Tumblr blog recommendation with boosted inductive matrix completion

Donghyuk Shin, Suleyman Cetintas, Kuang Chih Lee, Inderjit S. Dhillon

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

26 Scopus citations

Abstract

Popular microblogging sites such as Tumblr have attracted hundreds of millions of users as a content sharing platform, where users can create rich content in the form of posts that are shared with other users who follow them. Due to the sheer amount of posts created on such services, an important task is to make quality recommendations of blogs for users to follow. Apart from traditional recommender system settings where the follower graph is the main data source, additional side-information of users and blogs such as user activity (e.g., like and reblog) and rich content (e.g., text and images) are also available to be exploited for enhanced recommendation performance. In this paper, we propose a novel boosted inductive matrix completion method (BIMC) for blog recommendation. BIMC is an additive low-rank model for user-blog preferences consisting of two components; one component captures the low-rank structure of follow relationships and the other captures the latent structure using side-information. Our model formulation combines the power of the recently proposed inductive matrix completion (IMC) model (for side-information) together with a standard matrix completion (MC) model (for low-rank structure). Furthermore, we utilize recently developed deep learning techniques to obtain semantically rich feature representations of text and images that are incorporated in BIMC. Experiments on a large-scale real-world dataset from Tumblr illustrate the effectiveness of the proposed BIMC method.

Original languageEnglish (US)
Title of host publicationCIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages203-212
Number of pages10
ISBN (Electronic)9781450337946
DOIs
StatePublished - Oct 17 2015
Externally publishedYes
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume19-23-Oct-2015

Conference

Conference24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

Keywords

  • Blog recommendation
  • Deep learning features
  • Inductive matrix completion

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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