INTIMATE: a Web-based movie recommender using text categorization

Author(s):  
H. Mak ◽  
I. Koprinska ◽  
J. Poon
2002 ◽  
Vol 11 (03) ◽  
pp. 389-423 ◽  
Author(s):  
ROBERTO BASILI ◽  
ALESSANDRO MOSCHITTI

Information Retrieval (IR) and NLP-driven Information Extraction (IE) are complementary activities. IR helps in locating specific documents within a huge search space (localization) while IE supports the localization of specific information within a document (extraction or explanation). In application scenarios both capabilities are usually needed. IE is important here, as it can enrich the IR inferences with motivating information. Works on Web-based IR suggest that embedding linguistic information (e.g. sense distinctions) at a suitable level within traditional quantitative approaches (e.g. query expansion as in [26]) is a promising approach. "Which linguistic level is best suited to which IR mechanism" is the interesting representational problem posed by the current research stage. This is also the central concern of this paper. A traditional method for efficient text categorization is here presented. Original features of the proposed model are a self-adapting parameterized weighting model and the use of linguistic information. The key idea is the integration of NLP methods within a robust and efficient TC framework. This allows to combine benefits of large scale and efficient IR with the richer expressivity closer to IE. In this paper we capitalize the systematic benchmarking resources available in TC to extensively derive empirical evidence about the above representational problem. The positive experimental results confirm that the proposed TC framework characterizes as a viable approach to intelligent text categorization on a large scale.


1998 ◽  
Vol 62 (9) ◽  
pp. 671-674
Author(s):  
JF Chaves ◽  
JA Chaves ◽  
MS Lantz
Keyword(s):  

2013 ◽  
Vol 23 (3) ◽  
pp. 82-87 ◽  
Author(s):  
Eva van Leer

Mobile tools are increasingly available to help individuals monitor their progress toward health behavior goals. Commonly known commercial products for health and fitness self-monitoring include wearable devices such as the Fitbit© and Nike + Pedometer© that work independently or in conjunction with mobile platforms (e.g., smartphones, media players) as well as web-based interfaces. These tools track and graph exercise behavior, provide motivational messages, offer health-related information, and allow users to share their accomplishments via social media. Approximately 2 million software programs or “apps” have been designed for mobile platforms (Pure Oxygen Mobile, 2013), many of which are health-related. The development of mobile health devices and applications is advancing so quickly that the Food and Drug Administration issued a Guidance statement with the purpose of defining mobile medical applications and describing a tailored approach to their regulation.


2008 ◽  
Vol 41 (8) ◽  
pp. 23
Author(s):  
MITCHEL L. ZOLER
Keyword(s):  

2009 ◽  
Vol 42 (19) ◽  
pp. 27
Author(s):  
BRUCE JANCIN
Keyword(s):  

GeroPsych ◽  
2013 ◽  
Vol 26 (4) ◽  
pp. 233-241 ◽  
Author(s):  
Pär Bjälkebring ◽  
Daniel Västfjäll ◽  
Boo Johansson

Regret and regret regulation were studied using a weeklong web-based diary method. 108 participants aged 19 to 89 years reported regret for a decision made and a decision to be made. They also reported the extent to which they used strategies to prevent or regulate decision regret. Older adults reported both less experienced and anticipated regret compared to younger adults. The lower level of experienced regret in older adults was mediated by reappraisal of the decision. The lower level of anticipated regret was mediated by delaying the decision, and expecting regret in older adults. It is suggested that the lower level of regret observed in older adults is partly explained by regret prevention and regulation strategies.


Sign in / Sign up

Export Citation Format

Share Document