Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach

Xiangjun Peng; Hongzhi Zhang; Xiaosong Zhou; Shuolei Wang; Xu Sun; Qingfeng Wang

doi:10.1007/978-3-030-50017-7_6

Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach

Xiangjun Peng, Hongzhi Zhang, Xiaosong Zhou, Shuolei Wang, Xu Sun, Qingfeng Wang

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

The term “serendipity” has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-ucc/CHESTNUT.

Original language	English
Title of host publication	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
Editors	Sakae Yamamoto, Hirohiko Mori
Publisher	Springer
Pages	78-95
Number of pages	18
ISBN (Print)	9783030500160
DOIs	https://doi.org/10.1007/978-3-030-50017-7_6
Publication status	Published - 2020
Event	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 - Copenhagen, Denmark Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12185 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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
Country/Territory	Denmark
City	Copenhagen
Period	19/07/20 → 24/07/20

Keywords

Information Theory
Recommeder systems
Serendipity

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-50017-7_6

Cite this

Peng, X., Zhang, H., Zhou, X., Wang, S., Sun, X., & Wang, Q. (2020). Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach. In S. Yamamoto, & H. Mori (Eds.), 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 (pp. 78-95). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12185 LNCS). Springer. https://doi.org/10.1007/978-3-030-50017-7_6

Peng, Xiangjun ; Zhang, Hongzhi ; Zhou, Xiaosong et al. / Chestnut : Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach. 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. editor / Sakae Yamamoto ; Hirohiko Mori. Springer, 2020. pp. 78-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6621a7661cc94023bb47434840ec2dac,

title = "Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach",

abstract = "The term “serendipity” has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-ucc/CHESTNUT.",

keywords = "Information Theory, Recommeder systems, Serendipity",

author = "Xiangjun Peng and Hongzhi Zhang and Xiaosong Zhou and Shuolei Wang and Xu Sun and Qingfeng Wang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 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 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

doi = "10.1007/978-3-030-50017-7_6",

language = "English",

isbn = "9783030500160",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "78--95",

editor = "Sakae Yamamoto and Hirohiko Mori",

booktitle = "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",

}

Peng, X, Zhang, H, Zhou, X, Wang, S, Sun, X & Wang, Q 2020, Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach. in S Yamamoto & H Mori (eds), 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12185 LNCS, Springer, pp. 78-95, 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, Copenhagen, Denmark, 19/07/20. https://doi.org/10.1007/978-3-030-50017-7_6

Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach. / Peng, Xiangjun; Zhang, Hongzhi; Zhou, Xiaosong et al.
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. ed. / Sakae Yamamoto; Hirohiko Mori. Springer, 2020. p. 78-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12185 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 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 - Zhang, Hongzhi

AU - Zhou, Xiaosong

AU - Wang, Shuolei

AU - Sun, Xu

AU - Wang, Qingfeng

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - The term “serendipity” has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-ucc/CHESTNUT.

AB - The term “serendipity” has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-ucc/CHESTNUT.

KW - Information Theory

KW - Recommeder systems

KW - Serendipity

UR - http://www.scopus.com/inward/record.url?scp=85088742859&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-50017-7_6

DO - 10.1007/978-3-030-50017-7_6

M3 - Conference contribution

AN - SCOPUS:85088742859

SN - 9783030500160

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 78

EP - 95

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 -

Peng X, Zhang H, Zhou X, Wang S, Sun X , Wang Q. Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach. In Yamamoto S, Mori H, editors, 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. Springer. 2020. p. 78-95. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-50017-7_6

Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this