  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 state and item embeddings in
the same latent space and subject them to Newton's law of gravitation.
Moreover, to recommend items to users, we model the reality principle, i.e.,
balancing the conscious and unconscious states, via a gating function. Based on
extensive experiments on various benchmark datasets, this paper provides
insight into the characteristics of the proposed model. FaNC initiates a new
direction of sequential recommendations at the convergence of psychoanalysis
and recommender systems.
