Advertisement Recommendation System as a Fuzzy Knapsack Problem

Ceren Öner; Başar Öztayşi

doi:10.1007/978-3-031-67195-1_62

Advertisement Recommendation System as a Fuzzy Knapsack Problem

Ceren Öner^*, Başar Öztayşi

^*Corresponding author for this work

Department of Industrial Engineering

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

Abstract

Effective advertisement allocation is a critical aspect within recommendation systems, necessitating robust methodologies such as the knapsack problem to accommodate a multitude of constraints. In our study, we delve into the intricacies of addressing the advertisement allocation problem through a fully fuzzy knapsack framework, aiming to provide comprehensive model insights. Our proposed methodology encompasses four key modules meticulously designed to preprocess data for the primary advertisement allocation model. These modules comprise: (i) the utilization of a Fuzzy Inference System (FIS) for detecting advertisement prices, (ii) the segmentation of locations employing Interval Valued Intuitionistic Fuzzy C Means (IVIFCM) clustering, (iii) the segmentation of users through Interval Valued Fuzzy Clustering (IVFC), and (iv) the integration of FastText to estimate the probability of sequential location visits. These modules serve as indispensable components of the fuzzy advertisement allocation problem, which we model as a fully fuzzy knapsack problem. Through our detailed exploration, we aim to offer valuable insights into the operationalization of advertisement allocation amidst imprecise data, thereby facilitating the decision-making process in advertisement display.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	559-568
Number of pages	10
ISBN (Print)	9783031671944
DOIs	https://doi.org/10.1007/978-3-031-67195-1_62
Publication status	Published - 2024
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey Duration: 16 Jul 2024 → 18 Jul 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1089 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/Territory	Turkey
City	Canakkale
Period	16/07/24 → 18/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

fuzzy knapsack problem
Fuzzy recommendation systems

Access to Document

10.1007/978-3-031-67195-1_62

Cite this

Öner, C., & Öztayşi, B. (2024). Advertisement Recommendation System as a Fuzzy Knapsack Problem. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. Ucal Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference (pp. 559-568). (Lecture Notes in Networks and Systems; Vol. 1089 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-67195-1_62

Öner, Ceren ; Öztayşi, Başar. / Advertisement Recommendation System as a Fuzzy Knapsack Problem. Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A. Cagri Tolga. Springer Science and Business Media Deutschland GmbH, 2024. pp. 559-568 (Lecture Notes in Networks and Systems).

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abstract = "Effective advertisement allocation is a critical aspect within recommendation systems, necessitating robust methodologies such as the knapsack problem to accommodate a multitude of constraints. In our study, we delve into the intricacies of addressing the advertisement allocation problem through a fully fuzzy knapsack framework, aiming to provide comprehensive model insights. Our proposed methodology encompasses four key modules meticulously designed to preprocess data for the primary advertisement allocation model. These modules comprise: (i) the utilization of a Fuzzy Inference System (FIS) for detecting advertisement prices, (ii) the segmentation of locations employing Interval Valued Intuitionistic Fuzzy C Means (IVIFCM) clustering, (iii) the segmentation of users through Interval Valued Fuzzy Clustering (IVFC), and (iv) the integration of FastText to estimate the probability of sequential location visits. These modules serve as indispensable components of the fuzzy advertisement allocation problem, which we model as a fully fuzzy knapsack problem. Through our detailed exploration, we aim to offer valuable insights into the operationalization of advertisement allocation amidst imprecise data, thereby facilitating the decision-making process in advertisement display.",

keywords = "fuzzy knapsack problem, Fuzzy recommendation systems",

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Öner, C & Öztayşi, B 2024, Advertisement Recommendation System as a Fuzzy Knapsack Problem. in C Kahraman, S Cevik Onar, B Oztaysi, I Ucal Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Lecture Notes in Networks and Systems, vol. 1089 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 559-568, International Conference on Intelligent and Fuzzy Systems, INFUS 2024, Canakkale, Turkey, 16/07/24. https://doi.org/10.1007/978-3-031-67195-1_62

Advertisement Recommendation System as a Fuzzy Knapsack Problem. / Öner, Ceren; Öztayşi, Başar.
Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A. Cagri Tolga. Springer Science and Business Media Deutschland GmbH, 2024. p. 559-568 (Lecture Notes in Networks and Systems; Vol. 1089 LNNS).

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

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T1 - Advertisement Recommendation System as a Fuzzy Knapsack Problem

AU - Öner, Ceren

AU - Öztayşi, Başar

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Effective advertisement allocation is a critical aspect within recommendation systems, necessitating robust methodologies such as the knapsack problem to accommodate a multitude of constraints. In our study, we delve into the intricacies of addressing the advertisement allocation problem through a fully fuzzy knapsack framework, aiming to provide comprehensive model insights. Our proposed methodology encompasses four key modules meticulously designed to preprocess data for the primary advertisement allocation model. These modules comprise: (i) the utilization of a Fuzzy Inference System (FIS) for detecting advertisement prices, (ii) the segmentation of locations employing Interval Valued Intuitionistic Fuzzy C Means (IVIFCM) clustering, (iii) the segmentation of users through Interval Valued Fuzzy Clustering (IVFC), and (iv) the integration of FastText to estimate the probability of sequential location visits. These modules serve as indispensable components of the fuzzy advertisement allocation problem, which we model as a fully fuzzy knapsack problem. Through our detailed exploration, we aim to offer valuable insights into the operationalization of advertisement allocation amidst imprecise data, thereby facilitating the decision-making process in advertisement display.

AB - Effective advertisement allocation is a critical aspect within recommendation systems, necessitating robust methodologies such as the knapsack problem to accommodate a multitude of constraints. In our study, we delve into the intricacies of addressing the advertisement allocation problem through a fully fuzzy knapsack framework, aiming to provide comprehensive model insights. Our proposed methodology encompasses four key modules meticulously designed to preprocess data for the primary advertisement allocation model. These modules comprise: (i) the utilization of a Fuzzy Inference System (FIS) for detecting advertisement prices, (ii) the segmentation of locations employing Interval Valued Intuitionistic Fuzzy C Means (IVIFCM) clustering, (iii) the segmentation of users through Interval Valued Fuzzy Clustering (IVFC), and (iv) the integration of FastText to estimate the probability of sequential location visits. These modules serve as indispensable components of the fuzzy advertisement allocation problem, which we model as a fully fuzzy knapsack problem. Through our detailed exploration, we aim to offer valuable insights into the operationalization of advertisement allocation amidst imprecise data, thereby facilitating the decision-making process in advertisement display.

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M3 - Conference contribution

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SN - 9783031671944

T3 - Lecture Notes in Networks and Systems

SP - 559

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BT - Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference

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A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Ucal Sari, Irem

A2 - Cebi, Selcuk

A2 - Tolga, A. Cagri

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2024

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Öner C, Öztayşi B. Advertisement Recommendation System as a Fuzzy Knapsack Problem. In Kahraman C, Cevik Onar S, Oztaysi B, Ucal Sari I, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Springer Science and Business Media Deutschland GmbH. 2024. p. 559-568. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-67195-1_62

Advertisement Recommendation System as a Fuzzy Knapsack Problem

Abstract

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Keywords

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