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  1. Proceedings of the 2014 Recommender Systems Challenge (RecSysChallenge '14)
  2. Recommender System Based on Modularity
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An analysis of the 2014 RecSys Challenge
User Engagement as Evaluation: a Ranking or a Regression Problem?
RecSys Challenge 2014: an ensemble of binary classifiers and matrix factorization
Ranking approach to RecSys Challenge
Utilizing Voting Systems for Ranking User Tweets
Regression and Learning to Rank Aggregation for User Engagement Evaluation
A Two Step Ranking Solution for Twitter User Engagement
Predicting User Engagement in Twitter with Collaborative Ranking
Exploring Tweet Engagement in the RecSys 2014 Data Challenge
A Recommender System for Predicting User Engagement in Twitter
Recommender System Based on Modularity
Online Tweet Recommendation Using Extreme Learning Machine
How popular are your tweets?

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Recommender System Based on Modularity

Content Provider ACM Digital Library
Author Ren, Tsang Ing de Carvalho, Tiago B. A. Sibaldo, Maria A. A. Cavalcanti, George
Abstract This paper presents a solution for the RecSys Challenge 2014 by performing a clustering in a bipartite graph whose vertices are of two types: user and item, having the edges as the engagement given to a tweet. The Modularity metric, which is well-known in the area of complex networks to quantify how good the groups of nodes are defined in the network, was used to form the groups that contain users and movies that are closely related. The resulting groups have members of both classes of vertices. From this clustering, a nearest neighbor technique was applied to rank the tweets by the engagement.
Starting Page 58
Ending Page 61
Page Count 4
File Format PDF
ISBN 9781450331883
DOI 10.1145/2668067.2668079
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-10-10
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Graph clustering Bipartite graph Modularity Recommender systems
Content Type Text
Resource Type Article
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