## 3 Results
### Others  
This section explains other unfamiliar approaches and techniques used by researchers to offer or provide recommendation for cold start users. From Table 5, we have found different sorts of studies that employ varying approaches for mitigating user cold start recommendation. One of the eye-catching studies is the study of [91] that proposed a hybrid model by combining the strength of item response theory (IRT) models with machine learning (classification and regression trees) for predicting students skills or ability to address the cold start problem for learners in their learning systems in online learning environments. The study demonstrates the application of machine learning in combination with item response theory to alleviate the effect of cold start in adaptive learning environments. Although fully implicit data are not used (partial implicit data), we intend to articulate and highlight this study due to lack of cold start recommendation approaches in learning and educational environments. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**Articles**} & \multirow{2}{*}{\
\begin{tabular}{c} **Platform, Data and** \\ **Domain** \\ \end{tabular} } & \multirow{2}{*}{**Summary of Approaches**} \\ \cline{3-3} \cline{5-5}  & & & \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{[30]} & \multirow{2}{*}{
\begin{tabular}{c} Tancient weibo \\ (video) \\ \end{tabular} } & \multirow{2}{*}{Social-group-based algorithm for video recommendation} \\ \cline{3-3} \cline{5-5}  & & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{[59]} & \multirow{2}{*}{Music} & Inter domain cross recommendation of \\  & & & using representation of music taste and past \\ \cline{3-3} \cline{5-5}  & & & music listening for user cold start \\ \cline{3-3} \cline{5-5}  & & & recommendation \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{[91]} & \multirow{2}{*}{ERS} & Cold start recommendation in online learning adaptive systems using machine learning (classification and regression trees) \\ \cline{3-3} \cline{5-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Others. Another interesting approach involves the use of probabilistic approach (see [92; 93]) coupled with deep learning approach-neural network. A probabilistic model based on uncertainty rules to allow new users to informally infer their own recommendations. Such uncertain rules involve for example \"if a user likes item A, this user will probably like item B\". As such, cold start users are offered recommendation using inferred probabilistic information as the auxiliary information. One of the typical shortcomings with probabilistic recommendation is that the models built are not as accurate as the successful matrix factorization models when dealing with ordinary registered users, and as such, their cold start recommendation are not deemed accurate and reliable especially in pure cold start cases. A similar approach from the study of [94] alleviates cold start recommendation by hybridizing rule mining and community-based knowledge. For rule mining, association rules are frequently used in providing useful recommendations. In this study, the top rules are ranked based on the highest score using the Apriori algorithm and FP Growth for cold start user recommendation. As such, the topmost popular rules are the auxiliary information used for cold start recommendation. It is clear that the recommendation does not involve integrating or the use of the total or complete implicit auxiliary information elicited, researchers usually rank the auxiliary data in accordance with their reliability in providing the accurate or 'closer to the truth' recommendation to cold start user. This is true from the study of [30] in Table 5 that used a social group-based algorithm for video recommendation based on social group information. Candidate videos from a single group and multiple groups which at a user is affiliated to could be ranked by the method.