Paper Info
Title
Towards real-time collaborative filtering for big fast data
Abstract
The Web of people is highly dynamic and the life experiences between our on-line and \"real-world\" interactions are increasingly interconnected. For example, users engaged in the Social Web more and more rely upon continuous social streams for real-time access to information and fresh knowledge about current affairs. However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time. Having Twitter as test bed, we tackle this information overload problem by following an online collaborative approach. That is, we go beyond the general perspective of information finding in Twitter, that asks: \"What is happening right now?\", towards an individual user perspective, and ask: \"What is interesting to me right now within the social media stream?\". In this paper, we review our recently proposed online collaborative filtering algorithms and outline potential research directions.
Year
DOI
Venue
2013
10.1145/2487788.2488044
WWW (Companion Volume)
Keywords
Field
DocType
continuous social stream,towards real-time collaborative,online collaborative,online collaborative approach,big fast data,information overload problem,social web,general perspective,social media stream,individual user perspective,right time,information finding,collaborative filtering
Data science,Data mining,World Wide Web,Information overload,Ask price,Social media,Collaborative filtering,Social web,Ranking,Computer science,Information finding,Access to information
Conference
ISBN
Citations 
PageRank 
978-1-4503-2038-2
2
0.40
References 
Authors
6
4
Name
Order
Citations
PageRank
Ernesto Diaz-Aviles122820.08
Wolfgang Nejdl26633556.13
Lucas Drumond339524.27
Lars Schmidt-Thieme43802216.58