Freudian and Newtonian Recurrent Cell for Sequential Recommendation
Hoyeop Lee, Jinbae Im, Chang Ouk Kim, Sehee Chung

TL;DR
This paper introduces FaNC, a novel recurrent cell inspired by Freudian and Newtonian theories, to model user decision-making in sequential recommendation systems by separating conscious and unconscious states.
Contribution
It proposes a new psychoanalytically inspired recurrent cell, FaNC, that models user states and decision processes using Freud's principles and Newtonian physics, offering a novel perspective in recommendation models.
Findings
FaNC outperforms traditional models on benchmark datasets.
The model provides new insights into user decision-making processes.
It bridges psychoanalysis and recommender systems for the first time.
Abstract
A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings. However, these approaches lack fundamental insight into how such models are related to the user's inherent decision-making process. To provide this insight, we propose a novel recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC divides the user's state into conscious and unconscious states, and the user's decision process is modelled by Freud's two principles: the pleasure principle and reality principle. To model the pleasure principle, i.e., free-floating user's instinct, we place the user's unconscious…
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Taxonomy
TopicsRecommender Systems and Techniques · Topic Modeling · Advanced Graph Neural Networks
