Study Comparing the Performance of Linear and Non-linear Models in Recommendation Systems 


Vol. 13,  No. 8, pp. 388-394, Aug.  2024
https://doi.org/10.3745/TKIPS.2024.13.8.388


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  Abstract

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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[IEEE Style]

D. Seong and Y. Lim, "Study Comparing the Performance of Linear and Non-linear Models in Recommendation Systems," The Transactions of the Korea Information Processing Society, vol. 13, no. 8, pp. 388-394, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.8.388.

[ACM Style]

Da-Hun Seong and Yujin Lim. 2024. Study Comparing the Performance of Linear and Non-linear Models in Recommendation Systems. The Transactions of the Korea Information Processing Society, 13, 8, (2024), 388-394. DOI: https://doi.org/10.3745/TKIPS.2024.13.8.388.

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