Paper Info
Title
HotDigg: finding recent hot topics from digg
Abstract
The popular news aggregator called Digg is a social news service that lets people share new articles or blog postings in web pages with other users and vote thumbs up and thumbs down on the shared contents. Digg itself provides only the functionality to search the articles for the topics provided by users using manually tagged keywords. Helping users to find the most interesting Digg articles with the current hot topics will be very useful, but it is not an easy task to classify the articles according to their topics and discover the articles with the hot topics quickly. In this paper, we propose HotDigg, a recommendation system to provide the articles with hot topics in Digg using a novel probabilistic generative model suitable for representing the activities in Digg service. We next propose an EM algorithm to learn the parameters of our probabilistic model. Our performance study with real-life data from Digg confirms the effectiveness of HotDigg by showing that the articles with current hot topics are recommended.
Year
DOI
Venue
2012
10.1007/978-3-642-29038-1_30
DASFAA
Keywords
Field
DocType
recent hot topic,probabilistic model,digg service,novel probabilistic generative model,social news service,current hot topic,em algorithm,interesting digg article,blog postings,popular news aggregator,hot topic
Recommender system,Data mining,World Wide Web,Conditional probability distribution,Information retrieval,News aggregator,Web page,Computer science,Expectation–maximization algorithm,Probabilistic generative model,Statistical model,Mixture model
Conference
Citations 
PageRank 
References 
1
0.36
12
Authors
2
Name
Order
Citations
PageRank
Younghoon Kim112.05
Kyuseok Shim25120752.19