## 3 Results
### Adapting Model Based Approaches
#### 3.1.1 Adapting Traditional Filtering Strategies
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**Articles**} & **Platform, Data and** **Domain** & **Summary of Approaches** \\ \hline \multicolumn{4}{c}{_Coupling collaborative filtering with machine learning algorithms_} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{[9]} & \multirow{2}{*}{Movielens (Movies)} & Collaborative filtering algorithms combined with naive bayes and C4.5 classification algorithms \\ \cline{2-2}  & & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{[23]} & \multirow{2}{*}{Movielens (Movies)} & Model based collaborative filtering by a context aware to elicit auxiliary data \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{[24]} & Netflix, jetstar, & User demographic information together with collaborative filtering to address cold start problem using a SCOAL (Simultaneous co-clustering and learning) algorithm \\ \cline{2-2}  & & & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{[25]} & Not stated & Adapting collaborative filtering using explicit trusted user neighbor data \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{[26]} & \multirow{2}{*}{Netflix (Movies)} & Collaborative filtering merged with machine learning algorithms-deep learning algorithm (deep neural network) SADE, timeSVD++ \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{[27]} & \multirow{2}{*}{Opinions} & Collaborative filtering merged with K-nearest neighbors \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{[28]} & Collaborative filtering with word2 vec unsupervised machine learning algorithm \\ \hline \multicolumn{4}{c}{_Coupling collaborative filtering with other approaches_} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{[29]} & [PERSON] (Video) & Collaborative filtering merged with a hybrid random walk (HRW) method \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{[30]} & [PERSON] (Video) & Collaborative filtering merged with a social group-based algorithm \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{[31]} & \multirow{2}{*}{Ontological approach} & Collaborative filtering adapted with an ontological approach \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{[32]} & WSDream (Web services) & A collaborative filtering web service QoS prediction approach that utilizes geographical neighborhoods knowledge \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adapting traditional filtering strategies. similar characteristics, and accordingly the ratings from a cold start user group category is used for inferring the new users cold start recommendation. One of the main strengths of the study in relation to handling cold start situation is that it mitigates the new user cold start problem effectively. The study also involves semantic similarity metrics in the calculation process and does not depend on any complex calculations. Similarly, the study of [24] leverage the use of user demographic information together with collaborative filtering to curl new user preferences implicitly so as to provide suitable recommendation using an algorithm called SCOAL (simultaneous co-clustering and learning). First, users with common interest are identified and grouped into sub-groups, then prediction models are built simultaneously specifically for each sub-group. The second step involves identifying which sub-group a new cold start user fits or should be allocated to base on the prediction models generated by the SCOAL. In this case, the new user is offered recommendation based on the data of the new user allocated group. Researchers in recommender system domain have also devised the use of multiple models approach to extract implicit auxiliary information for solving the problem of cold start recommendation, since it is challenging to find useful information from a single source. Integration is usually performed by allowing the models to train each other in a \"semi-supervised manner\", using an implemented \"co-training algorithm\".