## 3 Results
### Adapting Model Based Approaches
#### 3.1.1 Adapting Traditional Filtering Strategies
This approach utilizes item-detailed descriptions, as opposed to using meta-data such as tags, keywords; this helps in capturing the deep semantics of item descriptions result in richer information about cold start users and consequently more accurate predictions. Coupling Collaborative filtering with other algorithms:Obtaining and eliciting auxiliary information used for cold start user does not only involve coupling collaborative with machine learning algorithms. Researchers have adopted and developed various other algorithms to obtain additional user inferences and ratings for user cold start recommendation. The study of [29] adapted collaborative filtering with random walk-based algorithm modelled as hybrid-walk recommendation approach to infer auxiliary information for cold start recommendation from a different domain--the cross domain. A cross domain recommendation typically involves the transfer of knowledge from a source domain to a target domain (transfer learning to bridge two domains by the set of user nodes). Thus, [29] leverage the use of social ties as the fundamental bridge to connect item domains in social networks that determine behavioral consistency and popularity factors for the cold start recommendation as the implicit link. The study successfully transferred useful knowledge from a social network domain to a target domain which offers recommendation for both data sparsity and pure cold start recommendation. Because it involves more data from heterogenous domain that provide more auxiliary information for cold start recommendation, it provides better and reliable cold start recommendation as it also bridges cross-domain knowledge through social information. One of the main issues with the random walk recommendation algorithm is that it must be tuned considering information about the nature of the application. The study of [31] adapted collaborative filtering using an ontological approach for cold start recommendation. Ontology seeks the classification and explanation of entities. Ontology looks at the things the data are about and uses them as the basis for the structure of the data. This is achieved through the identification of key users within a social network. The implicit auxiliary information involves structuring data of key users of a network in an ontological way. The approach involves identification of users' best entry points into the network to explore its shared resource content. The study is based on an importance measure which combines several aspects characterizing the individuals within the social network. One of the shortcomings of this study is that it was not experimented in real world social network such as Facebook or Twitter. The study of [30] infer recommendation to cold start users by coupling social group-based algorithm with traditional collaborative filtering to produce personalized video recommendations in cold start. The approach is similar to [9; 24] in which users in a social network are grouped into friends based on their similarities, then a new user is inferred or offered recommendation based on the group that the cold start user is best affiliated to. The proposed algorithm was found to not only improves the click-through rate, but more diverse videos are recommended. However, one of the main drawbacks associated with the social group-based algorithm is that the collaborative filtering is not outperformed by the social group-based algorithm in all cases. Generation of results is difficult for highly active users by the social group-based algorithm since many videos in the group video candidate pool have been viewed previously by the active users. In a pure cold start problem, the model leverage factor models in order to boost performance. Thus, it cannot perform well in warm start but only in pure cold start. The study of [32] proposed a collaborative filtering Web service QoS prediction approach which utilizes the knowledge of geographical neighborhoods for effectively mitigating data sparsity and cold-start user recommendation.