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 | |
| 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
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
}
TY - GEN
T1 - Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items
AU - Miyamoto, Seiki
AU - Zamami, Takumi
AU - Yamana, Hayato
PY - 2019/1/22
Y1 - 2019/1/22
N2 - 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.
AB - 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.
KW - collaborative filtering
KW - diversity
KW - recommender system
UR - http://www.scopus.com/inward/record.url?scp=85062623490&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062623490&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622314
DO - 10.1109/BigData.2018.8622314
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 5392
EP - 5394
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Song, Yang
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Abe, Naoki
A2 - Pu, Calton
A2 - Qiao, Mu
A2 - Ahmed, Nesreen
A2 - Kossmann, Donald
A2 - Saltz, Jeffrey
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Liu, Huan
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
ER -