You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Towards Bayesian Deep Learning: A Survey 
Chapter Title:  Symmetric Collaborative Deep Learning 
1. Abstract of Cited Paper (BIB001):  Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction. 
2. Abstract of Cited Paper (BIB002):  Collaborative Filtering with Implicit Feedbacks e.g., browsing or clicking records, named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing works of CF-IF can be mainly classified into two categories, i.e., point-wise regression based and pair-wise ranking based, where the latter one relaxes assumption and usually obtains better performance in empirical studies. In real applications, implicit feedback is often very sparse, causing CF-IF based methods to degrade significantly in recommendation performance. In this case, side information e.g., item content is usually introduced and utilized to address the data sparsity problem. Nevertheless, the latent feature representation learned from side information by topic model may not be very effective when the data is too sparse. To address this problem, we propose collaborative deep ranking CDR, a hybrid pair-wise approach with implicit feedback, which leverages deep feature representation of item content into Bayesian framework of pair-wise ranking model in this paper. The experimental analysis on a real-world dataset shows CDR outperforms three state-of-art methods in terms of recall metric under different sparsity level.

Literature Review:
Models like BIB001 , BIB003 focus the deep learning component on modeling the item content. Besides the content information from the items, attributes of users sometimes contain much more important information. It is therefore desirable to extend CDL to model user attributes as well BIB002 . We call this variant symmetric CDL. For example, using an extra mSDAE on the user attributes gives the following joint log-likelihood: where Y 0,j * (a k-by-D matrix for user attributes) is the collection of k different corrupted versions of Y 0,j * and Y c,i * (also a k-by-D matrix) is the k-time repeated version of Y c,i * (the clean user attributes). P 2 is the transformation matrix for user latent factors and D is the number of user attributes. Similar to the marginalized CDL, the solution for W 2 given other parameters is: where , the hinge variables Ω h = {V, U}, and the task variables Ω t = {P 1 , P 2 , R}.