AUC Optimisation and Collaborative Filtering

In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underpinning. In practical recommendation, only items at the top of the ranked list are presented to the users. With this in mind, we propose a class of objective functions over matrix factorisations which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list. The objectives are differentiable and optimised through a carefully designed stochastic gradient-descent-based algorithm which scales linearly with the size of the data. In the special case of square loss we show how to improve computational complexity by leveraging previously computed measures. To understand theoretically the underlying matrix factorisation approaches we study both the consistency of the loss functions with respect to AUC, and generalisation using Rademacher theory. The resulting generalisation analysis gives strong motivation for the optimisation under study. Finally, we provide computation results as to the efficacy of the proposed method using synthetic and real data

Culture and concept design : a study of international teams

This paper explores the relationship between culture and performance in concept design. Economic globalisation has meant that the management of global teams has become of strategic importance in product development. Cultural diversity is a key factor in such teams, and this work seeks to better understand the effect this can have on two key aspects of the concept design process: concept generation and concept selection. To this end, a group of 32 students from 17 countries all over the world were divided into culturally diverse teams and asked to perform a short design exercise. A version of the Gallery Method allowed two kinds of activity to be monitored – the individual development of concepts and the collective filtering and selection of them. The effect of culture on these processes was the focus of the work. Using Hofstede’s cultural dimensions, the output from the sessions were reviewed according to national boundaries. The results indicate that individualism and masculinity had the most discernable effect on concept generation and concept selection respectively

Development of an Ontology-Based Personalised E-Learning Recommender System

E-learning has become an active field of research with a lot of investment towards web-based delivery of personalised learning contents to learners. Some issues of e-learning arise from the heterogeneity and interoperability of learning content to suit learner’s style and preferences in order to improve the e-learning environment. Hence, this paper developed an ontology-based personalised recommender system that is needed to recommend suitable learning contents to learners using collaborative filtering and ontology. A pre-test is carried out for users in order to segment them in learning categories to suit their skill level. The learning contents are structured using ontology; and collaborative filtering is used to collects preferences from many users and then recommending the highest rated contents to users. The system is implemented using JAVA programming language with Structured Query Language (MySQL) as database management system. Performance evaluation of the system is carried out using survey and standard metrics such as precision, recall and F1-Measrure. The results from the two performance evaluation models showed that the system is suitable for recommending the required learning contents to learners

Annual Report #2

Share.TEC has undertaken to build an advanced user-focused system that aggregates metadata describing TE-related digital resources located Europe-wide. The system aims to offer personalized, culturally-sensitive brokerage for the retrieval of relevant digital content and to nurture a more Europe-wide perspective among those working in and with the TE community. As well as generally pursuing its objectives as set out in the Description of Work throughout Year 2, the consortium also targeted its efforts on a series of realignment actions. These were specifically devised as a suitable response to the findings of the First Intermediate Review (EC evaluation) and the project\u27s internal Year 1 Evaluation Report. The most important of the realignment actions regarded greater end-user involvement to ensure acceptance of project results (especially portal & services); significant enhancement of the system from prototype to pilot, ensuring it is capable of meeting user needs; steps to set up a network of user communities; measures to ensure a suitable balance between quantity and quality of items available in the portal. The actions taken in each of these cases are reported in Sections 5 and 7, while a detailed report listing each evaluation finding and the corresponding actions is contained in Deliverable D1.6

Mainstreaming Open Textbooks: Educator Perspectives on the Impact of OpenStax College open textbooks

This paper presents the results of collaborative research between OpenStax College, who have published 16 open textbooks to date, and the OER Research Hub, a Hewlett funded open research project examining the impact of open educational resources (OER) on learning and teaching. The paper focuses primarily on the results of two surveys that were conducted with educators during 2013 and 2014/2015. These surveys focused on use and perceptions of OER and OpenStax College materials, financial savings and perceptions of impact on both educators and students. This paper reports on the research findings related to the impact of OER on educator practice and make a series of specific recommendations based on these findings

Use What You Have: Video Retrieval Using Representations From Collaborative Experts

The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity, with some queries describing specific details such as the names of famous identities, content from speech, or text available on the screen. Our goal is to condense the multi-modal, extremely high dimensional information from videos into a single, compact video representation for the task of video retrieval using free-form text queries, where the degree of specificity is open-ended. For this we exploit existing knowledge in the form of pre-trained semantic embeddings which include 'general' features such as motion, appearance, and scene features from visual content. We also explore the use of more 'specific' cues from ASR and OCR which are intermittently available for videos and find that these signals remain challenging to use effectively for retrieval. We propose a collaborative experts model to aggregate information from these different pre-trained experts and assess our approach empirically on five retrieval benchmarks: MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet. Code and data can be found at www.robots.ox.ac.uk/~vgg/research/collaborative-experts/. This paper contains a correction to results reported in the previous version.Comment: This update contains a correction to previously reported result