## 3 Results
### Adapting Memory Based Approaches  
Memory-based algorithms also referred to as neighbourhood-based collaborative filtering algorithms were among the oldest approaches developed for collaborative filtering [61]. The neighbourhood-based approaches are generally based on the k-nearest neighbour rule (KNN), and provide recommendations by aggregating the opinions of a user's k-nearest neighbours [62]. Two stages are involved in neighbourhood-based methods: the neighbour selection and the rating prediction. In cold start scenario, researchers have modified and adapted k-nearest neighbour by coupling them with various algorithms to obtain auxiliary information needed for cold start recommendation. Similarly, researchers have leveraged the use of tags to establish neighbourhood link for cold start recommendation. Tags-based elicitation approach:Tags are usually associated with supplementing and offering valuable information that are used in recommendation. This is tags summarize item properties as well as describing user preferences through tagging behaviors. From Table 3, researchers have leveraged the use of tags in recommender systems in identifying neighbhonhoods of a cold start user. Tags carry vital information and defines the behavior or preferences of the tag recommendation or user. Although the use of tags has been popular in recommender systems, tags have also been used in eliciting implicit information for user cold start recommendation. For example, the study of [64] proposed a syntactic and neighborhood-based attributes by using syntactic patterns of the text associated with web objects which can be exploited to identify and recommend tags to new users in a cold start scenario. However, this approach was not tested in pure cold start recommendation. Similarly, the study of [65] used k \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S/N** & **Articles** & **Platform, Data and Domain** & **Summary of Approaches** \\ \hline \multirow{2}{*}{1} & [63] & \multirow{2}{*}{Facebook (movies)} & Proposed a similarity metric for grouping users of the same interest for improving cold start movie recommendation through the use of KNN and K Means algorithms \\ \hline \multirow{2}{*}{2} & [64] & \multirow{2}{*}{Movielens, Elo7 (Movies)} & Tag recommendation methods in a cold start scenario through syntactic and neighbourhood-based attributes. \\ \hline \multirow{3}{*}{3} & [65] & \multirow{2}{*}{Youtube (videos)} & Video recommendation based on visual tags; visual description of the videos is used for the automatic annotation of the tags using K-Nearest Neighbor algorithm. \\ \hline \multirow{2}{*}{4} & [66] & \multirow{2}{*}{Mobile app} & Predicting the next app to be used which involve a technique based on a set of features representing the real-time spatiotemporal contexts sensed by the home screen app for user cold start \\ \hline \multirow{2}{*}{5} & [67] & \multirow{2}{*}{Mobile app} & Provides cross domain cold start recommendation for mobile apps from a mobile application domain to a news domain. \\ \hline \multirow{2}{*}{6} & [68] & \multirow{2}{*}{Mobile app} & A modified incremental k-nearest neighbors (IkNN) and K-clustering algorithm for predicting next app for cold start \\ \hline \multirow{2}{*}{7} & [69] & \multirow{2}{*}{Mobile app} & Location features and user interests are transferred from app usage data for cold-start location recommendation, \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Adapting memory-based approaches. nearest neighbor algorithm for cold start recommendation. The process involves eliciting implicit data with the help of tags for user cold start recommendation using visual tags that are automatically annotated to videos based on visual description of the videos. Such features are used as the auxiliary information, automatically extracted and added to the video items cold start recommendation. Research in recommender system has shown the reliability and effectiveness of building models capable of learning the correlation between video tags and visual features.