## 3 Results
### Adapting Model Based Approaches
#### 3.1.1 Adapting Traditional Filtering Strategies  
Collaborative filtering is a well-developed framework that utilizes the history of different users to provide personalized recommendation. Over the years, collaborative filtering model has been extensively studied and revised, and researchers have proposed various enhancements and modification of the model to suit various recommendation needs in varying application domains [19; 20]. The main drawback of collaborative filtering is the cold-start problem in providing accurate recommendation to new users because traditional filtering strategies/techniques-collaborative filtering and content-based filtering have limited scope in the cold start scenario due to lack of descriptive information about the new user/item. One of the ways to use these traditional filtering strategies for cold start scenario is to modify or adapt them to work on sparse data and pure cold start situations. This adaptation usually involves coupling them with algorithms and other strategies to work on cold start recommendation. Collaborative filtering algorithms are combined with classification algorithms such as C4.5 and naive Bayes algorithm, for finding a neighborhood for a new user [9]. The use of collaborative filtering for cold start recommendation has been widely adopted in recent decades, for example, the study of [21] built error reflected model and applying it to collaborative filtering recommendation for cold start recommendation using user neighborhood and user similarity matrix. The study of [22] proposed unique approach which is a weighted linear combination of simpler similarity measures that is optimized using neural network machine learning algorithm. Coupling collaborative filtering with machine learning algorithms:  
Mitigating the cold start problem ordinarily consider traditional factorization algorithms as the baseline model or the initial step in acquiring the additional auxiliary information to build the cold start recommendation. This is because traditional factorization algorithms usually capture the fine-grained link or relationships between users or items, then these links or models are coupled with various algorithms or approaches-machine learning algorithms being the most popular, to find the neighbor of the items, users or links in order to extract the implicit auxiliary information from the same network/domain or from an entirely different domain for cold start recommendation. From Table 1, coupling of collaborative filtering with other approaches to infer auxiliary or side information which is used for cold start user recommendation can be categorized into two main sub-categories. One of the most effective approaches for obtaining and eliciting auxiliary information in recommender systems for cold start recommendation is to couple collaborative filtering with machine learning algorithms. Researchers have used various supervised, semi-supervised and unsupervised machine learning approaches coupled together with traditional collaborative filtering to obtain relatively reliable new user inference or preferences which is used for the cold start recommendation. From Table 1, the study of [9] involves modelling collaborative filtering algorithms with C4.5 and Naive Bayes classification algorithms to find a neighborhood for a new user. First, the system trains the classification model with the new users' demographic information, which then classifies the new users to demographics-based groups. The process involves adopting a mechanism that considers users' demographic data, based on the similarity techniques, the system then finds the users' auxiliary information for identifying their neighbors preferences which leads to cold start recommendation. Neighborhood recommendation was built based on the idea that users having similar characteristics and common backgrounds are more likely to have similar preferences. Hence, each cold start user is categorized to a group with members having