Utilizing Voting Systems for Ranking User Tweets

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.

Item Reputation-Aware Recommender Systems

“…It is because they will collect some votes from one or more of the candidates on top which possibly will affect their ranking. The Baldwin method is used to eliminate this dilemma by applying a recursive BC method [25]. First they calculate BC votes, then the candidate with the least number of votes is removed and the BC is calculated again without the removed candidate as he never existed.…”