Social content sites [3], 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.
[1]
Latha S. Colby.
A recursive algebra and query optimization for nested relations
,
1989,
SIGMOD '89.
[2]
Michael I. Jordan,et al.
Latent Dirichlet Allocation
,
2001,
J. Mach. Learn. Res..
[3]
Cong Yu,et al.
SocialScope: Enabling Information Discovery on Social Content Sites
,
2009,
CIDR.
[4]
Rakesh Agarwal,et al.
Fast Algorithms for Mining Association Rules
,
1994,
VLDB 1994.
[5]
John Riedl,et al.
Explaining collaborative filtering recommendations
,
2000,
CSCW '00.
[6]
Joseph A. Konstan,et al.
Introduction to recommender systems
,
2008,
SIGMOD Conference.
[7]
Li Chen,et al.
Trust building with explanation interfaces
,
2006,
IUI '06.
[8]
Judith Masthoff,et al.
Effective explanations of recommendations: user-centered design
,
2007,
RecSys '07.
[9]
Thomas L. Griffiths,et al.
Hierarchical Topic Models and the Nested Chinese Restaurant Process
,
2003,
NIPS.
[10]
Raymond J. Mooney,et al.
Explaining Recommendations: Satisfaction vs. Promotion
,
2005
.
[11]
Ravi Kumar,et al.
Pig latin: a not-so-foreign language for data processing
,
2008,
SIGMOD Conference.
[12]
Gediminas Adomavicius,et al.
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
,
2005,
IEEE Transactions on Knowledge and Data Engineering.