# Weight Adjustment Framework for Self-Attention Sequential Recommendation
###### Abstract  
In recent years, sequential recommendation systems have become a hot topic in the field of recommendation system research. These systems predict future user actions or preferences by analyzing their historical interaction sequences, such as browsing history and purchase records, and then recommend items that users may be interested in. Among various sequential recommendation algorithms, those based on the Transformer model have become a focus of research due to their powerful self-attention mechanisms. However, one of the main challenges faced by sequential recommendation systems is the noise present in the input data, such as erroneous clicks and incidental browsing. This noise can disrupt the model's accurate allocation of attention weights, thereby affecting the accuracy and personalization of the recommendation results. To address this issue, we propose a novel method named \"weight adjustment framework for self-attention sequential recommendation\" (WAF-SR). WAF-SR mitigates the negative impact of noise on the accuracy of the attention layer weight distribution by improving the quality of the input data. Furthermore, WAF-SR enhances the model's understanding of user behavior by simulating the uncertainty of user preferences, allowing for a more precise distribution of attention weights during the training process. Finally, a series of experiments demonstrate the effectiveness of the WAF-SR in enhancing the performance of sequential recommendation systems. sequential recommendation; deep learning; self-attention mechanism; denoise +
Footnote †: journal: Medical  
Traditional methods in sequential recommendation primarily include techniques such as Markov chains, matrix factorization [4], and k-nearest neighbor (KNN) [5]. These methods played a pivotal role in the early development of sequential recommendation. [PERSON] et al. [6] introduced the factorizing personalized Markov chains (FPMC) model. The core idea of FPMC is to combine traditional Markov chain approaches with matrix factorization techniques, constructing a personalized transition matrix for each user to form a transition cube. This approach retains the sequence-modeling capabilities of Markov chains while incorporating considerations for individual user preferences. In sequential recommendation, KNN is a method that considers the sequence of user behaviors [7]. The core idea of KNN is to find the K most similar neighbors to the target user and predict the target user's preferences for unknown items based on the behaviors or ratings of these neighbors or to recommend items to them. These traditional methods have achieved certain results in the field of recommendation systems, but their limitations in processing complex sequence data and understanding deep user preferences have spurred on the development of sequential recommendation methods based on deep learning. In recent years, deep learning technology has made significant progress with regard to recommendation algorithms [8; 9; 10]. With the successful application of deep learning technology in fields such as image recognition and natural language processing, researchers have begun to apply deep learning models to recommendation systems, especially in the field of sequential recommendation. [PERSON] et al. [11] proposed the deep interest network (DIN), specifically designed to improve the accuracy of click-through rate predictions. The core of this model lies in its unique user-interest-modeling approach, which captures the dynamic changes in user interests by learning deep representations of users' historical behaviors. With the rise of the Transformer architecture [12], Transformer-based sequential recommendation methods have increasingly garnered attention, such as SASRec [13] and BERT4 Rec [14]. The Transformer utilizes the self-attention mechanism to effectively model long-sequence data, address long-range dependency issues, and boasts an efficient computational speed.