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  1. Proceedings of the 2014 Recommender Systems Challenge (RecSysChallenge '14)
  2. How popular are your 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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How popular are your tweets?

Content Provider ACM Digital Library
Author Ravindran, Balaraman Saha, Avijit Rajendran, Janarthanan Shekhar, Shubhranshu
Abstract Evaluation is a key factor to reflect the quality of a recommender system algorithm. Traditional recommenders pose the problem as an optimization task where they seek to minimize the error in predicted rating for an item or predicted top-n items of interest with respect a user. However, these predictions do not often translate to a well-perceived recommendation. In this work, instead of the typical rating prediction task, we predict the amount of interaction an item would receive through a social network. In particular, we propose a simple and efficient model to generate a ranked list of tweets of a user in the order of expected user interaction that they would receive on Twitter, which is expressed in terms of retweets and favorites. We evaluate our proposed model on an extended version of the MovieTweetings dataset, which contains tweets that are generated when users rate movies on IMDb (using the IMDb iOS app), and show that the proposed model performs better compared to the baselines.
Starting Page 66
Ending Page 69
Page Count 4
File Format PDF
ISBN 9781450331883
DOI 10.1145/2668067.2668080
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-10-10
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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