Abstract
The explosive popularity of social networking services such as Twitter and Facebook has made it common to communicate with unknown people via the Internet. Further, services based on a 'sharing economy' such as Airbnb and Uber have gained popularity and increased opportunities to connect with strangers. Users would like to determine whether the opinions of other users can be trusted. However, it is sometimes difficult to do so, and the judgment of other users is not necessarily correct. We propose a model for predicting the trust of unknown users by using information of users known to a target user. We predicted the trust of reviewers on an online review site based on the proposed model and recommended items based on collaborative filtering (CF) using the obtained trust of the reviewers. The experimental results showed that the recommendation accuracy with the trust was higher than that with general CF.
| Original language | English |
|---|---|
| Title of host publication | 2017 12th International Conference on Digital Information Management, ICDIM 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 252-256 |
| Number of pages | 5 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538606643 |
| DOIs | |
| Publication status | Published - Jan 2 2018 |
| Event | 12th International Conference on Digital Information Management, ICDIM 2017 - Fukuoka, Japan Duration: Sep 12 2017 → Sep 14 2017 |
Other
| Other | 12th International Conference on Digital Information Management, ICDIM 2017 |
|---|---|
| Country | Japan |
| City | Fukuoka |
| Period | 9/12/17 → 9/14/17 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
Cite this
Can you trust the user? Collaborative trust estimation model for recommendations. / Minami, Daichi; Ushiama, Taketoshi.
2017 12th International Conference on Digital Information Management, ICDIM 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 252-256.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Can you trust the user?
T2 - Collaborative trust estimation model for recommendations
AU - Minami, Daichi
AU - Ushiama, Taketoshi
PY - 2018/1/2
Y1 - 2018/1/2
N2 - The explosive popularity of social networking services such as Twitter and Facebook has made it common to communicate with unknown people via the Internet. Further, services based on a 'sharing economy' such as Airbnb and Uber have gained popularity and increased opportunities to connect with strangers. Users would like to determine whether the opinions of other users can be trusted. However, it is sometimes difficult to do so, and the judgment of other users is not necessarily correct. We propose a model for predicting the trust of unknown users by using information of users known to a target user. We predicted the trust of reviewers on an online review site based on the proposed model and recommended items based on collaborative filtering (CF) using the obtained trust of the reviewers. The experimental results showed that the recommendation accuracy with the trust was higher than that with general CF.
AB - The explosive popularity of social networking services such as Twitter and Facebook has made it common to communicate with unknown people via the Internet. Further, services based on a 'sharing economy' such as Airbnb and Uber have gained popularity and increased opportunities to connect with strangers. Users would like to determine whether the opinions of other users can be trusted. However, it is sometimes difficult to do so, and the judgment of other users is not necessarily correct. We propose a model for predicting the trust of unknown users by using information of users known to a target user. We predicted the trust of reviewers on an online review site based on the proposed model and recommended items based on collaborative filtering (CF) using the obtained trust of the reviewers. The experimental results showed that the recommendation accuracy with the trust was higher than that with general CF.
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UR - http://www.scopus.com/inward/citedby.url?scp=85049406772&partnerID=8YFLogxK
U2 - 10.1109/ICDIM.2017.8244681
DO - 10.1109/ICDIM.2017.8244681
M3 - Conference contribution
VL - 2018-January
SP - 252
EP - 256
BT - 2017 12th International Conference on Digital Information Management, ICDIM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -