Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items

Seiki Miyamoto; Takumi Zamami; Hayato Yamana

doi:10.1109/BigData.2018.8622314

Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items

Seiki Miyamoto, Takumi Zamami, Hayato Yamana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Recommender systems have been used for analyzing users' preference through their past activities and recommend items in which they might be interested in. There are numerous research on improving the accuracy of recommendation being conducted, so the recommender system reads user preference more accurately. However, it is important to consider the recommendation diversity, because lacking diversity will lead to recommendation being repetitive and obvious. In this paper, we propose a method to re-rank the recommendation list by appearance frequency of items to recommend more range of items. The experimental result shows that our method consistently performs better than a related work to improve recommendation diversity.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
Editors	Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5392-5394
Number of pages	3
ISBN (Electronic)	9781538650356
DOIs	https://doi.org/10.1109/BigData.2018.8622314
Publication status	Published - 2019 Jan 22
Event	2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: 2018 Dec 10 → 2018 Dec 13

Publication series

Name	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference	2018 IEEE International Conference on Big Data, Big Data 2018
Country	United States
City	Seattle
Period	18/12/10 → 18/12/13

Fingerprint

Keywords

collaborative filtering
diversity
recommender system

ASJC Scopus subject areas

Computer Science Applications
Information Systems

Cite this

Miyamoto, S., Zamami, T., & Yamana, H. (2019). Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, & X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 5392-5394). [8622314] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622314

Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. / Miyamoto, Seiki; Zamami, Takumi; Yamana, Hayato.

Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5392-5394 8622314 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Miyamoto, S, Zamami, T & Yamana, H 2019, Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622314, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 5392-5394, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 18/12/10. https://doi.org/10.1109/BigData.2018.8622314

Miyamoto S, Zamami T, Yamana H. Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. In Song Y, Liu B, Lee K, Abe N, Pu C, Qiao M, Ahmed N, Kossmann D, Saltz J, Tang J, He J, Liu H, Hu X, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5392-5394. 8622314. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). https://doi.org/10.1109/BigData.2018.8622314

Miyamoto, Seiki ; Zamami, Takumi ; Yamana, Hayato. / Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Yang Song ; Bing Liu ; Kisung Lee ; Naoki Abe ; Calton Pu ; Mu Qiao ; Nesreen Ahmed ; Donald Kossmann ; Jeffrey Saltz ; Jiliang Tang ; Jingrui He ; Huan Liu ; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5392-5394 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

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Access to Document

10.1109/BigData.2018.8622314