Recommender system for online shopping

Recommender systems are changing from novelties utilized by some E-commerce sites to important commercial enterprise tools. All the large E-commerce sites have their own recommender systems to recommend products to customer. Current recommendation algorithms usually learn the ranking scores of items...

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Main Author: Wan, Tianyi
Other Authors: Zhang Jie
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/153259
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1532592021-11-17T02:28:59Z Recommender system for online shopping Wan, Tianyi Zhang Jie School of Computer Science and Engineering ZhangJ@ntu.edu.sg Engineering::Computer science and engineering Recommender systems are changing from novelties utilized by some E-commerce sites to important commercial enterprise tools. All the large E-commerce sites have their own recommender systems to recommend products to customer. Current recommendation algorithms usually learn the ranking scores of items by using a single task like conversion rate base on the user data. However, there are new ways such as adding transformers to model users’ multiple types of behaviour sequences like click through rate (CTR) and conversion rate (CVR) simultaneously. Many of the new recommender systems have also been using multi-task learning (MTL). Studies also found that there is a seesaw phenomenon on multi-task learning where performance of all tasks is not matching which means performance of a task is improved by sacrificing other tasks. However, there is not a single recommender system that model both the multiple types of user behaviour sequence and seesaw phenomenon of multi-task learning. In this paper, I proposed a Multi-transformer Progressive Layered Extraction (MTPLE) model for online recommender system. It utilises the transformers for user’s multiple types of behaviour sequences simultaneously. In addition, it uses progressive layered extraction to optimize multiple objectives and reduce seesaw phenomenon of multi-task learning. Experiments on JD RecSys Dataset were carried out to demonstrate the effectiveness of MTPLE. MTPLE achieved an overall improvement in performance compared to other models. Bachelor of Engineering (Computer Science) 2021-11-17T02:28:59Z 2021-11-17T02:28:59Z 2021 Final Year Project (FYP) Wan, T. (2021). Recommender system for online shopping. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153259 https://hdl.handle.net/10356/153259 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Wan, Tianyi
Recommender system for online shopping
description Recommender systems are changing from novelties utilized by some E-commerce sites to important commercial enterprise tools. All the large E-commerce sites have their own recommender systems to recommend products to customer. Current recommendation algorithms usually learn the ranking scores of items by using a single task like conversion rate base on the user data. However, there are new ways such as adding transformers to model users’ multiple types of behaviour sequences like click through rate (CTR) and conversion rate (CVR) simultaneously. Many of the new recommender systems have also been using multi-task learning (MTL). Studies also found that there is a seesaw phenomenon on multi-task learning where performance of all tasks is not matching which means performance of a task is improved by sacrificing other tasks. However, there is not a single recommender system that model both the multiple types of user behaviour sequence and seesaw phenomenon of multi-task learning. In this paper, I proposed a Multi-transformer Progressive Layered Extraction (MTPLE) model for online recommender system. It utilises the transformers for user’s multiple types of behaviour sequences simultaneously. In addition, it uses progressive layered extraction to optimize multiple objectives and reduce seesaw phenomenon of multi-task learning. Experiments on JD RecSys Dataset were carried out to demonstrate the effectiveness of MTPLE. MTPLE achieved an overall improvement in performance compared to other models.
author2 Zhang Jie
author_facet Zhang Jie
Wan, Tianyi
format Final Year Project
author Wan, Tianyi
author_sort Wan, Tianyi
title Recommender system for online shopping
title_short Recommender system for online shopping
title_full Recommender system for online shopping
title_fullStr Recommender system for online shopping
title_full_unstemmed Recommender system for online shopping
title_sort recommender system for online shopping
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/153259
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