Deep Learning Social Filtering Model for Event Recommendation Services

David Ademola Oyemade; Linda Chioma Aworonye

doi:doi:10.11648/j.ajai.20240801.14

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Deep Learning Social Filtering Model for Event Recommendation Services

David Ademola Oyemade^*

, Linda Chioma Aworonye

Published in American Journal of Artificial Intelligence (Volume 8, Issue 1)

Received: 14 May 2024 Accepted: 29 May 2024 Published: 14 June 2024

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Abstract

In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

Published in	American Journal of Artificial Intelligence (Volume 8, Issue 1)
DOI	10.11648/j.ajai.20240801.14
Page(s)	22-31
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Deep Learning, Multilayered Perceptron, Neural Collaborative Filtering, Event Service Recommender

References

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APA Style

Oyemade, D. A., Aworonye, L. C. (2024). Deep Learning Social Filtering Model for Event Recommendation Services. American Journal of Artificial Intelligence, 8(1), 22-31. https://doi.org/10.11648/j.ajai.20240801.14

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ACS Style

Oyemade, D. A.; Aworonye, L. C. Deep Learning Social Filtering Model for Event Recommendation Services. Am. J. Artif. Intell. 2024, 8(1), 22-31. doi: 10.11648/j.ajai.20240801.14

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AMA Style

Oyemade DA, Aworonye LC. Deep Learning Social Filtering Model for Event Recommendation Services. Am J Artif Intell. 2024;8(1):22-31. doi: 10.11648/j.ajai.20240801.14

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@article{10.11648/j.ajai.20240801.14,
  author = {David Ademola Oyemade and Linda Chioma Aworonye},
  title = {Deep Learning Social Filtering Model for Event Recommendation Services
},
  journal = {American Journal of Artificial Intelligence},
  volume = {8},
  number = {1},
  pages = {22-31},
  doi = {10.11648/j.ajai.20240801.14},
  url = {https://doi.org/10.11648/j.ajai.20240801.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240801.14},
  abstract = {In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.
},
 year = {2024}
}

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TY  - JOUR
T1  - Deep Learning Social Filtering Model for Event Recommendation Services

AU  - David Ademola Oyemade
AU  - Linda Chioma Aworonye
Y1  - 2024/06/14
PY  - 2024
N1  - https://doi.org/10.11648/j.ajai.20240801.14
DO  - 10.11648/j.ajai.20240801.14
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 22
EP  - 31
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20240801.14
AB  - In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

VL  - 8
IS  - 1
ER  -

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Author Information

David Ademola Oyemade

Department of Computer Science, Federal University of Petroleum Resources, Effurun, Nigeria

Contact Email

http://orcid.org/0000-0003-4390-095X
Linda Chioma Aworonye

Department of Computer Science and Telecommunication, Novena University, Ogume, Nigeria

Contact Email

http://orcid.org/0009-0000-6835-5463

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Plain Text BibTeX RIS

APA Style

Oyemade, D. A., Aworonye, L. C. (2024). Deep Learning Social Filtering Model for Event Recommendation Services. American Journal of Artificial Intelligence, 8(1), 22-31. https://doi.org/10.11648/j.ajai.20240801.14

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ACS Style

Oyemade, D. A.; Aworonye, L. C. Deep Learning Social Filtering Model for Event Recommendation Services. Am. J. Artif. Intell. 2024, 8(1), 22-31. doi: 10.11648/j.ajai.20240801.14

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AMA Style

Oyemade DA, Aworonye LC. Deep Learning Social Filtering Model for Event Recommendation Services. Am J Artif Intell. 2024;8(1):22-31. doi: 10.11648/j.ajai.20240801.14

Copy | Download

@article{10.11648/j.ajai.20240801.14,
  author = {David Ademola Oyemade and Linda Chioma Aworonye},
  title = {Deep Learning Social Filtering Model for Event Recommendation Services
},
  journal = {American Journal of Artificial Intelligence},
  volume = {8},
  number = {1},
  pages = {22-31},
  doi = {10.11648/j.ajai.20240801.14},
  url = {https://doi.org/10.11648/j.ajai.20240801.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240801.14},
  abstract = {In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Deep Learning Social Filtering Model for Event Recommendation Services

AU  - David Ademola Oyemade
AU  - Linda Chioma Aworonye
Y1  - 2024/06/14
PY  - 2024
N1  - https://doi.org/10.11648/j.ajai.20240801.14
DO  - 10.11648/j.ajai.20240801.14
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 22
EP  - 31
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20240801.14
AB  - In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

VL  - 8
IS  - 1
ER  -

Copy | Download