Elver: Recommending Facebook pages in cold start situation without content features

Yusheng Xie, Zhengzhang Chen, Kunpeng Zhang, Chen Jin, Yu Cheng, Ankit Agrawal, Alok Choudhary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages475-479
Number of pages5
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Other

Other2013 IEEE International Conference on Big Data, Big Data 2013
CountryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

Keywords

  • Behavioral targeting
  • Facebook
  • Recommender system
  • Social media
  • Sparse matrix

ASJC Scopus subject areas

  • Software

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