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Successful sequential recommendation systems rely on accurately capturing theuser's short-term and long-term interest. Although Transformer-based modelsachieved state-of-the-art performance in the sequential recommendation task,they generally require quadratic memory and time complexity to the sequencelength, making it difficult to extract the long-term interest of users. On theother hand, Multi-Layer Perceptrons (MLP)-based models, renowned for theirlinear memory and time complexity, have recently shown competitive resultscompared to Transformer in various tasks. Given the availability of a massiveamount of the user's behavior history, the linear memory and time complexity ofMLP-based models make them a promising alternative to explore in the sequentialrecommendation task. To this end, we adopted MLP-based models in sequentialrecommendation but consistently observed that MLP-based methods obtain lowerperformance than those of Transformer despite their computational benefits.From experiments, we observed that introducing explicit high-order interactionsto MLP layers mitigates such performance gap. In response, we propose theMulti-Order Interaction (MOI) layer, which is capable of expressing anarbitrary order of interactions within the inputs while maintaining the memoryand time complexity of the MLP layer. By replacing the MLP layer with the MOIlayer, our model was able to achieve comparable performance withTransformer-based models while retaining the MLP-based models' computationalbenefits.

MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation