Regression and Learning to Rank Aggregation for User Engagement Evaluation

TL;DR

This paper explores ranking user engagement in social media posts, specifically tweets about movies, using regression and learning to rank methods, and proposes an aggregation approach to enhance ranking accuracy.

Contribution

It introduces an aggregation method combining regression and learning to rank techniques for improved user engagement prediction.

Findings

Learning to rank outperforms most regression models.

Aggregating methods significantly improves performance.

Features from user, movie, and tweet data are all essential.

Abstract

User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain. For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.