Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Disease Surveillance Networks Initiative Global: Final Evaluation

In August 2009, the Rockefeller Foundation commissioned an independent external evaluation of the Disease Surveillance Networks (DSN) Initiative in Asia, Africa, and globally. This report covers the results of the global component of the summative and prospective1 evaluation, which had the following objectives:[1] Assessment of performance of the DSN Initiative, focused on its relevance, effectiveness/impact, and efficiency within the context of the Foundation's initiative support.[2] Assessment of the DSN Initiative's underlying hypothesis: robust trans-boundary, multi-sectoral/cross-disciplinary collaborative networks lead to improved disease surveillance and response.[3] Assessment of the quality of Foundation management (value for money) for the DSN Initiative.[4] Contribute to the field of philanthropy by:a. Demonstrating the use of evaluations in grantmaking, learning and knowledge management; andb. Informing the field of development evaluation about methods and models to measure complex networks

Information Filtering on Coupled Social Networks

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks

An evaluation of professional networks, co-ordination, cooperation and collaboration in the West Midlands Paediatric Palliative Care Network

Introduction: This is a report on Strand 3 of the Big Study, which studied the West Midlands Paediatric Palliative Care Network. The Big Study was funded by The Big Lottery Fund and Strand 3 of the Big Study was researched by the Centre for Nursing and Healthcare Research in the School of Health and Social Care at the University of Greenwich. 1.1 Background: The West Midlands Paediatric Palliative Care Network began as an interest group which started in the year 2000, with 6 to 10 members and grew. At one stage it was allied to the Birmingham Cancer Network and funded by the NHS Strategic Health Authority and at this stage it became more representative of services and West Midlands geography. It has existed in its current format, as a voluntary clinical network to promote paediatric palliative care and share best practice since 2009. The membership is wide and inclusive which means 30 to 40 people may attend the meetings which are held on a bimonthly basis and are hosted and supported charitably. Subgroups are now used to manage work in specific areas e.g. transition or clinical standards. There are links to other related networks with reciprocal membership and informal links to NHS commissioners who may seek advice. 1.2 Scope: This strand of the Big Study focused on the West Midlands Paediatric Palliative Care Network. The geographical area of the West Midlands Paediatric Palliative Care Network includes Birmingham, Coventry, The Black Country, Herefordshire, Shropshire, Solihull, Staffordshire, Stoke-on-Trent, Telford and Wrekin, Warwickshire and Worcestershire. All members of the WMPCCN and the organisations they represent were included in the study. Both NHS and non-NHS organisations offering clinical services to any children requiring palliative care were represented. Excluded from this study was the detailed examination of any of the other networks, e.g. children’s speciality networks or networks covering smaller geographical areas, to which members belonged. 1.3 Report: This report will present the results of an analysis of the responses to an online questionnaire and Social Network data from semi structured telephone interviews. This data was collected during the period February to June 2012. The approach included analysing the online survey data in order to understand the benefits and constraints of the network for individual members and Social Network Analysis of data derived from telephone interviews to explore the flow of knowledge, communication and information within the network. This report will consist of 3 different sections, with Section 1 focusing on children’s palliative care policy, the development of clinical networks and social network analysis concepts. Section 2 will focus on the research design and methods. Section 3 presents the results of the study and the final section provides a summary and conclusions of the analysis

Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0