A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems

Date

2020-01-01

Author

Sagdic, A.
Tekinbaş, Cihad
ARSLAN, ENES
Kucukyilmaz, T.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Memory-based classification techniques are commonly used for modeling recommendation problems. They rely on the intuition that similar users and/or items behave similarly, facilitating user-toitem, item-to-item, or user-to-user proximities. A significant drawback of memory-based classification techniques is that they perform poorly with large scale data. Thus, using the off-the-shelf classification techniques for recommendation problems generally lead to impractical computational costs.

Subject Keywords

Recommendation Systems, Collaborative Filtering, Memory Based Classification, Recommendation Performance

URI

https://hdl.handle.net/11511/99211

DOI

https://doi.org/10.23919/mipro48935.2020.9245195

Conference Name

43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Sagdic, C. Tekinbaş, E. ARSLAN, and T. Kucukyilmaz, “A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems,” presented at the 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Hırvatistan, 2020, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/99211.