94,706 research outputs found
CASP-DM: Context Aware Standard Process for Data Mining
We propose an extension of the Cross Industry Standard Process for Data
Mining (CRISPDM) which addresses specific challenges of machine learning and
data mining for context and model reuse handling. This new general
context-aware process model is mapped with CRISP-DM reference model proposing
some new or enhanced outputs
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Building an Expert System for Evaluation of Commercial Cloud Services
Commercial Cloud services have been increasingly supplied to customers in
industry. To facilitate customers' decision makings like cost-benefit analysis
or Cloud provider selection, evaluation of those Cloud services are becoming
more and more crucial. However, compared with evaluation of traditional
computing systems, more challenges will inevitably appear when evaluating
rapidly-changing and user-uncontrollable commercial Cloud services. This paper
proposes an expert system for Cloud evaluation that addresses emerging
evaluation challenges in the context of Cloud Computing. Based on the knowledge
and data accumulated by exploring the existing evaluation work, this expert
system has been conceptually validated to be able to give suggestions and
guidelines for implementing new evaluation experiments. As such, users can
conveniently obtain evaluation experiences by using this expert system, which
is essentially able to make existing efforts in Cloud services evaluation
reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and
Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24,
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