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Tsinghua Science and Technology 2024, 29(5): 1580-1592 https://doi.org/10.26599/TST.2023.9010118
Open Access | Issue | Published: 02 May 2024

Exploring Artistic Embeddings in Service Design: A Keyword-Driven Approach for Artwork Search and Recommendations

Show Author's Information Jie Yuan1,2,  Fangru Lin1,  Hae Yoon Kim1( )
College of Design, Dongseo University, Busan 47011, Republic of Korea
Shandong Provincial University Laboratory for Protected Horticulture, College of Arts, Weifang University of Science and Technology, Weifang 262700, China
Keywords:
dynamic programming, recommendation, artwork, self-attention, implicit interest
Cite this article:
Yuan J, Lin F, Kim HY. Exploring Artistic Embeddings in Service Design: A Keyword-Driven Approach for Artwork Search and Recommendations. Tsinghua Science and Technology, 2024, 29(5): 1580-1592. https://doi.org/10.26599/TST.2023.9010118
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Abstract Full text About this article

As living standards improve, the demand for artworks has been escalating, transcending beyond the realm of mere basic human necessities. However, amidst an extensive array of artwork choices, users often struggle to swiftly and accurately identify their preferred piece. In such scenarios, a recommendation system can be invaluable, assisting users in promptly pinpointing the desired artworks for better service design. Despite the escalating demand for artwork recommendation systems, current research fails to adequately meet these needs. Predominantly, existing artwork recommendation methodologies tend to disregard users’ implicit interests, thereby overestimating their capability to articulate their preferences in full and often neglecting the nuances of their diverse interests. In response to these challenges, we have developed a weighted artwork correlation graph and put forth an embedding-based keyword-driven artwork search and recommendation methodology. Our approach transforms the keywords that delineate user interests into word embedding vectors. This allows for an effective distinction between the user’s core and peripheral interests. Subsequently, we employ a dynamic programming algorithm to extract artworks from the correlation graph, thereby obtaining artworks that align with the user’s explicit keywords and implicit interests. We have conducted an array of experiments using real-world datasets to validate our approach. The results attest to the superiority of our method in terms of its efficacy in searching and recommending artworks.


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About this article

Exploring Artistic Embeddings in Service Design: A Keyword-Driven Approach for Artwork Search and Recommendations

Show Author's information Jie Yuan1,2, Fangru Lin1, Hae Yoon Kim1( )
College of Design, Dongseo University, Busan 47011, Republic of Korea
Shandong Provincial University Laboratory for Protected Horticulture, College of Arts, Weifang University of Science and Technology, Weifang 262700, China

Abstract

As living standards improve, the demand for artworks has been escalating, transcending beyond the realm of mere basic human necessities. However, amidst an extensive array of artwork choices, users often struggle to swiftly and accurately identify their preferred piece. In such scenarios, a recommendation system can be invaluable, assisting users in promptly pinpointing the desired artworks for better service design. Despite the escalating demand for artwork recommendation systems, current research fails to adequately meet these needs. Predominantly, existing artwork recommendation methodologies tend to disregard users’ implicit interests, thereby overestimating their capability to articulate their preferences in full and often neglecting the nuances of their diverse interests. In response to these challenges, we have developed a weighted artwork correlation graph and put forth an embedding-based keyword-driven artwork search and recommendation methodology. Our approach transforms the keywords that delineate user interests into word embedding vectors. This allows for an effective distinction between the user’s core and peripheral interests. Subsequently, we employ a dynamic programming algorithm to extract artworks from the correlation graph, thereby obtaining artworks that align with the user’s explicit keywords and implicit interests. We have conducted an array of experiments using real-world datasets to validate our approach. The results attest to the superiority of our method in terms of its efficacy in searching and recommending artworks.

Keywords: dynamic programming, recommendation, artwork, self-attention, implicit interest

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Received: 18 August 2023
Revised: 30 September 2023
Accepted: 13 October 2023
Published: 02 May 2024
Issue date: October 2024

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© The Author(s) 2024.

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