Improvement of a Product Recommendation Model using Customers&#039; Search Patterns and Product Details (고객의 검색패턴과 상품 상세정보를 활용한 상품 추천 모형의 개선)

Lee Yunju (이윤주); Lee Jaejun (이재준); Ahn, Hyunchul (안현철)

doi:10.9708/jksci.2021.26.01.265

Improvement of a Product Recommendation Model using Customers' Search Patterns and Product Details

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(1), pp.265-274
DOI : 10.9708/jksci.2021.26.01.265
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : December 22, 2020
Accepted : January 20, 2021
Published : January 29, 2021

Lee Yunju ¹, Lee Jaejun ¹, Ahn, Hyunchul ¹

¹국민대학교

Accredited

ABSTRACT

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers’ purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.

KEYWORDS

Recommender Systems, Search Keywords, Product Details, Collaborative Filtering, Doc2vec

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Improvement of a Product Recommendation Model using Customers' Search Patterns and Product Details

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Improvement of a Product Recommendation Model using Customers' Search Patterns and Product Details

ABSTRACT

KEYWORDS

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REFERENCES (32) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.