## 2 Related Work  
Recommendation techniques mainly include content-based recommendations, collaborative filtering (CF), and hybrid recommendations. Content-based recommendation focuses on user profiles and preferences [15; 16; 17], while CF recommendation focuses on user ratings related to the user-item matrix to find a set of like-minded users or similar items [18; 19], and hybrid recommendation combines two or more recommendation techniques [20]. Collaborative filtering (CF) techniques play a significant role in recommender systems and can be classified into neighborhood-based CF and latent factor model-based CF. Neighborhood-based CF focuses on user ratings related to the user-item matrix to find a set of likeminded users or similar items and is mainly classified into user-based CF and item-based CF [21]. User-based CF discovers users with similar interests or preferences on items to a given user based on users' similarities, while item-based CF usually recommends similar items to a user based on items' correlations [22; 23; 24]. Components of reliability [6; 7], users' relationships and interest [22], personalized behaviors [23], and similarities [2; 24] are often used to improve the predictive accuracy of neighborhood-based CF recommendations. Personalization is the core topic during recommendation [25]. Latent factor-based CF uses historical rating data to discover latent features in users' behaviors. Because latent factor-based CF can train and learn users' personalization offline, it has higher prediction and has become one of the most popular topics in recommender systems since the Netflix Prize competition. The latent factor model is derived from the singular value decomposition (SVD) [26]. [PERSON] investigated baseline estimates, neighborhood models, and latent factor models of CF recommendations and proposed SVD++ by extending SVD to make full use of implicit feedback information [5]. [PERSON] et al. [9] incorporated social popularity factors in the SVD++ matrix factorization model to improve the practical accuracy of recommendations. [PERSON] et al. [10] adopted topic modeling to model latent topics in review content and used biased matrix factorization (MF) for prediction in recommender systems, simultaneously correlating with user and item factors. [PERSON] et al. [11; 12] investigated the effectiveness of novel fusing of trust networks to rating prediction, where not only the explicit but also the implicit influence of trust are integrated into the SVD model. [PERSON] et al. [13] proposed a self-transfer learning algorithm to iteratively identify and integrate some likely positive unlabeled feedback into the learning task of labeled feedback. In the past five years, deep learning has also been imported into the recommendation to discover more useful latent factors based on users' preferences by big data analysis. [PERSON] et al. [27] proposed a deep latent factor model based on a high-dimensional and sparse matrix to construct a deep-structured model by sequentially connecting multiple latent factor models. [PERSON] et al. [28] proposed a combined deep CF framework by probabilistic matrix factorization with marginalized denoising stacked autoencoders, deep learning the effective latent representations and achieving good performance. [PERSON] et al. [29] summarized related research efforts on deep learning-based recommender systems with expanded trend analysis and mentioned that recommendations need better, more unified, and harder evaluation. As argued in [1], the assessment criterion should be considered when choosing the appropriate recommendation. The baseline estimation model is a critical component for latent factor model-based CF recommendations, such as in [3; 5; 8; 9; 11; 12; 13; 14]. Baseline estimation can evaluate the global deviations of users' personalized ratings on items against global average ratings and the deviations of items' received ratings to the overall average [5]. The important elements in baseline estimation are \(b_{u},b_{i}\), and \(\mu\).