اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدImproving Usability of User Centric Decision Making of Multi-Attribute Products on E-commerce Websites
53
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The high number of products available makes it difficult for a user to find the most suitable products according to their needs. This problem is especially exacerbated when the user is trying to optimize multiple attributes during product selection, e.g. memory size and camera resolution requirements in case of smartphones. Previous studies have shown that such users search extensively to find a product that best meets their needs. In this paper, we propose an interface that will help users in selecting a multi-attribute product through a series of visualizations. This interface is especially targeted for users that desire to purchase the best possible product according to some criteria. The interface works by allowing the user to progressively shortlist products and ultimately select the most appropriate product from a very small consideration set. We evaluated our proposed interface by conducting a controlled experiment that empirically measures the efficiency, effectiveness and satisfaction of our visualization based interface and a typical e-commerce interface. The results showed that our proposed interface allowed the user to find a desired product quickly and correctly, moreover, the subjective opinion of the users also favored our proposed interface.
قيم البحث
اقرأ أيضاً
This paper presents a pilot study on developing an instrument to predict the quality of e-commerce websites. The 8C model was adopted as the reference model of the heuristic evaluation. Each dimension of the 8C was mapped into a set of quantitative w
ebsite elements, selected websites were scraped to get the quantitative website elements, and the score of each dimension was calculated. A software was developed in PHP for the experiments. In the training process, 10 experiments were conducted and quantitative analyses were regressively conducted between the experiments. The conversion rate was used to verify the heuristic evaluation of an e-commerce website after each experiment. The results showed that the mapping revisions between the experiments improved the performance of the evaluation instrument, therefore the experiment process and the quantitative mapping revision guideline proposed was on the right track. The software resulted from the experiment 10 can serve as the aimed e-commerce website evaluation instrument. The experiment results and the future work have been discussed.
Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evalua
tes the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.
A distributed and transparent ledger system is considered for various e-commerce products including health medicines, electronics, security appliances, food products and many more to ensure technological and e-commerce sustainability. This solution,
named as PRODCHAIN, is a generic blockchain framework with lattice-based cryptographic processes for reducing the complexity for tracing the e-commerce products. Moreover, we have introduced a rating based consensus process called Proof of Accomplishment (PoA). The solution has been analyzed and experimental studies are performed on Ethereum network. The results are discussed in terms of latency and throughput which prove the efficiency of PRODCHAIN in e-commerce products and services. The presented solution is beneficial for improving the traceability of the products ensuring the social and financial sustainability. This work will help the researchers to gain knowledge about the blockchain implications for supply chain possibilities in future developments for society.
Designing infographics can be a tedious process for non-experts and time-consuming even for professional designers. Based on the literature and a formative study, we propose a flexible framework for automated and semi-automated infographics design. T
his framework captures the main design components in infographics and streamlines the generation workflow into three steps, allowing users to control and optimize each aspect independently. Based on the framework, we also propose an interactive tool, ame{}, for assisting novice designers with creating high-quality infographics from an input in a markdown format by offering recommendations of different design components of infographics. Simultaneously, more experienced designers can provide custom designs and layout ideas to the tool using a canvas to control the automated generation process partially. As part of our work, we also contribute an individual visual group (VG) and connection designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs. This dataset plays a crucial role in diversifying the infographic designs created by our framework. We evaluate our approach with a comparison against similar tools, a user study with novice and expert designers, and a case study. Results confirm that our framework and ame{} excel in creating customized infographics and exploring a large variety of designs.
Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This
paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of Unremarkable Computing, that by augmenting the users routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
