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Published in: The Journal of Supercomputing 7/2021

04-01-2021

Exploiting uninteresting items for effective graph-based one-class collaborative filtering

Authors: Yeon-Chang Lee, Jiwon Son, Taeho Kim, Daeyoung Park, Sang-Wook Kim

Published in: The Journal of Supercomputing | Issue 7/2021

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Abstract

The goal of recommender systems is to identify the items appealing to a target user by analyzing her/his past preferences. Collaborative filtering is one of the most popular recommendation methods that use the similarity between users’ past behaviors such as explicit user ratings (i.e., multi-class setting) or implicit click logs (i.e., one-class setting). Graph-theoretic one-class collaborative filtering (gOCCF) has been successful in dealing with sparse datasets in one-class settings (e.g., clicked or bookmarked). In this paper, we point out the problem that gOCCF requires long processing time compared to existing OCCF methods. To overcome the limitation of the original gOCCF, we propose a new gOCCF approach based on signed random walk with restart (SRWR). Using SRWR, the proposed approach accurately and efficiently captures users’ preferences by analyzing not only the positive preferences from rated items but also the negative preferences from uninteresting items. We also perform an in-depth analysis to further understand the effect of employing uninteresting items in OCCF. Toward this end, we employ the following well-known graph properties: (1) effective diameter, (2) number of reachable pairs, (3) number of nodes in the largest connected component, (4) clustering coefficient, (5) singular values, and (6) signed butterfly. From this comprehensive analysis, we demonstrate that signed graphs with uninteresting items have properties similar to real-life signed graphs. Lastly, through extensive experiments using real-life datasets, we verify that the proposed approach improves the accuracy and decreases the processing time of the original gOCCF.

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Footnotes
1
The initial idea of this paper was introduced with some preliminary results of evaluation at IEEE BigComp 2020 as a short paper of four pages (i.e., conference version) [25]. This paper is its extended version written for the archival purpose in a journal.
 
2
Note that the same trend was observed as well in other datasets (i.e., MovieLens 100K, Watcha, CiteULike, and Lastfm).
 
3
The same trend was also observed in other datasets (i.e., MovieLens 100K, Watcha, CiteULike, and Lastfm).
 
4
We omit the results for other top-Ns (=20, 50), because they showed tendency very similar to that in Fig. 6.
 
5
We omit the results for other top-Ns (=20, 50) because they showed tendency similar to that in Fig. 10.
 
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Metadata
Title
Exploiting uninteresting items for effective graph-based one-class collaborative filtering
Authors
Yeon-Chang Lee
Jiwon Son
Taeho Kim
Daeyoung Park
Sang-Wook Kim
Publication date
04-01-2021
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 7/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03573-8

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