GOCCF: Graph-theoretic one-class collaborative filtering based on uninteresting items

Yeon Chang Lee, Sang Wook Kim, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We investigate how to address the shortcomings of the popular One-Class Collaborative Filtering (OCCF) methods in handling challenging “sparse” dataset in one-class setting (e.g., clicked or bookmarked), and propose a novel graph-theoretic OCCF approach, named as gOCCF, by exploiting both positive preferences (derived from rated items) as well as negative preferences (derived from unrated items). In capturing both positive and negative preferences as a bipartite graph, further, we apply the graph shattering theory to determine the right amount of negative preferences to use. Then, we develop a suite of novel graph-based OCCF methods based on the random walk with restart and belief propagation methods. Through extensive experiments using 3 real-life datasets, we show that our gOCCF effectively addresses the sparsity challenge and significantly outperforms all of 8 competing methods in accuracy on very sparse datasets while providing comparable accuracy to the best performing OCCF methods on less sparse datasets. The datasets and implementations used in the empirical validation are available for access: https://goo.gl/sfiawn.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages3448-3456
Number of pages9
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

Fingerprint

Collaborative filtering
Graph theory
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Lee, Y. C., Kim, S. W., & Lee, D. (2018). GOCCF: Graph-theoretic one-class collaborative filtering based on uninteresting items. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 3448-3456). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.
Lee, Yeon Chang ; Kim, Sang Wook ; Lee, Dongwon. / GOCCF : Graph-theoretic one-class collaborative filtering based on uninteresting items. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. pp. 3448-3456 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).
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Lee, YC, Kim, SW & Lee, D 2018, GOCCF: Graph-theoretic one-class collaborative filtering based on uninteresting items. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI press, pp. 3448-3456, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, United States, 2/2/18.

GOCCF : Graph-theoretic one-class collaborative filtering based on uninteresting items. / Lee, Yeon Chang; Kim, Sang Wook; Lee, Dongwon.

32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 3448-3456 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lee YC, Kim SW, Lee D. GOCCF: Graph-theoretic one-class collaborative filtering based on uninteresting items. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press. 2018. p. 3448-3456. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).