An Empirical Study of a Large Scale Online Recommendation System

Author(s):  
Huazheng Fu ◽  
Kang Chen ◽  
Jianbing Ding
2012 ◽  
Vol 7 (2) ◽  
pp. 676-690 ◽  
Author(s):  
Seungwon Shin ◽  
Guofei Gu ◽  
Narasimha Reddy ◽  
Christopher P. Lee
Keyword(s):  

Author(s):  
Xin (Shane) Wang ◽  
Shijie Lu ◽  
X I Li ◽  
Mansur Khamitov ◽  
Neil Bendle

Abstract Persuasion success is often related to hard-to-measure characteristics, such as the way the persuader speaks. To examine how vocal tones impact persuasion in an online appeal, this research measures persuaders’ vocal tones in Kickstarter video pitches using novel audio mining technology. Connecting vocal tone dimensions with real-world funding outcomes offers insight into the impact of vocal tones on receivers’ actions. The core hypothesis of this paper is that a successful persuasion attempt is associated with vocal tones denoting (1) focus, (2) low stress, and (3) stable emotions. These three vocal tone dimensions—which are in line with the stereotype content model—matter because they allow receivers to make inferences about a persuader’s competence. The hypotheses are tested with a large-scale empirical study using Kickstarter data, which is then replicated in a different category. In addition, two controlled experiments provide evidence that perceptions of competence mediate the impact of the three vocal tones on persuasion attempt success. The results identify key indicators of persuasion attempt success and suggest a greater role for audio mining in academic consumer research.


Author(s):  
Selvi C ◽  
Keerthana D

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi’s historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.


Author(s):  
Emanuele Iannone ◽  
Roberta Guadagni ◽  
Filomena Ferrucci ◽  
Andrea De Lucia ◽  
Fabio Palomba

2016 ◽  
Vol 25 (01) ◽  
pp. 184-187
Author(s):  
J. Charlet ◽  
L. F. Soualmia ◽  

Summary Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering. Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.


1998 ◽  
Vol 16 (1) ◽  
pp. 119-134 ◽  
Author(s):  
Carol L. Krumhansl

This study examines possible parallels between large-scale organization in music and discourse structure. Two experiments examine the psychological reality of topics in the first movements of W. A. Mozart's String Quintet No. 3 in C major, K. 515, and L. van Beethoven's String Quartet No. 15 in A minor, Op. 132. Listeners made real-time judgments on three continuous scales: memorability, openness, and amount of emotion. All three kinds of judgments could be accounted for by the topics identified in these pieces by Agawu (1991) independently of the listeners' musical training. The results showed hierarchies of topics. However, these differed for the three tasks and for the two pieces. The topics in the Mozart piece appear to function as a way of establishing the musical form, whereas the topics in the Beethoven piece are more strongly associated with emotional content.


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