Learning dynamic insurance recommendations from users' click sessions

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Learning dynamic insurance recommendations from users' click sessions. / Bruun, Simone Borg.

RecSys 2021 - 15th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc., 2021. p. 860-863 (RecSys 2021 - 15th ACM Conference on Recommender Systems).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Bruun, SB 2021, Learning dynamic insurance recommendations from users' click sessions. in RecSys 2021 - 15th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc., RecSys 2021 - 15th ACM Conference on Recommender Systems, pp. 860-863, 15th ACM Conference on Recommender Systems, RecSys 2021, Virtual, Online, Netherlands, 27/09/2021. https://doi.org/10.1145/3460231.3473900

APA

Bruun, S. B. (2021). Learning dynamic insurance recommendations from users' click sessions. In RecSys 2021 - 15th ACM Conference on Recommender Systems (pp. 860-863). Association for Computing Machinery, Inc.. RecSys 2021 - 15th ACM Conference on Recommender Systems https://doi.org/10.1145/3460231.3473900

Vancouver

Bruun SB. Learning dynamic insurance recommendations from users' click sessions. In RecSys 2021 - 15th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc. 2021. p. 860-863. (RecSys 2021 - 15th ACM Conference on Recommender Systems). https://doi.org/10.1145/3460231.3473900

Author

Bruun, Simone Borg. / Learning dynamic insurance recommendations from users' click sessions. RecSys 2021 - 15th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc., 2021. pp. 860-863 (RecSys 2021 - 15th ACM Conference on Recommender Systems).

Bibtex

@inproceedings{b924366cd10645a6a750ce5778c8a4d1,
title = "Learning dynamic insurance recommendations from users' click sessions",
abstract = "While personalised recommendations have been most successful in domains like retail due to large volume of users' feedback on items, it is challenging to implement traditional recommender systems into the insurance domain where such prior information is very small in volume. This work addresses the problem of sparse feedback by studying users' click sessions as signals for learning insurance recommendations. Our preliminary results show limitations in representing click sessions by manually engineered features. The proposed framework uses an autoencoder approach to automatically learns representation of sessions, then a neural network approach to model dependencies across sessions that can be used to predict recommendations. Thereby, it is further able to capture users' dynamic needs of insurance products evolving over time. ",
keywords = "Click-based models, Insurance domain, Session-based recommender systems",
author = "Bruun, {Simone Borg}",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 15th ACM Conference on Recommender Systems, RecSys 2021 ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2021",
month = sep,
day = "13",
doi = "10.1145/3460231.3473900",
language = "English",
series = "RecSys 2021 - 15th ACM Conference on Recommender Systems",
pages = "860--863",
booktitle = "RecSys 2021 - 15th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery, Inc.",

}

RIS

TY - GEN

T1 - Learning dynamic insurance recommendations from users' click sessions

AU - Bruun, Simone Borg

N1 - Publisher Copyright: © 2021 Owner/Author.

PY - 2021/9/13

Y1 - 2021/9/13

N2 - While personalised recommendations have been most successful in domains like retail due to large volume of users' feedback on items, it is challenging to implement traditional recommender systems into the insurance domain where such prior information is very small in volume. This work addresses the problem of sparse feedback by studying users' click sessions as signals for learning insurance recommendations. Our preliminary results show limitations in representing click sessions by manually engineered features. The proposed framework uses an autoencoder approach to automatically learns representation of sessions, then a neural network approach to model dependencies across sessions that can be used to predict recommendations. Thereby, it is further able to capture users' dynamic needs of insurance products evolving over time.

AB - While personalised recommendations have been most successful in domains like retail due to large volume of users' feedback on items, it is challenging to implement traditional recommender systems into the insurance domain where such prior information is very small in volume. This work addresses the problem of sparse feedback by studying users' click sessions as signals for learning insurance recommendations. Our preliminary results show limitations in representing click sessions by manually engineered features. The proposed framework uses an autoencoder approach to automatically learns representation of sessions, then a neural network approach to model dependencies across sessions that can be used to predict recommendations. Thereby, it is further able to capture users' dynamic needs of insurance products evolving over time.

KW - Click-based models

KW - Insurance domain

KW - Session-based recommender systems

U2 - 10.1145/3460231.3473900

DO - 10.1145/3460231.3473900

M3 - Article in proceedings

AN - SCOPUS:85115613496

T3 - RecSys 2021 - 15th ACM Conference on Recommender Systems

SP - 860

EP - 863

BT - RecSys 2021 - 15th ACM Conference on Recommender Systems

PB - Association for Computing Machinery, Inc.

T2 - 15th ACM Conference on Recommender Systems, RecSys 2021

Y2 - 27 September 2021 through 1 October 2021

ER -

ID: 306689949