TY - GEN
T1 - Early findings from a large-scale user study of chestnut
T2 - Thematic Area on Human Interface and the Management of Information, HIMI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Peng, Xiangjun
AU - Huang, Zhentao
AU - Yang, Chen
AU - Song, Zilin
AU - Sun, Xu
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Towards a serendipitous recommender system with user-centred understanding, we have built CHESTNUT, an Information Theory-based Movie Recommender System, which introduced a more comprehensive understanding of the concept. Although off-line evaluations have already demonstrated that CHESTNUT has greatly improved serendipity performance, feedback on CHESTNUT from real-world users through online services are still unclear now. In order to evaluate how serendipitous results could be delivered by CHESTNUT, we consequently designed, organized and conducted large-scale user study, which involved 104 participants from 10 campuses in 3 countries. Our preliminary feedback has shown that, compared with mainstream collaborative filtering techniques, though CHESTNUT limited users’ feelings of unexpectedness to some extent, it showed significant improvement in their feelings about certain metrics being both beneficial and interesting, which substantially increased users’ experience of serendipity. Based on them, we have summarized three key takeaways, which would be beneficial for further designs and engineering of serendipitous recommender systems, from our perspective. All details of our large-scale user study could be found at https://github.com/unnc-idl-ucc/Early-Lessons-From-CHESTNUT.
AB - Towards a serendipitous recommender system with user-centred understanding, we have built CHESTNUT, an Information Theory-based Movie Recommender System, which introduced a more comprehensive understanding of the concept. Although off-line evaluations have already demonstrated that CHESTNUT has greatly improved serendipity performance, feedback on CHESTNUT from real-world users through online services are still unclear now. In order to evaluate how serendipitous results could be delivered by CHESTNUT, we consequently designed, organized and conducted large-scale user study, which involved 104 participants from 10 campuses in 3 countries. Our preliminary feedback has shown that, compared with mainstream collaborative filtering techniques, though CHESTNUT limited users’ feelings of unexpectedness to some extent, it showed significant improvement in their feelings about certain metrics being both beneficial and interesting, which substantially increased users’ experience of serendipity. Based on them, we have summarized three key takeaways, which would be beneficial for further designs and engineering of serendipitous recommender systems, from our perspective. All details of our large-scale user study could be found at https://github.com/unnc-idl-ucc/Early-Lessons-From-CHESTNUT.
KW - Recommeder systems
KW - Serendipity
KW - User study
UR - http://www.scopus.com/inward/record.url?scp=85088745763&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50017-7_5
DO - 10.1007/978-3-030-50017-7_5
M3 - Conference contribution
AN - SCOPUS:85088745763
SN - 9783030500160
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 77
BT - Human Interface and the Management of Information. Interacting with Information - Thematic Area, HIMI 2020, Held as Part of the 22nd International Conference, HCII 2020, Proceedings
A2 - Yamamoto, Sakae
A2 - Mori, Hirohiko
PB - Springer
Y2 - 19 July 2020 through 24 July 2020
ER -