti-Urban Education? Work camps and ideals of the land in Interwar Britain

The article examines the role of work camp movements in developing rural critiques of urban living in interwar Britain. A variety of work camp movements flourished in Europe during the interwar years, often partly as a reaction against urbanisation, and this paper explores the ways in which three such movements developed the work camp as a means of countering the socialising influences of city life. Yet while all of the interwar British work camps were located in the countryside, they varied in the extent to which they tried to promote rural values and orientations among their trainees.We can see the work camp as a liminal pedagogic space, designed to lead trainees to particular educational outcomes, using techniques and methods that focused on bodily change as well as cognitive development

Whiting–related sediment export along the Middle Miocene carbonate ramp of Great Bahama Bank.

International audienc

Endometriosis : a radiological review

Endometriosis is a chronic gynaecological disorder primarily affecting women of reproductive age of uncertain aetiology. It is pathologically defined as the presence of functioning endometrium outside the uterus. Its physical presentation, clinical course and radiological appearances are varied. Endometriosis most commonly affects the ovaries and adjacent pelvic structures. Deposits on serosal surfaces lead to fibrosis and subsequent adhesions, strictures and tubal obstruction. Ultrasound is useful in identifying and characterising adnexal endometriotic cysts by their characteristic ultrasonographic appearance including low level internal echoes. X-ray computed tomography plays a limited role in identification of an ovarian mass and sequelae of endometriosis such as bowel obstruction, pelvic effusions, ureteric obstruction and haemorrhagic pleural effusions. Magnetic resonance imaging plays an important role in characterising and quantifying adnexal and extra adnexal disease. It is particularly useful in identifying small deposits on the serosal surfaces of small and large bowel. These may appear as fibrotic linear areas or spiculate foci. Disease affecting the uterine ligaments and tubes often leads to obstructive hydrosalpinx with resultant sub fertility. Involvement of the urinary bladder and distal ureters may lead to stricturing and subsequent hydronephrosis. MRI is useful in characterising endometriotic deposits in unusual locations such as subcutaneous tissues and in relation to surgical scars. Disease of the pelvic side wall may involve the sacral plexus with resultant deep pelvic and lower limb referred pain. These sites are amenable to MRI investigation. MRI is playing an increasingly important role in the pre operative staging of endometriosis, enabling more focal targeting of disease during laparoscopy.peer-reviewe