=Paper=
{{Paper
|id=Vol-1751/AICS_2016_paper_15
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1751/AICS_2016_paper_15.pdf
|volume=Vol-1751
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1751/AICS_2016_paper_15.pdf</pdf>
<pre>
       Evaluating the Relative Performance of
    Collaborative Filtering Recommender Systems

       Humberto Jesús Corona Pampın* , Houssem Jerbi** , and Michael P.
                                O’Mahony**
                        *
                         Fashion Insights Centre, Zalando Ireland
           **
                Insight Centre for Data Analytics, University College Dublin



Summary
Past work on the evaluation of recommender systems indicates that collabora-
tive filtering algorithms are accurate and suitable for the top-N recommendation
task. Further, the importance of performance beyond accuracy has been recog-
nised in the literature. Here, we present an evaluation framework based on a set
of accuracy and beyond accuracy metrics, including a novel metric that captures
the uniqueness of a recommendation list. We perform an in-depth evaluation of
three well-known collaborative filtering algorithms using three datasets. The re-
sults show that the user-based and item-based collaborative filtering algorithms
have a high inverse correlation between popularity and diversity and recommend
a common set of items at large neighbourhood sizes. The study also finds that
the matrix factorisation approach leads to more accurate and diverse recommen-
dations, while being less biased toward popularity [1]1 .


References
1. Pampın, H.J.C., Jerbi, H., O’Mahony, M.P.: Evaluating the relative performance of
   collaborative filtering recommender systems. Journal of Universal Computer Science
   21(13), 1849–1868 (2015)




1
    This work was supported by Science Foundation Ireland under Grant Number
    SFI/12/RC/2289 through The Insight Centre for Data Analytics

</pre>