ScholarWorks@숙명여자대학교

초록

Since recommendation systems play a key role in increasing the revenue of companies, various approaches and models have been studied in the past. However, this diversity also leads to a complexity in the types of recommendation systems, which makes it difficult to select a recommendation model. Therefore, this study aims to solve the difficulty of selecting an appropriate recommendation model for recommendation systems by providing a unified criterion for categorizing various recommendation models and comparing their performance in a unified environment. The experiments utilized MovieLens and Coursera datasets, and the performance of linear models(ADMM-SLIM, EASER, LightGCN) and non-linear models(Caser, BERT4Rec) were evaluated using HR@10 and NDCG@10 metrics. This study will provide researchers and practitioners with useful information for selecting the best model based on dataset characteristics and recommendation context.

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