Recommending tumblr blogs to follow with inductive matrix completion

Donghyuk Shin, Suleyman Cetintas, Kuang Chih Lee

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

In microblogging sites, recommending blogs (users) to follow is one of the core tasks for enhancing user experience. In this paper, we propose a novel inductive matrix completion based blog recommendation method to effectively utilize multiple rich sources of evidence such as the social network and the content as well as the activity data from users and blogs. Experiments on a large-scale real-world dataset from Tumblr show the effectiveness of the proposed blog recommendation method.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume1247
StatePublished - 2014
Externally publishedYes
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: Oct 6 2014Oct 10 2014

Keywords

  • Blog recommendation
  • Inductive matrix completion
  • SVD

ASJC Scopus subject areas

  • Computer Science(all)

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