Abstract
Retailing and social media platforms recommend two types of items to their users: sponsored items that generate ad revenue and non-sponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through some form of programmatic auction, and non-sponsored items to maximize user utility with a recommender system (RS). We develop a multiobjective binary integer programming model to allocate sponsored recommendations considering a dual objective of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method can be applied as a form of post processing to an existing RS, making it widely applicable. We apply the model to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach, which unifies programmatic advertising and RS, opens a new frontier for advertising and RS research and we therefore provide an extended discussion of future research topics.
| Original language | English (US) |
|---|---|
| Journal | CEUR Workshop Proceedings |
| Volume | 2440 |
| State | Published - Jan 1 2019 |
| Event | 2019 Workshop on Recommendation in Multi-Stakeholder Environments, RMSE 2019 - Copenhagen, Denmark Duration: Sep 20 2019 → … |
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Keywords
- Multistakeholder
- Platforms
- Recommender systems
- Sponsored content
- Sponsored recommendations
ASJC Scopus subject areas
- Computer Science(all)
Cite this
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A multistakeholder recommender systems algorithm for allocating sponsored recommendations. / Malthouse, Edward Carl; Vakeel, Khadija Ali; Hessary, Yasaman Kamyab; Burke, Robin; Fudurić, Morana.
In: CEUR Workshop Proceedings, Vol. 2440, 01.01.2019.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - A multistakeholder recommender systems algorithm for allocating sponsored recommendations
AU - Malthouse, Edward Carl
AU - Vakeel, Khadija Ali
AU - Hessary, Yasaman Kamyab
AU - Burke, Robin
AU - Fudurić, Morana
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Retailing and social media platforms recommend two types of items to their users: sponsored items that generate ad revenue and non-sponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through some form of programmatic auction, and non-sponsored items to maximize user utility with a recommender system (RS). We develop a multiobjective binary integer programming model to allocate sponsored recommendations considering a dual objective of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method can be applied as a form of post processing to an existing RS, making it widely applicable. We apply the model to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach, which unifies programmatic advertising and RS, opens a new frontier for advertising and RS research and we therefore provide an extended discussion of future research topics.
AB - Retailing and social media platforms recommend two types of items to their users: sponsored items that generate ad revenue and non-sponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through some form of programmatic auction, and non-sponsored items to maximize user utility with a recommender system (RS). We develop a multiobjective binary integer programming model to allocate sponsored recommendations considering a dual objective of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method can be applied as a form of post processing to an existing RS, making it widely applicable. We apply the model to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach, which unifies programmatic advertising and RS, opens a new frontier for advertising and RS research and we therefore provide an extended discussion of future research topics.
KW - Multistakeholder
KW - Platforms
KW - Recommender systems
KW - Sponsored content
KW - Sponsored recommendations
UR - http://www.scopus.com/inward/record.url?scp=85072737058&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072737058&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85072737058
VL - 2440
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
ER -