Improving business rating predictions using graph based features

Amit Tiroshi, Shlomo Berkovsky, Mohamed Ali Kaafar, David Vallet, Terence Chen, Tsvi Kuflik

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


Many types of recommender systems rely on a rich ensemble of user, item, and context features when generating recommendations for users. The features can be either manually engineered or automatically extracted from the available data, such that feature engineering becomes an important step in the recommendation process. In this work, we propose to leverage graph based representation of the data in order to generate and automatically populate features. We represent the standard user-item rating matrix and some domain metadata, as graph vertices and edges. Then, we apply a suite of graph theory and network analysis metrics to the graph based data representation, to populate features that augment the original user-item ratings data. The augmented data is fed into a classifier that predicts unknown user ratings, which are used for the generation of recommendations. We evaluate the proposed methodology using the recently released Yelp business ratings dataset. Our results indicate that the automatically populated graph features allow for more accurate and robust predictions, with respect to both the variability and sparsity of ratings.
Original languageEnglish
Title of host publicationProceedings of the 19th international conference on Intelligent User Interfaces
PublisherPubl by ACM
Number of pages10
ISBN (Print)9781450321846
StatePublished - 2014
Event19th International Conference on Intelligent User Interfaces, IUI 2014 - Haifa, Israel
Duration: 24 Feb 201427 Feb 2014

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


Conference19th International Conference on Intelligent User Interfaces, IUI 2014


  • Feature Extraction
  • Graph-Based Recommendations
  • Recommender Systems

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction


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