# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 2 Related Work  
### Collaborative Filtering  
The collaborative filtering recommendation algorithm is the most widely used recommendation method. It was proposed by [PERSON] et al. [11] in 1992, was applied to news filtering in 1994, and has become one of the most popular recommendation algorithms in the corporate world [12]. There are three main categories of collaborative filtering-based recommendation methods--user-based collaborative filtering [13], item-based collaborative filtering [14; 15], and matrix factorization based collaborative filtering algorithms [16]. The core of the user-based collaborative filtering algorithm is to first identify users with high similarity to the target users by their preferences, predict the target users' ratings of the items of interest based on the ratings of the similar users, and recommend the series of items with the highest ratings to the users. The idea of the item-based collaborative filtering algorithm is similar, and recommendations are made by calculating the similarity between items. The matrix decomposition-based collaborative filtering algorithm represents the user's item rating information in the form of a matrix. It represents the user and the item in the low-dimensional space through the matrix decomposition operation, which portrays the correlation between household items as the inner product of the user's item vectors. However, in many cases, it may encounter problems such as low recommendation performance, cold start, and long tail, which are caused by the highly sparse and uneven distribution of data [17; 18]. The most widely used recommendation technique is the collaborative filtering recommendation algorithm. It mines the user's hidden preferences from the user's historical activity records and makes recommendations based on the user's hidden preferences. Collaborative filtering algorithms are mainly divided into three categories--memory-based collaborative filtering recommendation algorithms, model-based collaborative filtering algorithms and hybrid recommendation algorithms. Matrix-based recommendation algorithms have received a lot of attention in the industry because of their scalability and predictive ability in handling large scale data. PMF, SVD++, NMF, MMMF and NPCA are typical matrix-based recommendation algorithms. SoRec [19] first proposed a social recommendation model based on the shared representation of user feature matrix, which laid a solid foundation for subsequent researchers to carry out research on collaborative filtering algorithms based on matrix decomposition. Based on different ways of constructing social information, TrustMF, a social recommendation model based on the relationship between trust and trusted, was proposed by [PERSON] et al. [20]. TrustMF models trusting and trusted behaviors from the perspective of trust relationship generation, while SoRec models them only from the mathematical perspective, and the matrix decomposition process does not have any practical meaning. [PERSON] et al. [21] also proposed a recommendation model RSTE for weighted social information that takes into account the preferences of trusted friends. A new social recommendation model based on a trust propagation mechanism in social networks, SocialMF [22], was proposed by [PERSON] et al. Similar to SocialMF, [PERSON] et al. [23] proposed a social regularized recommendation model, SoReg, which argues that trust relationships and friend relationships are two different behaviors; a trust relationship is related to similar interests and hobbies among users, while a friend relationship is based entirely on real life social relationships. This information is added to the original SVD++ [24] to propose the TrustSVD model [25]. In traditional collaborative filtering algorithms, the extreme sparsity of the user-item rating matrix seriously affects the performance of the collaborative filtering algorithm.