Improving Cold-Start Recommendations with Social-Media Trends and Reputations

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings, it becomes difficult to relate it to other items or users. In this paper, we address the cold-start problem and propose to leverage on social-media trends and reputations to improve the recommendation of new items. The proposed framework models the long-term reputation of actors and directors, to better characterize new movies. In addition, movies popularity are deduced from social-media trends that are related to the corresponding new movie. A principled method is then applied to infer cold-start recommendations from these social-media signals. Experiments on a realistic time-frame, covering several movie-awards events between January 2014 and March 2014, showed significant improvements over ratings-only and metadata-only based recommendations.

Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XVI - 16th International Symposium, IDA 2017, Proceedings
EditorsNiall Adams, Allan Tucker, David Weston
Place of PublicationCham
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)978-3-319-68765-0
ISBN (Print)978-3-319-68764-3
Publication statusPublished - 1 Jan 2017
Event16th International Symposium on Intelligent Data Analysis, IDA 2017 - London, United Kingdom
Duration: 26 Oct 201728 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Volume10584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Intelligent Data Analysis, IDA 2017
Country/TerritoryUnited Kingdom


  • Cold-start
  • Online reputation
  • Recommendation
  • Sentiment analysis
  • Social-media


Dive into the research topics of 'Improving Cold-Start Recommendations with Social-Media Trends and Reputations'. Together they form a unique fingerprint.

Cite this