582 research outputs found
Empirical Findings On Persuasiveness Of Recommender Systems For Customer Decision Support In Electronic Commerce
More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness
Domino: exploring mobile collaborative software adaptation
Social Proximity Applications (SPAs) are a promising new area for ubicomp software that exploits the everyday changes in the proximity of mobile users. While a number of applications facilitate simple file sharing between co–present users, this paper explores opportunities for recommending and sharing software between users. We describe an architecture that allows the recommendation of new system components from systems with similar histories of use. Software components and usage histories are exchanged between mobile users who are in proximity with each other. We apply this architecture in a mobile strategy game in which players adapt and upgrade their game using components from other players, progressing through the game through sharing tools and history. More broadly, we discuss the general application of this technique as well as the security and privacy challenges to such an approach
Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
Recommender systems are gaining traction in healthcare because they can tailor recommendations
based on users' feedback concerning their appreciation of previous health-related messages. However,
recommender systems are often not grounded in behavioral change theories, which may further increase
the effectiveness of their recommendations. This paper's objective is to describe principles for designing
and developing a health recommender system grounded in the I-Change behavioral change model that
shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon
an existing smoking cessation health recommender system that delivered motivational messages through a
mobile app. A group of experts assessed how the system may be improved to address the behavioral change
determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender
algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages
were designed using 10 health communication methods. The algorithm was designed to match 58 message
characteristics to each user pro le by following the principles of the I-Change model and maintaining the
bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed
to improve the user experience, and this system's design bridges the gap between health recommender
systems and the use of behavioral change theories. This article presents a novel approach integrating
recommender system technology, health behavior technology, and computer-tailored technology. Future
researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112
On hybrid modular recommendation systems for video streaming
The recommendation systems aim to improve the user engagement by recommending
appropriate personalized content to users, exploiting information about their
preferences. We propose the enabler, a hybrid recommendation system which
employs various machine-learning (ML) algorithms for learning an efficient
combination of several recommendation algorithms and selects the best blending
for a given input.Specifically, it integrates three layers, namely, the trainer
which trains the underlying recommenders, the blender which determines the most
efficient combination of the recommenders, and the tester for assessing the
performance of the system. The enabler incorporates a variety of recommendation
algorithms that span from collaborative filtering and content-based techniques
to ones based on neural networks. It uses the nested cross validation for
automatically selecting the best ML algorithm along with its hyper-parameter
values for the given input, according to a specific metric. The enabler can be
easily extended to include other recommenders and blenders. The enabler has
been extensively evaluated in the context of video-streaming. It outperforms
various other algorithms, when tested on the Movielens 1M benchmark
dataset.encouraging results. Moreover For example, it achieves an RMSE of
0.8206, compared to the state-of-the-art performance of the AutoRec and SVD,
0.827 and 0.845, respectively. A pilot web-based recommendation system was
developed and tested in the production environment of a large telecom operator
in Greece. Volunteer customers of the video-streaming service provided by the
telecom operator employed the system in the context of an out-in-the-wild field
study with a post-analysis of the enabler, using the collected ratings of the
pilot, demonstrated that it significantly outperforms several popular
recommendation algorithms
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