Among the topics are building recommender systems for detecting network intrusion using intelligent design technologies, novel approaches and case studies in intelligent techniques in recommender systems and contextual advertising, rough web intelligent techniques for page recommendation, modeling clearance sales outshopping behavior using a neural network model, and a hybrid neural genetic architecture.
What does RecSys stand for?
RecSys stands for Recommender Systems (International Conference)
This definition appears frequently and is found in the following Acronym Finder categories:
- Information technology (IT) and computers
- Organizations, NGOs, schools, universities, etc.
- Reconnaissance Satellite
- Reconnaissance Satellite Vulnerability Report System
- Ranhill Engineers and Constructors Sdn. Bhd. (Berhad, Malaysia)
- Receiving Ship
- Reunión Española Sobre Criptología y Seguridad de la Información
- Recreational and Commercial Sea Kayaking Association of South Africa
- Regional Computer Science Postgraduate Conference
- Receiving Station
- Reception Station
- Reception Station Automation Management System
- Randomised Effect-Controlled Trial
- Regional Engineering College, Tiruchirappalli (India)
- Repair Event Cycle Time
- Received for Temporary Additional Duty
- Received for Temporary Additional Duty Under Instructions
- Regional Centre for Training in Aerospace Surveys
- Received for Temporary Duty
Samples in periodicals archive:
In [8, 9] another method is suggested for privacy preserving on centralized recommender systems by adding uncertainty to the data by using a randomized perturbation technique while attempting to make sure that the necessary statistical aggregates such as the mean do not greatly get disturbed.
Over the last decade, research on recommender systems has focused on performance of algorithms for recommendations; and improved ways of building user models to map user preferences, interests and behaviors into suggesting suitable products or services [Burke (2001)].
As Genius is a proprietary system whose secrets are not available to the public, the researchers studied it by testing its song recommendations against comparable song suggestions from experimental music recommender systems that they fully understood.
Representative content-based recommender systems include News Dude (Billsus, & Pazzani, 1999) and WebWatcher (Joachims, Freitag, & Mitchell, 1997).
The technologies involved in recommender systems are information filtering, collaborated filtering, user profiling, machine learning, case-based retrieval, data mining, and similarity-based retrieval.