## 3 Results
### Adapting Model Based Approaches
#### 3.1.2 Adapting Matrix Factorization Model
This rich attributes of items and users is the auxiliary information but, in this case, it only provides the baseline for the recommendation process for the second step. The second step involvesevaluating the model parameters of KAMF using an extension of KAMF incremental algorithm to which is further adapted for addressing the user cold-start problem using social links data between users. Although KAMF can deal with cold-start problem by nature, however KAMF's main weakness is that it focuses on predicting item rating first, then using those rating to recommend cold start users to these items. The study of [55] used social trust and social behavior data in microblogs to adapt matrix factorization using an algorithm called trust and behavior-based singular value decomposition (TBSVD) for eliciting user preferences. First, implicit trust is calculated based on user interaction behavior including comment, mention and retweet, while explicit trust is based on the direct connections between users. This implicit and explicit trust information are used as auxiliary information needed for cold start recommendation. Then, an extended trust matrix is constructed combining both implicit trust and explicit trust which is then used to build the model using matrix factorization technique. The technique was only evaluated in microblogs. Leveraging linked open data has been a common approach for building recommendation capabilities in recommender systems due to the recent advancement in semantic web technology which has enabled data to be represented strategically for improving recommender systems. This advancement enables systems to significantly and better understand user and item preferences and features based on their contexts and domains. The increase in publication of linked open data has enabled to link user and item entities from varying knowledge sources by connecting different homogenous data or information in a single global data space. Over the years, linked open data has been used for both cold start [56] and non-cold start [57; 58] recommendation. The study of [12] from Table 2 leverage implicit feedback data and linked open data based on similarity measure for user cold start recommendation. Matrix factorization is coupled with linked open data (MF-LOD) considered as the auxiliary information to enhance the matrix factorization model called singular value decomposition (SVD++) for cold start recommendation. The study of [51] proposed microblogging reviews based on cross lingual sentimental classification model for mitigating cross-site cold start product recommendation. An adapted matrix factorization called feature-based matrix factorization method is used in the mapping of user features from social media and product features from e-commerce website. Then sentimental analysis is applied on two languages reviews (English-Hindi) as well as the extracted texts from audio-video reviews. One of the main drawbacks of the study is that the approach is not fully evaluated. More experiments needed to validate the approach. However, researchers have also provided cross recommendations from within the same domain, in which two different social network-source and target networks are mapped to offer recommendations. The study of [50] adopts a across heterogeneous network approach (CHRS) which uses a two-step approach to integrate the auxiliary information in both of the source and target networks from within the same network for user cold start recommendation. The approach utilizes matrix factorization to incorporate item similarity information in the source network. This approach is somewhat similar to cross domain approaches, but happens within the same network. The approach leverages the rich information from meta-paths for calculating the movie similarities from multiple types of relation information. Using a unidirectional way, the movie similarities from these rich meta-paths is the auxiliary information.