## 3 Results
### Adapting Model Based Approaches
#### 3.1.1 Adapting Traditional Filtering Strategies
The study of [23] adopted a state-of-the-art model-based collaborative filtering algorithm by a context-aware approach to incorporate auxiliary information about both the users and items in the recommendation system. Based on the context modelling data, multiple supervised learning models are constructed to complement and train each other for offering a new user suitable recommendation in recommender system. These models train each other and new user information could be generated from the model training and used for cold start recommendation. This significantly alleviates the cold start problem. Over the past two decades, deep learning has received huge attention in various research domains [33; 34; 35]. Deep learning approaches have been applied in various capacities due to its capability in solving many complex tasks while providing start-of-the-art results. The two arguably most popular deep learning architectures are convolutional neural network CNN, which is a technique commonly used for image processing applications, and recurrent neural network RNN are mostly used in recommender systems, text and speech applications are the most popular deep learning methods currently in used. In [26], collaborative filtering is merged with deep learning algorithm (deep neural network) in forming a cold start recommendation. Deep learning machine learning approach are used for obtaining content features from content descriptions. Here, the content features are used as key components of the recommendation models which implicates the prediction of the unknown rating and the training models for cold start recommendation. The use of traditional collaborative filtering with trusted neighbors have been effective in resolving data sparsity and cold start problems which the traditional collaborative filtering suffer [25]. Specifically, from the study of [25], the implicit data for cold start recommendation was generated through merging trusted neighbors to complement and represent active users preferences based on the similar users that can be identified. The approach involves the use of explicit user's social trust information to specify other users as trusted neighbors. The approach involves merging trusted neighbors' ratings of an active user according to the extent to which the trusted neighbors are similar to the active user. The set of merged ratings is then used to represent the active user's preferences as auxiliary information used to adapt the traditional collaborative filtering for cold start recommendation. One of the main strengths of the study was effectively complementing user rating profiles based on the ratings of trusted neighbors-an effective trust-aware recommender system. Although the recommendation generated is reliable, it only covers the smallest portion of items, since only the ratings of the users who have a large number of trusted neighbors and high rating correlations are possible to be predicted. Therefore, for better recommendation, users have to provide explicit trust information for the merging process which users may not be willing to reveal. Adapting traditional filtering strategies can also be applicable for cold start item recommendation using unsupervised machine learning approaches such as the study of [28] that proposed a content embedding based hybrid recommender model which involve the combination and adaptation of traditional collaborative filtering together with machine learning algorithms for item cold start recommendation. The study proposed HRSC framework using Word2 Vec for obtaining description of items using content embedding methods. Word2 vec is an unsupervised methodology for building word embeddings. It uses a neural network model to learn word associations from a large corpus of text. The word2 vec algorithm once trained can detect synonymous words or suggest additional words for a partial sentence. These suggestions and predictions are used as the auxiliary information to offer recommendation to cold start users.