Authors:
Aleksandra Karpus
;
Marta Raczyńska
and
Adam Przybylek
Affiliation:
Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk and Poland
Keyword(s):
Recommender Systems, Collaborative Filtering, Similarity Measure, Neighborhood Size.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative Filtering
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Recommender Systems aim at suggesting potentially interesting items to a user. The most common kind of Recommender Systems is Collaborative Filtering which follows an intuition that users who liked the same things in the past, are more likely to be interested in the same things in the future. One of Collaborative Filtering methods is the k Nearest Neighbors algorithm which finds k users who are the most similar to an active user and then it computes recommendations based on the subset of users. The main aim of this paper is to compare two implementations of k Nearest Neighbors algorithm, i.e. from Mahout and LensKit libraries, as well as six similarity measures. We investigate how implementation differences between libraries influence optimal neighborhood size k and prediction error. We also show that measures like F1-score and nDCG are not always a good choice for choosing the best neighborhood size k. Finally, we compare different similarity measures according to the average time o
f generating recommendations and the prediction error.
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