# An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory
###### Abstract  
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. collaborative filtering; recommendation algorithm; retroactive inhibition; item embedding; embedding clustering +
Footnote †: journal: Applied  
13 January 2021  
Footnote †: journal: Applied  
13 January 2021  
known ratings of the target item by similar users. On the other hand, the itemCF calculates the similarity between items first and then predicts the unknown rating by averaging the (weighted) known ratings of similar items by other users [1; 2]. As shown in Figure 1, according to user's movie-watching history, user \(a\) and user \(b\) share preferences, so the userCF recommends movie 3 and movie 6, which user \(a\) likes, to user \(b\). Meanwhile, the itemCF recommends movie 2, which is similar to movie 6 that user \(b\) liked before, to user \(b\). CF algorithms are all designed to recommend items to users based on the preferences of similar users or the user's history, and the process is done by calculating the similarity between users or items. However, traditional CF algorithms suffer from the problem of data sparsity and inadequate exploration of mechanisms of how user preferences decay. In fact, several prior studies [3; 4; 5] pointed out that preference decay and memory decay are very similar. Thus, this paper proposes an improved CF algorithm based on retroactive inhibition of preferences (abbreviated as RICF) to capture the evolution of user's preferences. There are a number of papers in the field of recommendation systems that measure the memory forgetting process based directly on the decay of time [6; 7; 8; 9]. However, according to the [10; 11; 12] researches in psychology, memory attenuation mainly stems from memory inhibition. Therefore, RICF adopts the theory of retroactive inhibition, which measures the retention of the user's preference on item \(a\) by calculating the strength of inhibition that item \(a\) suffered within the corresponding time period, and consequently affects the weight of the contribution that item \(a\) makes to the target item's rating prediction.