## 3 Results
### Adapting Model Based Approaches
#### 3.1.2 Adapting Matrix Factorization Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**Articles**} & \multirow{2}{*}{**Platform, Data and Domain**} & \multirow{2}{*}{**Summary of Approaches**} \\  & & & **Domain** \\ \hline \multicolumn{4}{c}{_Coupling matrix factorization with machine learning algorithms_} \\ \hline
1 & [40] & Not stated & Matrix factorization coupled with regression tree and linear regression supervised learning models \\ \hline
2 & [41] & Movielens & Matrix factorization with Markov chain \\  & & & (Markov random field prior (mrf-MF)) \\ \hline
3 & [42] & Social media & Matrix factorization with Markov chain \\ \hline
4 & [43] & Music & Merging Markov chain with matrix factorization model \\ \hline
5 & [44] & Siena weibo (e-commerce) & Cross site (cross domain). Matrix factorization with modified gradient boosting trees and recurrent neural networks \\ \hline
6 & [45] & Not stated & Cluster-based matrix factorization model with K means clustering algorithm for cold start cross domain recommendation \\ \hline
7 & [46] & Videos \& DVDs, Books, and Music & Cross-domain recommendation mechanism based partial least squares regression analysis. PLSR coupled with matrix factorization \\ \hline
8 & [47] & Not stated & Cross-domain and cross-system recommendations coupling deep neural network with matrix factorization models \\ \hline \multicolumn{4}{c}{_Coupling matrix factorization with other approaches_} \\ \hline
1 & [12] & DBpedia & Adapting matrix factorization with open linked data (MF-LOD) \\ \hline
2 & [48] & Not Stated & algorithmic framework based on matrix factorization \\ \hline
3 & [49] & Social media (Twitter) & ISoNTRE—is a framework called intelligent Social Network Transformer into Recommendation Engine is combined with matrix factorization \\ \hline
4 & [50] & Douban movie iMDB (Movies) &
\begin{tabular}{c} _CHRS_ uses a two-step approach to integrate the complementary in both the target and source networks for cold start recommendation utilizing the rich information from these two paths \\ \end{tabular} \\ \hline
5 & [51] & Social media platforms platforms (e-commerce) & Cross-domain cold start recommendation based on mapping user feature based matrix factorization from social media and product features from e-commerce website \\ \hline
6 & [52] & Yelp; movielens (Movies) & Developed KAMF that use social links between users for cold start recommendation \\ \hline
7 & [53]. & Movielens (Movies) & Matrix factorization with a content \\ \hline
8 & [54] & \
\begin{tabular}{c} Tancient Weibo \\ (Movie) \\ \end{tabular} &
\begin{tabular}{c} Neighborhood and matrix factorization \\ (hybrid—memory based and model based) \\ \end{tabular} \\ \hline
9 & [55] & \
\begin{tabular}{c} Tencent Weibo \\ (microblogs) \\ \end{tabular} &
\begin{tabular}{c} Adapted matrix factorization adapted by coupling social behavior and social trust data \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adapting matrix factorization model. [PERSON]'s chain has been used coupled with matrix factorization to infer and accurately predict new users' preferences. The study of [41] enhanced matrix factorization prediction capability by integrating an n-dimensional Markov random field prior (mrf-MF) to cope with three types of cold-start problem: recommending new users to existing items; recommending new items to existing users; recommend new items to new users. First, a specific neighbors system for user attributes such as age, occupation of users and genre, release year of items is defined followed by conditional distribution of latent profiles. Based on the k-nearest neighbors system, the conditional distribution of latent profiles of users/items in n-dimensional Markov random field is defined. The proposed model could not deal with sparse data or warm cold scenario where very sparse training data is available. Similarly, the study of [43] mitigate the cold start problem for next-song recommendation by merging Markov chain with matrix factorization model.