## 2 Related Works  
In this section, this article will introduce some related work in the field of news recommendations. Traditional news recommendation methods mainly rely on collaborative filtering algorithms, content-based recommendation algorithms, and hybrid recommendation algorithms combined with manual feature engineering to represent users and news [5]. For example, collaborative filtering is a classic recommendation algorithm that can effectively utilize user behavior information to capture user interests and recommend similar news, and is also relatively easy to implement and explain [13; 14; 15]. Content-based recommendation algorithms [16; 17] are mainly used for recommending text-based items, which is a method that utilizes users' historical behaviors, user features, and item features to provide recommendations for users. It first constructs user features, then item features, and finally recommends to users based on user features and item features. Hybrid recommendation algorithms [18; 19] combine various types of recommendation algorithms through methods such as weighting, switching, blending, stacking, cascading, and feature combination, aiming to enhance the performance and effectiveness of recommendation systems. Therefore, many early news recommendation algorithms adopted these methods. Manual feature engineering is a commonly used feature representation method. For news recommendations, some news-related features such as keywords, topics, etc., are utilized to construct news-feature vectors, in addition to some user attributes and behavior information, such as user age, geographical location, etc. The advantages of these methods lie in their ability to utilize abundant domain knowledge to build high-quality feature vectors and improve the accuracy of recommendations. However, these methods also have some limitations. For instance, collaborative filtering algorithms encounter the \"cold start\" problem and perform poorly when user behavior is limited. Content-based recommendation algorithms typically rely on pre-defined features to describe items, which need to be extracted or specified manually. Due to this dependency, the algorithms may struggle to accurately capture the latest features or attribute changes of emerging and complex items, thereby resulting in recommendations that lack sufficient novelty and personalization. On the other hand, manual feature engineering is constrained by feature sparsity and scalability, making it difficult to handle large-scale and diverse data. Therefore, in recent years, researchers have begun to explore more intelligent methods, such as deep learning, to address these challenges. For instance, DKNs preprocess news through a knowledge graph, then learn news representations from news titles using a three-channel CNN and calculate relevance by aggregating candidates and news. NPA [2] applies personalized attention mechanisms to model user interests in different contexts and news. NRMS [11] utilizes a multi-head attention mechanism to construct word-level news-encoding modules. FIM [20] uses dilated convolution to extract multi-scale text features, and also employs 3D convolutions and MaxPooling to achieve fine-grained matching between accessed and candidate news at each semantic level. After 2021, many news recommendation methods have incorporated BERT modules for feature extraction in the preprocessing upstream part; some even combining multimodal methods by integrating information such as images to comprehensively process news, such as the UVCAN model [21]. However, this paper primarily focuses on studying the impact of structural optimization of the downstream part of preprocessing on the feature extraction effectiveness of news titles, so there is no comparison with incorporating BERT and multimodal news recommendation methods in this paper. This paper explores the advantages of some previous methods.