Semantic Tagging on Historical Maps

Tags assigned by users to shared content can be ambiguous. As a possible solution, we propose semantic tagging as a collaborative process in which a user selects and associates Web resources drawn from a knowledge context. We applied this general technique in the specific context of online historical maps and allowed users to annotate and tag them. To study the effects of semantic tagging on tag production, the types and categories of obtained tags, and user task load, we conducted an in-lab within-subject experiment with 24 participants who annotated and tagged two distinct maps. We found that the semantic tagging implementation does not affect these parameters, while providing tagging relationships to well-defined concept definitions. Compared to label-based tagging, our technique also gathers positive and negative tagging relationships. We believe that our findings carry implications for designers who want to adopt semantic tagging in other contexts and systems on the Web.Comment: 10 page

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation

The OpenCitations Data Model

A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations. This diversity, and the reuse of the same ontology terms with different nuances, generates inconsistencies in data. Adoption of a single data model would facilitate data integration tasks regardless of the data supplier or context application. In this paper we present the OpenCitations Data Model (OCDM), a generic data model for describing bibliographic entities and citations, developed using Semantic Web technologies. We also evaluate the effective reusability of OCDM according to ontology evaluation practices, mention existing users of OCDM, and discuss the use and impact of OCDM in the wider open science community.Comment: ISWC 2020 Conference proceeding

The Evolution of myExperiment

The myExperiment social website for sharing scientific workflows, designed according to Web 2.0 principles, has grown to be the largest public repository of its kind. It is distinctive for its focus on sharing methods, its researcher-centric design and its facility to aggregate content into sharable 'research objects'. This evolution of myExperiment has occurred hand in hand with its users. myExperiment now supports Linked Data as a step toward our vision of the future research environment, which we categorise here as '3rd generation e-Research'

OSN Model For Business Growth Using Ecommerce Product Recommendation

Now A Days Online Shopping Has Achieved A Tremendous Popularity Within Very Less Amount Of Time. Recently Few Ecommerce Websites Has Been Developed Their Functionalities To A Extent Such That They Recommend The Product For Their Users Referring To The Connectivity Of The Users To The Social Media And Provide Direct Login From Such Social Media Such As Facebook, Twitter, Whatsapp. Recommend The Users That Are Totally New To The Website Client Novel Solution For Cross-Site Cold-Start Product Recommendation That Aims For Recommending Products From E-Commerce Websites. In Specific Propose Learning Both Users And Products Feature Representations From Data Collected From E-Commerce Websites Using Recurrent Top-K To Transform User’s Social Networking Features Into User Embeddings. The Survey Paper Develops A Top-K Approach Which Can Manipulate The Learnt User Implanting For Cold-Start Product Recommendation

The Adoption and Effectiveness of Automation in Health Evidence Synthesis

Background: Health systems worldwide are often informed by evidence-based guidelines which in turn rely heavily on systematic reviews. Systematic reviews are currently hindered by the increasing volume of new research and by its variable quality. Automation has potential to alleviate this problem but is not widely used in health evidence synthesis. This thesis sought to address the following: why is automation adopted (or not), and what effects does it have when it is put into use? / Methods: Roger’s Diffusion of Innovations theory, as a well-established and widely used framework, informed the study design and analysis. Adoption barriers and facilitators were explored through a thematic analysis of guideline developers’ opinions towards automation, and by mapping the adoption journey of a machine learning (ML) tool among Cochrane Information Specialists (CISs). A randomised trial of ML assistance in Risk of Bias (RoB) assessments and a cost-effectiveness analysis of a semi-automated workflow in the maintenance of a living evidence map each evaluated the effects of automation in practice. / Results: Adoption decisions are most strongly informed by the professional cultural expectations of health evidence synthesis. The stringent expectations of systematic reviewers and their users must be met before any other characteristic of an automation technology is considered by potential adopters. Ease-of-use increases in importance as a tool becomes more diffused across a population. Results of the randomised trial showed that ML-assisted RoB assessments were non-inferior to assessments completed entirely by human researcher effort. The cost-effectiveness analysis showed that a semi-automated workflow identified more relevant studies than the manual workflow and was less costly. / Conclusions: Automation can have substantial benefits when integrated into health evidence workflows. Wider adoption of automation tools will be facilitated by ensuring they are aligned with professional values of the field and limited in technical complexity

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers' ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines

An investigation, evaluation and development of techniques to enable the spread and adoption of innovative practices, based on the Trent region older people services project (TROPSP)

This report contributes just over a third of the contribution to the researcher's D.Prof programme (see Ai Exhibits). It is a synthesis of many different activities and avenues of investigation and learning. This report is about one specific project and is focused on the ways to support the spread and adoption of innovative practices. It is not intended to be a report of the entire D.Prof Programme as the other areas are covered separately (see Exhibits Ai for D.Prof Programme Design). The literature review threw up a number of conflicts of definitions and perspectives, especially in the terminology that can be applied to 'spreading good practice' and 'social marketing'. The many paradoxes and contested concepts are highlighted in the review and the discussion that follows. Whilst this part of the D.Prof programme is centred on a work-based project - The Trent Region Older People Services Programme (TROPSP) - it has been difficult to separate learning in this project from other work based experiences in the same period. The deliverables and outputs generated (see Part B Exhibits) demonstrate both the breadth and depth of the researcher's experience and learning during this D.Prof programme. The experiential nature of action-based research is highly subjective as the researcher is an active participant in the investigative process, where personal actions immediately affect and have consequences on the context and subject matter under investigation. This report, therefore, needs to be read in the light of its context for the researcher, and understood as a piece of qualitative, action orientated research, rather than an analysis driven by more positivist or scientific values. The literature review, assessment of the TROPSP project and discussion about the researcher's personal learning themes, combine to produce a set of conclusions and recommendation as diverse and contested as is the topic of interprofessional social marketing itself. The paradoxes and tensions include: how different theories and frameworks can form unhelpful (or helpful) mental models; the importance of context, perspectives and expectations and how they can influence strategy and implementation of good practice; the tension between the individual and the organisation; how working with key influencers can be as damaging as it can be as supportive; and finally, the issue of whether the aim in social marketing is to spread good practice (Push out) or to enable adoption (Pull in). The work summarised in this report has received national and international recognition. The contribution to the modernising the NBS has been significant and there is much interest from other countries in using some of the techniques developed and used in the TROPSP work based project. The implications for professional practice, for those working with modernising healthcare as well as specifically for the researcher, are important