Multi-View Data approaches in Recommender Systems: an Overview

Ivan Palomares Carrascosa, Sergey Kovalchuk

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
296 Downloads (Pure)


This paper overviews an assortment of recent research work undertaken on recommender system models based on using multiple views of user and item-related data across the recommendation process. A summary of representative literature on multi-view recommender approaches is provided, describing their main characteristics, such as: their potential to overcome most common shortcomings in conventional recommender systems, as well as the use of data science, learning techniques and aggregation processes to combine information stemming from multiple views. A tabular summary is provided to facilitate the comparison of the similarities and dierences among the surveyed works, along with commonly identified directions for future research in the topic.
Original languageEnglish
Title of host publication6th International Young Scientists Conference in HPC and Simulation, YSC 2017
Subtitle of host publication1-3 November 2017, Kotka, Finland
Number of pages12
Publication statusPublished - 4 Dec 2017
Event6th International Young Scientists Conference in HPC and Simulation: YSC 2017 - Finland, Kotka, Finland
Duration: 1 Nov 20173 Nov 2017

Publication series

NameProcedia Computer Science
ISSN (Print)1877-0509


Conference6th International Young Scientists Conference in HPC and Simulation
Internet address

Bibliographical note

Special issue: 6th International Young Scientist Conference on Computational Science, YSC 2017, 01-03 November 2017, Kotka, Finland


  • Recommender Systems
  • Collaborative Filtering
  • Clustering
  • Multi-View Data
  • Multi-View Recommendation
  • User Similarity
  • User Trust
  • Aggregation Functions


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