## 3 Results
### Adapting Model Based Approaches
#### 3.1.2 Adapting Matrix Factorization Model
As such, the two proposed recommendation models from the study-PLSR-Latent and PLSR-CrossRec were able to purely use source-domain ratings in predicting pure cold start users' ratings for users that have never rated any item in the target domains. The basic challenge associated with PLSR-based methods is that it first needs to complete the rating matrices before the recommendation process commences, and as such, may not lead to satisfying performance. _b. Coupling Matrix factorization with non-machine learning algorithms:_  
Basically, researchers leverage the handiness and utility of matrix factorization coupled with machine learning algorithm or other strategies or techniques such as cross domain recommendation for tackling cold start issues in various domains for user cold start recommendation. From Table 2, it can be seen that researchers have used matrix factorization heterogeneously and diversely coupled with diverse set of algorithms or modified in various ways to cope and suit their cold start recommendation pursuits. The study of [49] proposed a framework called iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) and combines the framework together with matrix factorization to offer cold start recommendation by using users' information on online social networks. The framework begins by transforming data on social network into useful information, and then the recommendation core--matrix factorization uses this information as auxiliary information to offer recommendation to cold start users. The approach sounds promising in mitigating both cold start users and item problems. However, the data evaluation is shallow using relatively undersized social network data. In addition, the data was only tested on a single social network stream (Twitter). [48] introduced a novel matrix factorization model in which the cold start users and items form the rating matrix, then are excluded and recover the obtained sub-matrix in a perfect way. Then, the recovered sub-matrix along with available similarity side information about users and items are utilized to transduct the knowledge to cold-start users/items for user cold start recommendation. Transduction is a word commonly used in biological sciences to explain the transfer of a genetic material within similar organisms but via a different type of specie. One of the main problems of this approach is that users can only be offered effective recommendation only when there is sufficient side or complementary information. Its optimization is only in pure cold start situation. The study of [53] presents a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and videos. Emotion modelling deals with estimating the likelihood of emotional response that an item would generate by its users. This estimated response data is the auxiliary information used for cold start user representation. The approach ensures cold start video recommendation with no user prior collaborative information. On the other hand, the visualCLiMF is not particularly effective in a pure cold start situation for recommendation to users. The study of [54] incorporates content-based information and social information to collaborative filtering to build model (i.e., combination of memory based and model based approaches) to build content association between items in selected tag-keywords and tags using user item ratings. The information generated from relational user interest by tag keyword relation matrix is considered as the auxiliary information for cold start user recommendation. The study of [52] used a two-step approach. The first step involves integrating items attribute information into an improved and revised matrix factorization called kernel-based attribute-aware matrix factorization model KAMF. KAMF typically exploits the rich attributes of items and users using nonlinear interactions among attributes.