Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2668067.2668072 | Proceedings of the 2014 Recommender Systems Challenge |
Keywords | DocType | Volume |
algorithms,experimentation,information filtering,recommender systems,parameter learning,measurement,user engagement,twitter,learning to rank | Journal | abs/1412.7990 |
ISSN | Citations | PageRank |
In Proceedings of the 2014 Recommender Systems Challenge
(RecSysChallenge'14). ACM, New York, NY, USA, , Pages 41 , 6 pages | 3 | 0.40 |
References | Authors | |
16 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ernesto Diaz-Aviles | 1 | 228 | 20.08 |
| Hoang Thanh Lam | 2 | 108 | 8.49 |
| Fabio Pinelli | 3 | 972 | 50.96 |
| Stefano Braghin | 4 | 8 | 2.56 |
| Yiannis Gkoufas | 5 | 17 | 5.96 |
| Michele Berlingerio | 6 | 510 | 28.92 |
| Francesco Calabrese | 7 | 6 | 1.16 |