4,629 research outputs found
Ranking algorithms on directed configuration networks
This paper studies the distribution of a family of rankings, which includes
Google's PageRank, on a directed configuration model. In particular, it is
shown that the distribution of the rank of a randomly chosen node in the graph
converges in distribution to a finite random variable that can
be written as a linear combination of i.i.d. copies of the endogenous solution
to a stochastic fixed point equation of the form where is a
real-valued vector with , , and the are i.i.d. copies of ,
independent of . Moreover, we
provide precise asymptotics for the limit , which when the
in-degree distribution in the directed configuration model has a power law
imply a power law distribution for with the same exponent
Search Results: Predicting Ranking Algorithms With User Ratings and User-Driven Data
The purpose of this correlational quantitative study was to examine the possible relationship between user-driven parameters, user ratings, and ranking algorithms. The study’s population consisted of students and faculty in the information technology (IT) field at a university in Huntington, WV. Arrow’s impossibility theorem was used as the theoretical framework for this study. Complete survey data were collected from 47 students and faculty members in the IT field, and a multiple regression analysis was used to measure the correlations between the variables. The model was able to explain 85% of the total variability in the ranking algorithm. The overall model was able to significantly predict the algorithm ranking discounted cumulative gain, R2 = .852, F(3,115) = 220.13, p \u3c .01. The Respondent DCG and Search term variables were the most significant predictor with p = .0001. The overall findings can potentially be useful to content providers who focus their content on a specific niche. The content created by these providers would most likely be focused entirely on that subgroup of interested users. While it is necessary to focus content to the interested users, it may be beneficial to expand the content to more generic terms to help reach potential new users outside of the subgroups of interest. User’s searching for more generic terms could potentially be exposed to more content that would generally require more specific search terms. This exposure with more generic terms could help users expand their knowledge of new content more quickly
Evolutionary Service Composition and Personalization Ecosystem for Elderly Care
Current demographic trends suggest that people are living longer, while
the ageing process entails many necessities, calling for care services tailored to
the individual senior’s needs and life style. Personalized provision of care
services usually involves a number of stakeholders, including relatives, friends,
caregivers, professional assistance organizations, enterprises, and other support
entities. Traditional Information and Communication Technology based care and
assistance services for the elderly have been mainly focused on the development
of isolated and generic services, considering a single service provider, and
excessively featuring a techno-centric approach.
In contrast, advances on collaborative networks for elderly care suggest the
integration of services from multiple providers, encouraging collaboration as a
way to provide better personalized services. This approach requires a support
system to manage the personalization process and allow ranking the {service,
provider} pairs.
An additional issue is the problem of service evolution, as individual’s care
needs are not static over time. Consequently, the care services need to evolve
accordingly to keep the elderly’s requirements satisfied. In accordance with these
requirements, an Elderly Care Ecosystem (ECE) framework, a Service
Composition and Personalization Environment (SCoPE), and a Service Evolution
Environment (SEvol) are proposed.
The ECE framework provides the context for the personalization and
evolution methods. The SCoPE method is based on the match between the
customer´s profile and the available {service, provider} pairs to identify suitable
services and corresponding providers to attend the needs. SEvol is a method to build an adaptive and evolutionary system based on the MAPE-K methodology
supporting the solution evolution to cope with the elderly's new life stages.
To demonstrate the feasibility, utility and applicability of SCoPE and SEvol,
a number of methods and algorithms are presented, and illustrative scenarios are
introduced in which {service, provider} pairs are ranked based on a
multidimensional assessment method. Composition strategies are based on
customer’s profile and requirements, and the evolutionary solution is
determined considering customer’s inputs and evolution plans.
For the ECE evaluation process the following steps are adopted: (i) feature
selection and software prototype development; (ii) detailing the ECE framework
validation based on applicability and utility parameters; (iii) development of a
case study illustrating a typical scenario involving an elderly and her care needs;
and (iv) performing a survey based on a modified version of the technology
acceptance model (TAM), considering three contexts: Technological,
Organizational and Collaborative environment
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