Fair-SRS: a fair session-based recommendation system

Naime Ranjbar Kermany; Jian Yang; Jia Wu; Luiz Pizzato

doi:10.1145/3488560.3502191

Fair-SRS: a fair session-based recommendation system

Naime Ranjbar Kermany, Jian Yang, Jia Wu, Luiz Pizzato

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

9 Citations (Scopus)

Abstract

This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.

Original language	English
Title of host publication	WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
Place of Publication	New York, NY
Publisher	Association for Computing Machinery, Inc
Pages	1601-1604
Number of pages	4
ISBN (Electronic)	9781450391320
DOIs	https://doi.org/10.1145/3488560.3502191
Publication status	Published - 2022
Event	15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual, Online, United States Duration: 21 Feb 2022 → 25 Feb 2022

Conference

Conference	15th ACM International Conference on Web Search and Data Mining, WSDM 2022
Country/Territory	United States
City	Virtual, Online
Period	21/02/22 → 25/02/22

Keywords

Fairness
Personalized diversity
Session-based recommendation

Access to Document

10.1145/3488560.3502191

Cite this

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title = "Fair-SRS: a fair session-based recommendation system",

abstract = "This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.",

keywords = "Fairness, Personalized diversity, Session-based recommendation",

author = "{Ranjbar Kermany}, Naime and Jian Yang and Jia Wu and Luiz Pizzato",

year = "2022",

doi = "10.1145/3488560.3502191",

language = "English",

pages = "1601--1604",

booktitle = "WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining",

publisher = "Association for Computing Machinery, Inc",

note = "15th ACM International Conference on Web Search and Data Mining, WSDM 2022 ; Conference date: 21-02-2022 Through 25-02-2022",

}

Ranjbar Kermany, N, Yang, J , Wu, J & Pizzato, L 2022, Fair-SRS: a fair session-based recommendation system. in WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, New York, NY, pp. 1601-1604, 15th ACM International Conference on Web Search and Data Mining, WSDM 2022, Virtual, Online, United States, 21/02/22. https://doi.org/10.1145/3488560.3502191

Fair-SRS: a fair session-based recommendation system. / Ranjbar Kermany, Naime; Yang, Jian ; Wu, Jia et al.
WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining. New York, NY: Association for Computing Machinery, Inc, 2022. p. 1601-1604.

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

TY - GEN

T1 - Fair-SRS

T2 - 15th ACM International Conference on Web Search and Data Mining, WSDM 2022

AU - Ranjbar Kermany, Naime

AU - Yang, Jian

AU - Wu, Jia

AU - Pizzato, Luiz

PY - 2022

Y1 - 2022

N2 - This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.

AB - This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.

KW - Fairness

KW - Personalized diversity

KW - Session-based recommendation

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DO - 10.1145/3488560.3502191

M3 - Conference proceeding contribution

AN - SCOPUS:85125795244

SP - 1601

EP - 1604

BT - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining

PB - Association for Computing Machinery, Inc

CY - New York, NY

Y2 - 21 February 2022 through 25 February 2022

ER -

Fair-SRS: a fair session-based recommendation system

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