## 4 Discussion
Despite the growth in research activities and the need to provide recommendations to users based on the context in which several review and empirical studies have shown its benefits and accuracy in providing better recommendation to users (see [18; 100; 101; 102; 103]), we have only found a few studies that offer cold start context-aware recommendations. These are mostly through mobile apps. Our study has clearly shown the need of using possibly the existing and common approaches of matrix factorization based cold start recommendation or neighborhood recommendation to look into offering better recommendation based on users' contexts in cold start situations. Arguably, implicit auxiliary information can be obtained and elicited contextually to offer rich auxiliary information needed for a cold start recommendation in mobile applications. One of the possible reasons why researchers have not focused on context-aware cold start recommendations might be due to the difficulty and challenges associated with identifying the precise and accurate context of cold start users, so as to utilize the accurate auxiliary information, plus merging or coupling the traditional collaborative filtering or its derivatives-matrix factorization models with this contextual information for context-based cold start recommendations. Another important finding of this study is the use of tag-based recommendations. Although tags are usually associated with certain problems and drawbacks, such as personalization, trust, task categorization (identifying characteristics of tags, its meaning, its origin, etc.) (see [104]), many researchers have leveraged the use of tag information on recommender systems to improve the performance of traditional recommendation techniques for mitigating cold start recommendation. Because tags carry user information and preferences, the use of tags improve the quality and prediction accuracy of a recommender system. Relevant recommendations are provided through the use of tag information with suggesting the tags that are related to the content of a target object [105; 106]. Consequently, once a user or new user's tags or item's keywords are known, it becomes easier to suggest relevant or preferred items to that user using the known tag-keyword relations [107]. Commonly, researchers use matrix factorization matrices using tags to offer cold start recommendation. With tag information, a user-tag matrix that represents users' preferences about tags and convert sparse user-item matrix into dense user-item matrix is constructed. Results shows that the approach generates more precise prediction than general collaborative filtering suffering from the cold start problem. Our results show that the use of tag information have been valuable in providing recommendation to new users. further research is warranted on using tags for user cold start recommendation from domains other than entertainment (music and video) and e-commerce. Another interesting finding of our study involves educational recommender systems (ERS). Over the years, educational and blended learning institutions have intended to improve their learning systems and the whole online environments or components in which students use technologies for their learning process by designing and customizing the learning systems to offer the suitable recommendations to their students. Educational recommender system has received relatively less attention compared to other research domain such as entertainment (music, movies) or e commerce. Recommending learning materials to students have shown to positively impact students learning outcomes (see [108; 109; 110; 111; 112]). The recent study of [113] that recommend learning contents to be adjusted has shown that the future of e-learning heavily relies or is associated with recommender systems in which learning systems would be designed to recommend or provide learners with personalized learning materials based on their contexts, skill, and various other information that defines or identifies a learner.