TY - GEN
T1 - Session Expert
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
AU - Yi, Jinfeng
AU - Lei, Qi
AU - Yan, Junchi
AU - Sun, Wei
N1 - Funding Information:
ACKNOWLEDGEMENT The work was also in part supported by NSFC 61628203 and Partnership Collaboration Awards by The University of Sydney and Shanghai Jiao Tong University (WF610561702). The authors are thankful to Pietro Mazzoleni and Roman Vaculin for technical and subject matter discussions. The authors are also thankful to Dr. Chang Xu with Sydney University for his helpful technical discussion.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - At large and popular conferences, it is not uncommon for attendees to feel overwhelmed and lost while trying to navigate through many parallel sessions. In this paper, we present a conference session recommender system. In contrast to the conventional »query-search» model where a system passively engages with users, Session Expert actively interacts with users via natural, human-like conversations and provides personalized recommendations. The underlying session recommender engine is designed to handle the cold start problem, and is lightweight to enable real-time session recommendations and rationale-aware response generation. Specifically, the recommender system alleviates the cold start problem by transferring knowledge from another similar conference in an offline setting. This step is achieved by first exploiting a positive-unlabeled (PU) learning model to reveal the underlying user interest from the historical enrollment data, and then modeling a bilinear relationship which captures how user and session features influence users' interests. Given the learned bilinear model, recommendation scores and rationale can be generated online as it only involves a few matrix-vector multiplications which can be computed efficiently.
AB - At large and popular conferences, it is not uncommon for attendees to feel overwhelmed and lost while trying to navigate through many parallel sessions. In this paper, we present a conference session recommender system. In contrast to the conventional »query-search» model where a system passively engages with users, Session Expert actively interacts with users via natural, human-like conversations and provides personalized recommendations. The underlying session recommender engine is designed to handle the cold start problem, and is lightweight to enable real-time session recommendations and rationale-aware response generation. Specifically, the recommender system alleviates the cold start problem by transferring knowledge from another similar conference in an offline setting. This step is achieved by first exploiting a positive-unlabeled (PU) learning model to reveal the underlying user interest from the historical enrollment data, and then modeling a bilinear relationship which captures how user and session features influence users' interests. Given the learned bilinear model, recommendation scores and rationale can be generated online as it only involves a few matrix-vector multiplications which can be computed efficiently.
KW - cold start problem
KW - Conference session recommendation
KW - rationale generation
UR - http://www.scopus.com/inward/record.url?scp=85062432951&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062432951&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622231
DO - 10.1109/BigData.2018.8622231
M3 - Conference contribution
AN - SCOPUS:85062432951
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 1677
EP - 1682
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.
Y2 - 10 December 2018 through 13 December 2018
ER -