  Collaborative Filtering (CF) is a core component of popular web-based
services such as Amazon, YouTube, Netflix, and Twitter. Most applications use
CF to recommend a small set of items to the user. For instance, YouTube
presents to a user a list of top-n videos she would likely watch next based on
her rating and viewing history. Current methods of CF evaluation have been
focused on assessing the quality of a predicted rating or the ranking
performance for top-n recommended items. However, restricting the recommender
system evaluation to these two aspects is rather limiting and neglects other
dimensions that could better characterize a well-perceived recommendation. In
this paper, instead of optimizing rating or top-n recommendation, we focus on
the task of predicting which items generate the highest user engagement. In
particular, we use Twitter as our testbed and cast the problem as a
Collaborative Ranking task where the rich features extracted from the metadata
of the tweets help to complement the transaction information limited to user
ids, item ids, ratings and timestamps. We learn a scoring function that
directly optimizes the user engagement in terms of nDCG@10 on the predicted
ranking. Experiments conducted on an extended version of the MovieTweetings
dataset, released as part of the RecSys Challenge 2014, show the effectiveness
of our approach.
