Paper Info
Title
DIGTOBI: a recommendation system for Digg articles using probabilistic modeling
Abstract
Digg is a social news website that lets people submit articles to share their favorite web pages (e.g. blog postings or news articles) and vote the articles posted by others. Digg service currently lists the articles in the front page by popularity without considering each user's preference to the topics in the articles. Helping users to find the most interesting Digg articles tailored to each user's own interests will be very useful, but it is not an easy task to classify the articles according to their topics in order to recommend the articles differently to each user. In this paper, we propose DIGTOBI, a personalized recommendation system for Digg articles using a novel probabilistic modeling. Our model considers the relevant articles with low Digg scores important as well. We show that our model can handle both warm-start and cold-start scenarios seamlessly through a single model. We next propose an EM algorithm to learn the parameters of our probabilistic model. Our performance study with Digg data confirms the effectiveness of DIGTOBI compared to the traditional recommendations algorithms.
Year
DOI
Venue
2013
10.1145/2488388.2488449
WWW
Keywords
Field
DocType
social news website,single model,low digg score,digg article,novel probabilistic modeling,digg data,recommendation system,news article,interesting digg article,digg service,probabilistic model,collaborative filtering,expectation maximization,topic modeling,probabilistic latent semantic indexing
Recommender system,Data mining,World Wide Web,Collaborative filtering,Information retrieval,Web page,Computer science,Popularity,Statistical model,Probabilistic latent semantic analysis,Topic model,Probabilistic logic
Conference
ISBN
Citations 
PageRank 
978-1-4503-2035-1
5
0.44
References 
Authors
19
3
Name
Order
Citations
PageRank
Younghoon Kim1997.79
Yoonjae Park2773.33
Kyuseok Shim35120752.19