4 papers
Enhancing Time Awareness in Generative Recommendation
Sunkyung Lee, Seongmin Park, Jonghyo Kim +2
Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large langua…
MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation
Ilwoong Baek, Mincheol Yoon, Seongmin Park +1
Sequential recommendation (SR) aims to predict users' subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which…
Why is Normalization Necessary for Linear Recommenders?
Seongmin Park, Mincheol Yoon, Hye-young Kim +1
Despite their simplicity, linear autoencoder (LAE)-based models have shown comparable or even better performance with faster inference speed than neural recommender models. However…
Temporal Linear Item-Item Model for Sequential Recommendation
Seongmin Park, Mincheol Yoon, Minjin Choi +1
In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. On…