Utilize Deep learning to increase the performance of a Book recommender system using the Item-based Collaborative Filtering

• Autorzy: Giap Cu Nguyen , Dieu Le Thi Huyen , Tuan Tran Manh , Ngan Tran Thi , Lan Luong Thi Hong

Identyfikatory

DOI

10.15439/2022R11

• Języki publikacji: EN

Abstrakty

EN

Item-based Collaborative Filtering is a common and efficient approach for recommendation problems. In this study, we have investigated the power of deep learning in textual feature extraction and applied this advantage to a high-performance item-based collaborative filtering recommender system. The proposed approach has been experienced on book datasets added by texts collected from famous book review sites. The experiment proves that the proposed model has better performance thanks to the contribution of the new item profile process method based on Deep Learning.

Słowa kluczowe

EN

item-based collaborative filtering; deep learning; textual embedded recommender system

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 109--113
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Giap Cu Nguyen

• Economic Information System and E-commerce Thuongmai University Hanoi, Vietnam

autor

Dieu Le Thi Huyen

• Faculty of Information Technology FPT University -FPT Polytechnic Hanoi, Vietnam

autor

Tuan Tran Manh

• Faculty of Computer Science and Engineering Thuyloi University Hanoi, Vietnam

autor

Ngan Tran Thi

• Faculty of Computer Science and Engineering Thuyloi University Hanoi, Vietnam

autor

Lan Luong Thi Hong

• Faculty of Computer Science and Engineering Thuyloi University Hanoi, Vietnam

Bibliografia

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