Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user’s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user’s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU.

中文翻译：

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基于追溯抑制的改进型协同过滤推荐算法

协同过滤（CF）是最经典且使用最广泛的推荐算法，主要用于通过挖掘用户的历史数据来预测用户的偏好。CF算法可以分为两大类：基于用户的CF和基于项目的CF，它们基于相似用户配置文件中的评分信息来推荐项目（基于用户）或基于项目之间的相似性来推荐项目（基于项目） 。但是，由于用户的偏好不是一成不变的，因此在做出推荐以实现更准确的推荐时，必须考虑用户的偏好变化。近年来，已有研究使用记忆作为衡量偏好变化的因素，并基于遗忘机制与时间之间的关系探索偏好的保留。不过，根据记忆抑制理论，引起遗忘的主要因素是追溯抑制和前瞻性抑制，而不仅仅是随时间的演变。因此，我们的工作提出了一种结合追溯抑制理论和传统的基于项目的CF算法（即RICF）的方法，以准确地探索用户偏好的演变。同时，引入嵌入训练来更好地表现特征，减轻数据稀疏性，然后将项目嵌入聚类以表示偏好点，以度量不同项目之间的偏好抑制。此外，我们在现实世界的数据集上进行了实验，以证明所提出的RICF的实用性。