## 3 Results
### Adapting Model Based Approaches
#### 3.1.2 Adapting Matrix Factorization Model  
The use of matrix factorization has been one of the most common approaches for providing both user and item recommendations in recommender systems as well as mitigating cold start issues recommendation in cold start situations. Matrix factorization is a family or class of frequently used techniques in collaborative filtering. Users and items characteristics are transformed by the matrix factorization into latent factor space and predicts the rating of users concerning items by computing the similarity between the user's interest and the target item [36; 37]. The adaptation of matrix factorization collaborative filtering technique has been one of the main approaches used in addressing cold start problems in various domains. Researchers have adapted or modified the matrix factorization to suit various application domains and contexts. Similarly, researchers have used matrix factorization in combination with other approaches or machine learning techniques/algorithms for recommending items to new users. The use of matrix factorization has been a constant theme across for cold start recommendation especially in e-commerce and entertainment (movies and music) domains. Researchers have also provided recommendations for cold start users by leveraging cross domain data. Cross-domain recommendation refers to the use of representations of users and items in one domain to make recommendations in another domain [16]. These representations are considered the auxiliary information for the cold start recommendation. Adapting matrix factorization approaches can be used for cross-domain recommendation in such a way that aggregated information of user preferences from both domains are combined, however matrix factorization approaches have mostly been used in knowledge transfer and linkage approaches [38; 39] for cross domain recommendation by leveraging the relatively richer information such as ratings from the source domain for improving cold start recommendation accuracy in the target domain. Therefore, cold start recommendation accuracy highly depends on the mapping accuracy of the latent factors across the source and target domains. Coupling Matrix Factorization with machine learning algorithms:From Table 2, [40] tackled cold start recommendation through coupling matrix factorization with linear regression and regression tree supervised machine learning algorithms to input the missing values into the rating matrix. The approach combines attribute selection, local learning and value aggregation into a single approach to solve the problem of cold start recommendation. Attribute selection involves selecting a small relevant portion of a data that can be sufficient for predicting the larger data. This reduces complexity by removing irrelevant and redundant attributes from a training data. Local learning involves models built only with the neighboring training data. The attribute value data is used as the auxiliary information to transform pure cold start into warm start, then auxiliary information is obtained using the Euclidean distance formula to complete the missing values of the matrix from warm start to recommendation.