with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype

that this relationship applies to the dropout situation as well. Not only is the motivational model theoretically sound, it is also in line with the high school dropout literature. First, with respect to the social context, dropout students, relative to persistent students, report that they participate much less

be better equipped to deal directly with sexual health topics. • The normalization of sexual violence is pervasive in our culture, and too few know what healthy relationships look like. It is time to wake up to its presence in our lives and work across sectors to change negative social norms. • The unique

education (HE) via a range of approaches. Ultralab’s work with informal learning communities has led to the development of a community approach to support online programmes, 1 because our observations indicate that strong community leads to deep learning (Marton & Saljo, 1976). This paper presents our

Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a

. With the learned kernel, the second stage rectifies the pair-wise learning to rank approach to estimating the social strength. By learning the social strength graph, we are able to conduct collaborative recommendation and collective classification. The promising results show that the learning-based approach

Nowadays social websites have become a major trend in the Web 2.0 environment, enabling abundant social data available. In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender

and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical rela-tionships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection

.g. Web pages, research papers) recommen-dation using purely tagging data. That is, we only have data containing users, tags, documents and the relation-ships among them. We propose a novel graph-based rep-resentation learning algorithm for this purpose. The users, tags and documents are represented

ing attention and research efforts in a variety of artificial intelligence areas including speech recognition, computer vision, machine learning, text mining, etc. Since one main purpose of deep learning is to learn robust and powerful feature represen-tations for complex data, it is very natural