用于顺序推荐的Freudian和Newtonian递归单元
Freudian and Newtonian Recurrent Cell for Sequential Recommendation
摘要：
顺序推荐系统的目的是根据用户的行为模式向用户推荐有吸引力的项目。主要的顺序推荐模型是基于自然语言处理模型的，例如门控递归单元，其将项目嵌入在某些定义的空间中，并基于项目嵌入来掌握用户的长期和短期偏好。然而，这些方法缺乏对这些模型如何与用户的固有决策过程相关的基本见解。为了提供这种见解，我们提出了一种新的复发细胞，即FaNC，从弗洛伊德和牛顿的观点。FaNC将用户的状态分为有意识和无意识状态，用户的决策过程以弗洛伊德的两个原则为模型：快乐原则和现实原则。为了模拟快乐原则，即，自由浮动的用户的本能，我们把用户的无意识状态和项目嵌入在同一个潜在的空间，并使他们受到牛顿的万有引力定律。此外，为了向用户推荐项目，我们对现实原则进行建模，即，通过门控功能来平衡意识和无意识状态。基于各种基准数据集上的大量实验，本文提供了洞察所提出的模型的特点。FaNC在精神分析和推荐系统的融合中开创了顺序推荐的新方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
