Paper Info
Title
NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites
Abstract
Question-and-answer (Q&A) websites, such as Yahoo! Answers, Stack Overflow and Quora, have become a popular and powerful platform for Web users to share knowledge on a wide range of subjects. This has led to a rapidly growing volume of information and the consequent challenge of readily identifying high quality objects (questions, answers and users) in Q&A sites. Exploring the interdependent relationships among different types of objects can help find high quality objects in Q&A sites more accurately. In this paper, we specifically focus on the ranking problem of co-ranking questions, answers and users in a Q&A website. By studying the tightly connected relationships between Q&A objects, we can gain useful insights toward solving the co-ranking problem. However, co-ranking multiple objects in Q&A sites is a challenging task: a) With the large volumes of data in Q&A sites, it is important to design a model that can scale well; b) The large-scale Q&A data makes extracting supervised information very expensive. In order to address these issues, we propose an unsupervised Network-based Co-Ranking framework (NCR) to rank multiple objects in Q&A sites. Empirical studies on real-world Yahoo! Answers datasets demonstrate the effectiveness and the efficiency of the proposed NCR method.
Year
DOI
Venue
2014
10.1145/2661829.2661978
CIKM
Keywords
Field
DocType
q&a networks,co-ranking,unsupervise,interrelationships,data mining
Data mining,Information retrieval,Ranking,Computer science,Artificial intelligence,Machine learning,Empirical research,Scalability
Conference
Citations 
PageRank 
References 
10
0.49
31
Authors
5
Name
Order
Citations
PageRank
Jingyuan Zhang1605.74
Xiangnan Kong2105957.66
Roger Jie Luo3101.17
Yi Chang4146386.17
Philip S. Yu5306703474.16