Expert Review on Usefulness of an Integrated Checklist-based Mobile Usability Evaluation Framework

Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mo­bility aspects.In addition,much of the previous mobile usability conception was built on desktop computing measure­ments,such as desktop and web application checklists,or scarcely addressed the mobile user interface.Moreover,the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and spinners.Therefore,conducting usability evaluation using conventional us­ability measurement would result in irrelevant results.In addition,the resulting works are tailored for usability testing,which requires highly skilled evaluators and usability specialists(e.g.,usability testers and user experience designers),who are rarely integrated into a development team.The lack of expertise could lead to unreliable usability evaluations.This paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous work.The framework is dedicated to smartphone apps,which integrate evaluator skills and design concerns.However,there is no evidence of its usefulness in practice.Therefore,the usefulness of the frame­work measurement for evaluating apps’usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert review.The expert review involved eleven industrial developers and was complemented by a semi-structured interview.The method is replicated in comparison with a framework from another study.The findings show that the formulated framework significantly outperformed the framework(p=0.0286)from other studies with large effect sizes(r=1.81)in terms of usefulness.

Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship

The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert’s rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the 'cold start' problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation(LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert,and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts.