# An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory
## 2 Related Work  
### Collaborative Filtering Recommendation  
Recommendation System (RS) plays an important role in today's Internet, which aims to filter relevant as well as important information for users from previous feedbacks. The demand for such systems is gradually increasing along with the overload of data on the Internet. The root of RS can be traced back to the extensive researches in cognitive science [15], information retrieval [16], management science [17], and also in consumer choice modeling in marketing [18]. Later on, RS developed into an independent research field in the mid-1990s to tackle problems in the structure of explicit rating [19]. Naturally, one of the most common processes is to turn recommendations into operations to predict an unknown item's rating for users based on their previous behaviors, namely, the rating \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User** & **Movie** & **Rating** & **Time** & **Preference Cluster** & **Similarity to Movie 4** \\ \hline \(a\) & 1 & 2.5 & 05 April 2018 & C1 & 0.9 \\ \(a\) & 3 & 4.0 & 11 May 2018 & C2 & 0.8 \\ \(b\) & 4 & 3.5 & 05 April 2018 & C3 & 1.0 \\ \(b\) & 1 & 2.0 & 01 June 2018 & C1 & 0.9 \\ \(c\) & 2 & 2.5 & 10 October 2018 & C4 & 0.7 \\ \(c\) & 3 & 3.0 & 15 October 2018 & C2 & 0.8 \\ \(a\) & 4 &? & 11 December 2018 & C3 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The structure of sample data. prediction problem. Then, we can recommend the highest rated items to the user based on the predictions. In addition, there are top-N based recommendations, but this paper mainly focuses on the problem of rating prediction. Moreover, the approaches of RS can be divided into three categories [19]: (1) content-based recommendation, (2) collaborative recommendation, and (3) hybrid approaches. CF algorithms belong to the collaborative recommendation, which makes recommendations similar to user's previous preference or from similar users. According to [20], algorithms for collaborative recommendations can be grouped into two general classes: memory-based(or heuristic-based) and model-based. Memory-based algorithms [20; 21; 22] are essentially heuristics that make recommendations based on the entire dataset of user previous behaviors, which can be further divided into userCF and itemCF. Furthermore, [23; 24] present empirical evidence that itemCF can provide better computational performance as well as better quality of recommendation than traditional userCF. On the other hand, model-based algorithms [25; 26; 27; 28; 29; 30] focus on model learning and then apply the trained model to the recommendation. Meanwhile, there are two popular error functions for CF algorithms to evaluate the performance, especially for the task of rating prediction: mean absolute error (MAE) and root mean squared error (RMSE). Since the absolute value function is not differentiable at all points, RMSE is more desirable as an objective function [31]. However, traditional CF algorithms suffer from different problems: sparsity, high dimensionality of data [32], and disability of capturing the evolution of user preferences over time. From now on, amounts of researches in the recommendation community have emerged to tackle these difficulties. For example, to tackle the first problem, [33] directly reduce the number of users/items, [34; 35] extract information from clustered groups, and more advanced, [36; 37] propose to learn a latent space vector for each user/item by deep learning algorithms. On the second problem, to the best of our knowledge, there are two main different solutions that take user's dynamic preferences into consideration. One way is to directly apply time as a factor [7; 8; 38; 39], e.g., [PERSON]'s forgetting curve.