## 3 Results
### Adapting Model Based Approaches
#### 3.1.2 Adapting Matrix Factorization Model
The approach captures the content-based transition preference by mining both sequential behavior and content feature simultaneously. These extracted data is used as the auxiliary information for cold start recommendation. Due to the difficulty of obtaining the correct side information for cold start recommendation, recommender systems researchers have used arguably a hybrid of machine learning approaches by combining unsupervised and semi-supervised machine learning algorithms in different steps to arrive at accurate recommendation for user cold start. The study of [42] adopted the FPMC framework which combines matrix factorization and Markov's chain. The approach involves the use of node2 vec semi-supervised algorithm to pre-train the representation of users. Then inferences of network neighbors with high similarities are considered as the auxiliary information which are selected and integrated into the user cold start recommendation. The recommendation of this approach is relatively weak in dealing with cold start users having fewer social links. The study of [47] proposed a deep framework for both cross-domain and cross-system recommendations which is based on coupling deep neural network with matrix factorization models. The process of obtaining and transferring knowledge from the source domain to the target domain typically in a cross-domain recommendation involves three processes. First the user and item latent factor matrices is obtained using matrix factorization. The second phase, the benchmark factor matrices are generated by combining the latent factor matrices according to the sparsity degrees of individual users and items, then the latent factor matrices are mapped to fit the benchmark factor matrices. Based on the affine factor matrices learned from the second prediction phase, the users' ratings on all items in the target domain or system and recommend which matched items to target users is predicted. Similarly, The study of [45] used a modified or adapted matrix factorization-clustering based matrix factorization model to construct a rating matrix that includes available ratings on both of the domains for a cross domain recommendation, after which a k-means algorithm is used to categorize users and items after mapping the matrix into a lower-dimensional latent space. The matrix factorization models generate user and item latent factors and then employs the deep neural network to map the latent factors across domains or systems for cold start recommendation. [44] used linked users between two sites (e-commerce sites and social networking sites) who have made purchases on e-commerce websites and has social networking sites to map and elicit user preferences for cold start recommendation. The approach involves two distinct processes (two-step process). The first step involves learning both product and user embeddings from e-commerce websites data using machine learning algorithms-gradient boosting trees (supervised) and a recurrent neural network (supervised) for transforming users' social networking features into user embeddings. The second phase involve adapting matrix factorization to a feature-based matrix factorization which these user embeddings for cold start product recommendation. Typically, matrix factorization is adapted or coupled with additional algorithm to predict cold start user preferences, however, there are instances in which a particular technique or algorithm is adapted or extended so as to be able to couple or integrate with matrix factorization. In this case, matrix factorization is the secondary method or mechanism to trigger cold start recommendation. A typical example can be found from the study of [46] that adapted partial least squares regression (PLSR) machine learning technique to enable coupling with matrix factorization for cross domain cold start recommendation. Although, PLSR can be applied to matrices without missing values, the rational of adapting PLSR with matrix factorization is to train the data that reflect user preferences based on records.