A Hybrid Recommendation System Based on Density-Based Clustering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10750)


Collaborative filtering recommenders leverage past user-item ratings in order to predict ratings for new items. One of the most critical steps in such methods corresponds to the formation of the neighbourhood that contains the most similar users or items, so that the ratings associated with them can be employed for predicting new ratings. This work proposes to perform the combination of content-based and ratings-based evidence during the neighbourhood formation step and thus identify the most similar neighbours in a hybrid manner. To this end, DBSCAN, a density-based clustering approach, is applied for identifying the most similar users or items by considering the ratings-based and the content-based similarities, both individually and in combination. The resulting hybrid cluster-based CF recommendation scheme is then evaluated on the latest small MovieLens100k dataset and the experimental results indicate the potential of the proposed approach.


Collaborative filtering Neighbourhood formation Hybrid recommender systems Clustering DBSCAN MovieLens100k 



This work was partially supported by the European Commission by the PROFIT project (H2020-687895).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Information Technologies InstituteCentre for Research and Technology HellasThessalonikiGreece

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