Yo Han Park, Jong Hyeok Mun, Jong Sun Choi, Jae Young Choi

KIPS Transactions on Computer and Communication Systems, Vol. 13, No. 1, pp. 10-20, Jan. 2024
https://doi.org/10.3745/KTCCS.2024.13.1.10,   PDF Download:
Keywords: Recommendation System, User Behavior Data, Implicit Feedback Data, Co-occurrence, Target Data, Auxiliary Data
Abstract

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

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