AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation

Understand

Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.

• A pivotal challenge in user modeling for SR lies in the inherent variability of user preferences.
• An effective SR model is expected to capture both the long-term and short-term preferences exhibited by users, wherein the former can offer a comprehensive understanding of stable interests that impact the latter.
• To more effectively capture such information, we incorporate locality inductive bias into the Transformer by amalgamating its global attention mechanism with a local convolutional filter, and adaptively ascertain the mixing importance on a personalized basis through layer-aware adaptive mixture units, termed as AdaMCT.