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  1. Proceedings of the 2014 Recommender Systems Challenge (RecSysChallenge '14)
  2. Utilizing Voting Systems for Ranking User Tweets
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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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Utilizing Voting Systems for Ranking User Tweets

Content Provider ACM Digital Library
Author Xu, Yue Abdel-Hafez, Ahmad Phung, Quoc Viet
Abstract Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A subproblem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.
Starting Page 23
Ending Page 28
Page Count 6
File Format PDF
ISBN 9781450331883
DOI 10.1145/2668067.2668070
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-10-10
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Baldwin method Weighted borda-count Twitter Greedy feature selection Content-based features Reputation Tweet ranking
Content Type Text
Resource Type Article
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