@inproceedings{5dd0c6a4c8fe4a01b06876e559a5d1b2,
title = "Elver: Recommending Facebook pages in cold start situation without content features",
abstract = "Recommender systems are vital to the success of online retailers and content providers. One particular challenge in recommender systems is the 'cold start' problem. The word 'cold' refers to the items that are not yet rated by any user or the users who have not yet rated any items. We propose Elver to recommend and optimize page-interest targeting on Facebook. Existing techniques for cold recommendation mostly rely on content features in the event of lacking user ratings. Since it is very hard to construct universally meaningful features for the millions of Facebook pages, Elver makes minimal assumption of content features. Elver employs iterative matrix completion technology and nonnegative factorization procedure to work with meagre content inklings. Experiments on Facebook data shows the effectiveness of Elver at different levels of sparsity.",
keywords = "Behavioral targeting, Facebook, Recommender system, Social media, Sparse matrix",
author = "Yusheng Xie and Zhengzhang Chen and Kunpeng Zhang and Chen Jin and Yu Cheng and Ankit Agrawal and Alok Choudhary",
year = "2013",
doi = "10.1109/BigData.2013.6691609",
language = "English (US)",
isbn = "9781479912926",
series = "Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013",
publisher = "IEEE Computer Society",
pages = "475--479",
booktitle = "Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013",
address = "United States",
note = "2013 IEEE International Conference on Big Data, Big Data 2013 ; Conference date: 06-10-2013 Through 09-10-2013",
}