Told you i didn't like it: Exploiting uninteresting items for effective collaborative filtering

Won Seok Hwang, Juan Parc, Sang Wook Kim, Jongwuk Lee, Dongwon Lee

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

22 Citations (Scopus)

Abstract

We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in experiments are available at: https://goo.gl/KUrmip.

Original languageEnglish (US)
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages349-360
Number of pages12
ISBN (Electronic)9781509020195
DOIs
StatePublished - Jun 22 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: May 16 2016May 20 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Other

Other32nd IEEE International Conference on Data Engineering, ICDE 2016
CountryFinland
CityHelsinki
Period5/16/165/20/16

Fingerprint

Collaborative filtering
Singular value decomposition
Experiments
Rating

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Hwang, W. S., Parc, J., Kim, S. W., Lee, J., & Lee, D. (2016). Told you i didn't like it: Exploiting uninteresting items for effective collaborative filtering. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 349-360). [7498253] (2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498253
Hwang, Won Seok ; Parc, Juan ; Kim, Sang Wook ; Lee, Jongwuk ; Lee, Dongwon. / Told you i didn't like it : Exploiting uninteresting items for effective collaborative filtering. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 349-360 (2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016).
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Hwang, WS, Parc, J, Kim, SW, Lee, J & Lee, D 2016, Told you i didn't like it: Exploiting uninteresting items for effective collaborative filtering. in 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016., 7498253, 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 349-360, 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, 5/16/16. https://doi.org/10.1109/ICDE.2016.7498253

Told you i didn't like it : Exploiting uninteresting items for effective collaborative filtering. / Hwang, Won Seok; Parc, Juan; Kim, Sang Wook; Lee, Jongwuk; Lee, Dongwon.

2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 349-360 7498253 (2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016).

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

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AB - We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in experiments are available at: https://goo.gl/KUrmip.

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Hwang WS, Parc J, Kim SW, Lee J, Lee D. Told you i didn't like it: Exploiting uninteresting items for effective collaborative filtering. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 349-360. 7498253. (2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016). https://doi.org/10.1109/ICDE.2016.7498253