Program
08.30-09.15
Keynote:
Cong Yu and
Sihem Amer-Yahia
- Yahoo! Research [talk]
09.20-09.50 "SoQL: A Language
for Querying and Creating Data in
Social Networks" (Royi
Ronen, Oded Shmueli ) [talk]
09.50-10.20 Coffee break
10.20-10.40 "Social Streams Blog
Crawler" (Alexey Maykov, Matthew
Hurst) [talk]
10.40-11.00 "On Protecting
Private Information in Social Networks: A
Proposal" (Bo Luo,
Dongwon Lee) [talk]
11.00-11.20
"Contents-based Analysis of Community Formation and
Evolution" (Seok-Chul Baek, Sukwon Kang, Hyung Noh,
Sang-Wook Kim) [talk]
11.20-11.40 "Actively building
private recommender networks for
evolving reliable
relationships" (Ira Assent) [talk]
11.40-12.30 Keynote:
Professor Edward Y. Chang - Director of
Research, Google China [talk]
Keynotes
Cong Yu and Sihem Amer-Yahia: Jelly: A Language for Building Community-Centric Information Exploration Applications
Social content sites, which integrate
traditional content sites (e.g., Yahoo! Travel) with
social network features, have recently emerged as a
significant new trend on the Web. Users on those sites
share content and form various communities based on
explicit friendships or shared interests. However, the
existing information exploration mechanisms rarely
leverage the rich community structure. In this work, we
aim to unlock the value of social content sites by
helping developers specify community-based information
exploration strategies in a flexible and declarative
way. Our solution makes use of two key notions, topics
and communities, in order to identify socially and
semantically relevant information for users.
Specifically, we propose JELLY as a language for
developing community-centric information exploration
applications. JELLY provides several primitives which
exploit both content and user behavior in social content
sites in order to help users explore relevant content.
The topic generation primitive is used to extract topics
from tags. The community extraction primitive enables
building different user communities. The information
discovery primitive helps customize content relevance by
combining a user’s query and profile, as well as
insights from related communities.
Finally, the information explanation primitive offers
valuable
social provenance to help users better understand the
returned
content. We describe JELLY’s data model and language,
and its
application to building a system for finding socially
relevant travel
destinations in Yahoo! Travel.
Edward Y. Chang : Parallel Algorithms for Mining Large-Scale Data
In this talk, I will first describe both computational and storage challenges to traditional data mining algorithms brought about by information explosion. To deal with huge amount of data that expand continuously, an effective algorithm should be designed to 1) run on thousands of parallel machines for sharing storage and speeding up computation, 2) perform incremental retraining and updates for attaining online performance, and 3) fuse information from multiple sources in order to alleviate information sparseness. I will present algorithms we recently developed including parallel PF-Growth, parallel combinational collaborative filtering, parallel LDA, parallel spectral clustering, and parallel Support Vector Machines.