Abstract
Users usually play dual roles in real-world recommender systems. One is as a reviewer who writes reviews for items with rating scores, and the other is as a rater who rates the helpfulness scores of reviews. Traditional recommender systems mainly consider the reviewer role while not taking into account the rater role. However, the rater role allows users to express their opinions toward reviews about items; hence it may indirectly indicate their opinions about items, which could be complementary to the reviewer role. Since most real-world recommender systems provide convenient mechanisms for the rater role, recent studies show that typically there are much more helpfulness ratings from the rater role than item ratings from the reviewer role. Therefore, incorporating the rater role of users may have the potentials to mitigate the data sparsity and cold-start problems in traditional recommender systems. In this paper, we investigate how to exploit dual roles of users in recommender systems. In particular, we provide a principled way to exploit the rater role mathematically and propose a novel recommender system DualRec, which captures both the reviewer role and the rater role of users simultaneously for recommendation. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework, and further experiments are conducted to understand the importance of the rater role of users in recommendation.
| Original language | English (US) |
|---|---|
| Title of host publication | International Conference on Information and Knowledge Management, Proceedings |
| Publisher | Association for Computing Machinery |
| Pages | 1651-1660 |
| Number of pages | 10 |
| Volume | 19-23-Oct-2015 |
| ISBN (Print) | 9781450337946 |
| DOIs | |
| State | Published - Oct 17 2015 |
| Event | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia Duration: Oct 19 2015 → Oct 23 2015 |
Other
| Other | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 10/19/15 → 10/23/15 |
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Keywords
- Cold-start
- Collaborative filtering
- Helpfulness rating
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Decision Sciences(all)
Cite this
Toward dual roles of users in recommender systems. / Wang, Suhang; Tang, Jiliang; Liu, Huan.
International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015 Association for Computing Machinery, 2015. p. 1651-1660.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Toward dual roles of users in recommender systems
AU - Wang, Suhang
AU - Tang, Jiliang
AU - Liu, Huan
PY - 2015/10/17
Y1 - 2015/10/17
N2 - Users usually play dual roles in real-world recommender systems. One is as a reviewer who writes reviews for items with rating scores, and the other is as a rater who rates the helpfulness scores of reviews. Traditional recommender systems mainly consider the reviewer role while not taking into account the rater role. However, the rater role allows users to express their opinions toward reviews about items; hence it may indirectly indicate their opinions about items, which could be complementary to the reviewer role. Since most real-world recommender systems provide convenient mechanisms for the rater role, recent studies show that typically there are much more helpfulness ratings from the rater role than item ratings from the reviewer role. Therefore, incorporating the rater role of users may have the potentials to mitigate the data sparsity and cold-start problems in traditional recommender systems. In this paper, we investigate how to exploit dual roles of users in recommender systems. In particular, we provide a principled way to exploit the rater role mathematically and propose a novel recommender system DualRec, which captures both the reviewer role and the rater role of users simultaneously for recommendation. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework, and further experiments are conducted to understand the importance of the rater role of users in recommendation.
AB - Users usually play dual roles in real-world recommender systems. One is as a reviewer who writes reviews for items with rating scores, and the other is as a rater who rates the helpfulness scores of reviews. Traditional recommender systems mainly consider the reviewer role while not taking into account the rater role. However, the rater role allows users to express their opinions toward reviews about items; hence it may indirectly indicate their opinions about items, which could be complementary to the reviewer role. Since most real-world recommender systems provide convenient mechanisms for the rater role, recent studies show that typically there are much more helpfulness ratings from the rater role than item ratings from the reviewer role. Therefore, incorporating the rater role of users may have the potentials to mitigate the data sparsity and cold-start problems in traditional recommender systems. In this paper, we investigate how to exploit dual roles of users in recommender systems. In particular, we provide a principled way to exploit the rater role mathematically and propose a novel recommender system DualRec, which captures both the reviewer role and the rater role of users simultaneously for recommendation. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework, and further experiments are conducted to understand the importance of the rater role of users in recommendation.
KW - Cold-start
KW - Collaborative filtering
KW - Helpfulness rating
UR - http://www.scopus.com/inward/record.url?scp=84959256902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959256902&partnerID=8YFLogxK
U2 - 10.1145/2806416.2806520
DO - 10.1145/2806416.2806520
M3 - Conference contribution
SN - 9781450337946
VL - 19-23-Oct-2015
SP - 1651
EP - 1660
BT - International Conference on Information and Knowledge Management, Proceedings
PB - Association for Computing Machinery
ER -